From 03b965f0e22462c11245e16bc6464b7f865e4496 Mon Sep 17 00:00:00 2001
From: huaiyukeji <huaiyukeji@gmail.com>
Date: Sun, 17 Dec 2023 22:15:21 +0800
Subject: [PATCH 1/3] init

---
 .gitignore                                    |    4 +
 IMG_1766.JPG                                  |  Bin 0 -> 219722 bytes
 LICENSE                                       |   21 +
 README.md                                     |   96 +
 .../jiyan_gt_challenge_demo-checkpoint.ipynb  |  708 ++
 ...\346\250\241\345\236\213-checkpoint.ipynb" |  225 +
 ...knet yolo-v3|cuda|cudnn|opencv|anaconda.md |  350 +
 doc/classifier_train_handbook.md              |  108 +
 doc/detector_train_handbook.md                |  177 +
 doc/jiyan_gt_challenge.md                     |   45 +
 doc/jiyan_gt_challenge_demo.ipynb             |  711 ++
 ...01\350\256\255\347\273\203\351\233\206.md" |   98 +
 ...347\224\250\346\250\241\345\236\213.ipynb" |  225 +
 hanzi_detection/.gitignore                    |   27 +
 hanzi_detection/LICENSE                       |   12 +
 hanzi_detection/LICENSE.fuck                  |   13 +
 hanzi_detection/LICENSE.gen                   |   91 +
 hanzi_detection/LICENSE.gpl                   |  674 ++
 hanzi_detection/LICENSE.meta                  |    8 +
 hanzi_detection/LICENSE.mit                   |   22 +
 hanzi_detection/LICENSE.v1                    |   13 +
 hanzi_detection/Makefile                      |  105 +
 hanzi_detection/README.md                     |   75 +
 hanzi_detection/examples/art.c                |   59 +
 hanzi_detection/examples/attention.c          |  459 +
 hanzi_detection/examples/captcha.c            |  353 +
 hanzi_detection/examples/cifar.c              |  251 +
 hanzi_detection/examples/classifier.c         | 1098 +++
 hanzi_detection/examples/coco.c               |  357 +
 hanzi_detection/examples/darknet.c            |  503 ++
 .../examples/detector-scipy-opencv.py         |   56 +
 hanzi_detection/examples/detector.c           |  850 ++
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 hanzi_detection/examples/dice.c               |  116 +
 hanzi_detection/examples/go.c                 | 1370 +++
 hanzi_detection/examples/instance-segmenter.c |  267 +
 hanzi_detection/examples/lsd.c                | 1378 +++
 hanzi_detection/examples/nightmare.c          |  414 +
 hanzi_detection/examples/regressor.c          |  240 +
 hanzi_detection/examples/rnn.c                |  542 ++
 hanzi_detection/examples/rnn_vid.c            |  208 +
 hanzi_detection/examples/segmenter.c          |  255 +
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 hanzi_detection/include/darknet.h             |  805 ++
 hanzi_detection/libdarknet.so                 |  Bin 0 -> 538900 bytes
 hanzi_detection/model/jiyan_classify.cfg      |   83 +
 hanzi_detection/model/jiyan_yolov3.cfg        |  789 ++
 hanzi_detection/python/darknet.py             |  156 +
 hanzi_detection/python/proverbot.py           |   37 +
 hanzi_detection/readme_classify.md            |   20 +
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 hanzi_detection/src/blas_kernels.cu           | 1035 +++
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 hanzi_detection/src/convolutional_layer.c     |  622 ++
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 hanzi_detection/src/image.c                   | 1466 ++++
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 hanzi_detection/src/logistic_layer.h          |   15 +
 hanzi_detection/src/lstm_layer.c              |  626 ++
 hanzi_detection/src/lstm_layer.h              |   20 +
 hanzi_detection/src/matrix.c                  |  196 +
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 hanzi_detection/src/parser.c                  | 1312 +++
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 hanzi_detection/src/region_layer.c            |  507 ++
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 hanzi_detection/src/softmax_layer.c           |  107 +
 hanzi_detection/src/softmax_layer.h           |   19 +
 hanzi_detection/src/stb_image.h               | 7462 +++++++++++++++++
 hanzi_detection/src/stb_image_write.h         | 1568 ++++
 hanzi_detection/src/tree.c                    |  139 +
 hanzi_detection/src/tree.h                    |    8 +
 hanzi_detection/src/upsample_layer.c          |  106 +
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 hanzi_detection/src/utils.c                   |  726 ++
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HcmV?d00001

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..0c8c914
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 hunter 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..fb5146d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,96 @@
+# verification_code
+验证码研究破解心得记录。包含网易易盾，阿里云验证码，极验验证码，梦幻西游验证码，汉字验证码通用识别等主流验证码模型。已更新极验验证码、点选验证码、语序验证码、企业公示网/工商/文书采集系统（代码demo）。
+
+
+
+:boom:请保证本项目仅用于研究学习，谢谢:boom:
+
+
+
+![](https://img.shields.io/badge/Python-%3E%3D3.6-orange?style=social) ![](https://img.shields.io/github/license/huaiyukeji/verification_code) 
+
+
+
+## 项目解决了哪些问题？
+
+<p align="center">
+	<img src="./media/e697bc6aa912eeb2610998833f209242.jpg" alt="geet" width="172" height="192">
+	<img src="./media/geet_demo.jpg" alt="geet" width="172" height="192">
+	<img src="./media/yidun_demo.jpg" alt="yidun" width="240" height="120">
+	<img src="./media/0b7bbb3595309f7f9123704ef354a52d旅_u65c5.jpg" alt="geet">
+	<img src="./media/1590951689.png" alt="menghuan" >
+	<img src="./media/阿_1cde28c6abce11eab97c0242ac110002.jpg" alt="menghuan">
+</p>
+
+
+
+---
+
+### 本项目供大家一起学习交流，群里大佬们会免费一起训练模型
+
+---
+
+
+
+## 待更新内容
+
+如果对您有用，请给个star:star::star::star:谢谢。点赞可让我看到大家的关注情况，持续更新的动力，谢谢支持。
+
+刚建了个群，随缘加加！不能分享的代码在群里交流！群号：:rocket:`1040784971`:rocket:
+
+许多内容待整理。
+
+:star: **~~更新极验打码测试接口~~**:star:：
+
+~~垃圾服务器，图片识别速度很慢，随便用用，随时会挂掉，有疑问请留言~~
+
+~~可用于学习性质的数据标注和测试，严禁用于商业项目，谢谢配合~~
+
+~~(接口已关闭，谢谢关注)~~
+
+### 极验的JS破解流程以及点选验证码识别模型
+
+极验分为三个部分，1. 破解了前端，获取validate参数；2. 文字类的验证码；3. 滑动验证码。验证码部分结合机器学习在本地打码。
+
+- [极验验证码前端JS破解思路](./doc/jiyan_gt_challenge.md)
+
+	极验js方面，获取validate就完结了。
+
+  - [x] gt、challenge
+  - [ ] validate
+  - [x] [获取gt、challenge、validate，代码DEMO](./doc/jiyan_gt_challenge_demo.ipynb)
+  
+- 点选验证码汉字识别模型
+
+  这部分极验和易盾是通用的，数据集改改就好了。如果有数据集可以和我共享，我收集完统一标注然后共享出来。数据集完备后更新一波`一步识别`的模型出来。
+
+  - [x] [Ubuntu环境安装](./doc/Ubuntu18.04%20install%20darknet%20yolo-v3%7Ccuda%7Ccudnn%7Copencv%7Canaconda.md)
+  	- NVIDIA-SMI|CUDA|cuDNN|OpenCV|Anacoda
+  - [x] [汉字定位（目标检测）](./hanzi_detection/README.md)
+  - [x] [汉字识别](./hanzi_detection/readme_classify.md)
+  - [x] [定位模型训练手册（含数据）](./doc/detector_train_handbook.md)
+  - [x] [识别模型训练手册（含数据）](./doc/classifier_train_handbook.md)
+  - [x] [Python中使用模型](./doc/在Python中使用模型.ipynb)
+  - [ ] 模型速度优化
+  
+- 九宫格类型验证码
+
+  - [x] [数据准备](./doc/九宫格图片验证码训练集.md)
+  - [ ] 分类模型训练手册
+
+- 滑动3.0和机器学习
+
+  - [ ] 滑动轨迹
+
+### 瑞数JS
+
+
+
+
+## 联系方式
+欢迎技术交流：`huaiyukeji@gmail.com`
+
+
+
+TG电报群：https://t.me/joinchat/99xuS4HiOIc2YTU1(老群已解散)
+
diff --git a/doc/.ipynb_checkpoints/jiyan_gt_challenge_demo-checkpoint.ipynb b/doc/.ipynb_checkpoints/jiyan_gt_challenge_demo-checkpoint.ipynb
new file mode 100644
index 0000000..a95d7a4
--- /dev/null
+++ b/doc/.ipynb_checkpoints/jiyan_gt_challenge_demo-checkpoint.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用企业公示网做例子\n",
+    "\n",
+    "获取gt和challenge两个参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 企业公示网做例子\n",
+    "index_url = 'http://www.gsxt.gov.cn/index.html'\n",
+    "index_header= {\n",
+    "            'accept': \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3\",\n",
+    "            'accept-encoding': \"gzip, deflate\",\n",
+    "            'accept-language': \"zh-CN,zh;q=0.9\",\n",
+    "            'connection': \"keep-alive\",\n",
+    "            'host': \"www.gsxt.gov.cn\",\n",
+    "            'upgrade-insecure-requests': \"1\",\n",
+    "            'Referer': 'http://www.gsxt.gov.cn/index.html',\n",
+    "            \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36\",\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 代理手动配置，避免本地的IP被封了\n",
+    "proxy = {\n",
+    "        \"https\": \"https://\" + \"60.188.9.176:3000\",\n",
+    "        # \"http\": \"http://\" + \"60.167.135.237:8118\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(index_url, headers=index_header, proxies=proxy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取到第一段JS代码"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<script>var x=\"firstChild@setTimeout@toString@0xEDB88320@createElement@06@String@if@3@Wed@new@toLowerCase@@e@@search@@@replace@G@@length@Expires@@try@rOm9XFMtA3QKV7nYsPGT4lifyWwkq5vcjH2IdxUoCbhERLaz81DNB6@JgSe0upZ@@@innerHTML@@pathname@RegExp@@challenge@onreadystatechange@match@@@@2@F@fromCharCode@reverse@a@1589364371@@cookie@attachEvent@@captcha@function@eval@while@@return@chars@var@href@else@@@@f@location@Array@https@charCodeAt@@D@substr@1@@1500@DOMContentLoaded@false@13@@@Path@@@@@addEventListener@parseInt@split@S@document@g@@36@@@8@0xFF@@d@WO@@20@@@@May@0@@@@@join@11@for@Hkav@@charAt@@@@GMT@__jsl_clearance@438@catch@@@window@div@\".replace(/@*$/,\"\").split(\"@\"),y=\"1q 1v=1k(){2(\\'21.1r=21.10+21.g.j(/[\\\\\\\\?|&]1j-13/,\\\\\\\\\\'\\\\\\\\\\')\\',2a);2p.1g=\\'3p=1e.3q|3a|\\'+(1k(){1q 1t=[1k(1v){1o 1v},1k(1t){1o 1t},1k(1v){1o 1l(\\'7.1b(\\'+1v+\\')\\')},1k(1v){3h(1q 1t=3a;1t<1v.m;1t++){1v[1t]=2m(1v[1t]).3(2s)};1o 1v.3f(\\'\\')}],1v=[[[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]+[-~-~~~\\'\\'+((-~{}<<-~{})^-~~~\\'\\')]],\\'2o\\',[[((-~{}<<-~{})^-~~~\\'\\')-~~~\\'\\'-~!{}+((-~{}<<-~{})^-~~~\\'\\')]+[-~[-~-~~~\\'\\'-~-~~~\\'\\'-~-~~~\\'\\']]],\\'32\\',[[((-~{}<<-~{})^-~~~\\'\\')-~~~\\'\\'-~!{}+((-~{}<<-~{})^-~~~\\'\\')]+(-~[]-~[]+[[]][3a])],\\'1a\\',[[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]+[-~-~~~\\'\\'+((-~{}<<-~{})^-~~~\\'\\')]],[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~\\'\\'))],\\'3i\\',[[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]+[~~[]]],\\'k\\',[((-~{}<<-~{})^-~~~\\'\\')-~~~\\'\\'-~!{}+((-~{}<<-~{})^-~~~\\'\\')],\\'26\\',[[-~{}]+[-~[-~-~~~\\'\\'-~-~~~\\'\\'-~-~~~\\'\\']]],[[-~[-~-~~~\\'\\'-~-~~~\\'\\'-~-~~~\\'\\']]+[((-~{}<<-~{})<<-~{})]],[(-~[]-~[]+[[]][3a])+[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')],[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]+[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]],[[-~[-~-~~~\\'\\'-~-~~~\\'\\'-~-~~~\\'\\']]+(-~[]-~[]+[[]][3a])],[(-~[]-~[]+[[]][3a])+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][3a])],\\'33\\',[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~\\'\\'))]+[-~-~~~\\'\\'+((-~{}<<-~{})^-~~~\\'\\')]],[[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')]+[((-~{}<<-~{})<<-~{})]],[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~\\'\\'))]+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][3a])],\\'%\\',[(-~~~\\'\\'+[-~-~~~\\'\\']>>-~-~~~\\'\\')],\\'26\\'];3h(1q 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x[f(y,z)-1]||(\"_\"+y)}));break}catch(_){}</script>\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x0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+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.text    # 获取第一段js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取jsluid参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3873563fb2bec146ee50d1146cda3483'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jsluid = response.headers['Set-Cookie'].split('=')[1].split(';')[0]    \n",
+    "jsluid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "解密出第二段js代码"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"var _1v=function(){setTimeout('location.href=location.pathname+location.search.replace(/[\\\\\\\\?|&]captcha-challenge/,\\\\\\\\'\\\\\\\\')',1500);document.cookie='__jsl_clearance=1589364371.438|0|'+(function(){var _1t=[function(_1v){return _1v},function(_1t){return _1t},function(_1v){return eval('String.fromCharCode('+_1v+')')},function(_1v){for(var _1t=0;_1t<_1v.length;_1t++){_1v[_1t]=parseInt(_1v[_1t]).toString(36)};return _1v.join('')}],_1v=[[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],'S',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],'d',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+(-~[]-~[]+[[]][0])],'F',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))],'Hkav',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[~~[]]],'G',[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')],'D',[[-~{}]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+[((-~{}<<-~{})<<-~{})]],[(-~[]-~[]+[[]][0])+[(-~~~''+[-~-~~~'']>>-~-~~~'')],[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[(-~~~''+[-~-~~~'']>>-~-~~~'')]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+(-~[]-~[]+[[]][0])],[(-~[]-~[]+[[]][0])+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'WO',[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[((-~{}<<-~{})<<-~{})]],[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'%',[(-~~~''+[-~-~~~'']>>-~-~~~'')],'D'];for(var _t=0;_t<_1v.length;_t++){_1v[_t]=_1t[[3,1,2,1,2,1,3,0,1,3,1,0,1,3,2,3,2,3,1,2,3,2,1,0,1][_t]](_1v[_t])};return _1v.join('')})()+';Expires=Wed, 13-May-20 11:06:11 GMT;Path=/;'};if((function(){try{return !!window.addEventListener;}catch(e){return false;}})()){document.addEventListener('DOMContentLoaded',_1v,false)}else{document.attachEvent('onreadystatechange',_1v)}\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import execjs\n",
+    "\n",
+    "\n",
+    "des_js = 'function get_z(x, y) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    };\\n    x = x.replace(/@*$/, \"\").split(\"@\");\\n    z = f(y.match(/\\\\w/g).sort(function (x, y) {\\n        return f(x) - f(y)\\n    }).pop());\\n    return [x, y, z]\\n\\n}\\n\\nfunction get_js(x, y, z) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    };\\n    x = eval(x);\\n    z = z + 1;\\n    p = y.replace(/\\\\b\\\\w+\\\\b/g,\\n        function (y) {\\n            return x[f(y, z) - 1] || (\"_\" + y)\\n        });\\n    return p\\n\\n}\\n\\n\\nfunction once_js(x, y) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    },\\n        x = x.replace(/@*$/, \"\").split(\"@\");\\n    z = f(y.match(/\\\\w/g).sort(function (x, y) {\\n        return f(x) - f(y)\\n    }).pop());\\n    while (z++) try {\\n        g = y.replace(/\\\\b\\\\w+\\\\b/g,\\n            function (y) {\\n                return x[f(y, z) - 1] || (\"_\" + y)\\n            });\\n        return g\\n    } catch (_) {\\n    }\\n}'\n",
+    "des_js = execjs.compile(des_js)\n",
+    "first_js = response.text\n",
+    "x = re.findall('var x=\"(.*?)\"', first_js)[0]\n",
+    "y = re.findall(',y=\"(.*?)\"', first_js)[0]\n",
+    "second_js = des_js.call('once_js', x, y)\n",
+    "second_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "处理第二段js，去除document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"cookie='__jsl_clearance=1589364371.438|0|'+(function(){var _1t=[function(_1v){return _1v},function(_1t){return _1t},function(_1v){return eval('String.fromCharCode('+_1v+')')},function(_1v){for(var _1t=0;_1t<_1v.length;_1t++){_1v[_1t]=parseInt(_1v[_1t]).toString(36)};return _1v.join('')}],_1v=[[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],'S',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],'d',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+(-~[]-~[]+[[]][0])],'F',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))],'Hkav',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[~~[]]],'G',[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')],'D',[[-~{}]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+[((-~{}<<-~{})<<-~{})]],[(-~[]-~[]+[[]][0])+[(-~~~''+[-~-~~~'']>>-~-~~~'')],[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[(-~~~''+[-~-~~~'']>>-~-~~~'')]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+(-~[]-~[]+[[]][0])],[(-~[]-~[]+[[]][0])+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'WO',[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[((-~{}<<-~{})<<-~{})]],[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'%',[(-~~~''+[-~-~~~'']>>-~-~~~'')],'D'];for(var _t=0;_t<_1v.length;_t++){_1v[_t]=_1t[[3,1,2,1,2,1,3,0,1,3,1,0,1,3,2,3,2,3,1,2,3,2,1,0,1][_t]](_1v[_t])};return _1v.join('')})()+';Expires=Wed, 13-May-20 11:06:11 '\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_js = second_js.replace('\\\\\\\\', '\\\\')\n",
+    "\n",
+    "new_js = 'cookie' + new_js.split('document.cookie')[1]\n",
+    "new_js = new_js.split('GMT;Path=/;')[0] + \"'\"\n",
+    "\n",
+    "if re.findall(\"(var .{0,5}=)document\\.createElement\\('div'\\);\", new_js):\n",
+    "        _3d = re.findall(\"(var .{0,5}=)document\\.createElement\\('div'\\);\", new_js)\n",
+    "        _2b = re.findall(\"(var .{0,5}=).{0,5}\\.match\\(/https\\?:\\\\\\/\\\\\\//\\)\\[0\\];\", new_js)\n",
+    "        new_js = re.sub(\n",
+    "            \"var .{0,5}=document\\.createElement\\('div'\\);\", \n",
+    "            _3d[0] + f'\"{index_url.replace(\"http://\", \"\")}\";',\n",
+    "            new_js\n",
+    "        )\n",
+    "        new_js = re.sub(\"_.{0,5}\\.innerHTML='<a href=.{0,25}</a>';\", \"\", new_js)\n",
+    "        new_js = re.sub(\"_.{0,5}=.{0,5}\\.firstChild\\.href;\", \"\", new_js)\n",
+    "        new_js = re.sub(\"var .{0,5}=.{0,5}\\.match\\(/https\\?:\\\\\\/\\\\\\//\\)\\[0\\];\", _2b[0] + '\"http://\";', new_js)\n",
+    "        new_js = re.sub(\"_.{0,5}=.{0,5}\\.substr\\(.{0,5}\\.length\\)\\.toLowerCase\\(\\);\", \"\", new_js)\n",
+    "new_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "处理js，去除Window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find = re.findall(\"!*window\\[.*?\\]\", new_js)\n",
+    "if find:\n",
+    "    for f in find:\n",
+    "        if '!' in f:\n",
+    "            if len(re.findall('!', f)) % 2 == 0:\n",
+    "                new_js = new_js.replace(f, 'false')\n",
+    "            else:\n",
+    "                new_js = new_js.replace(f, 'true')\n",
+    "        else:\n",
+    "            new_js = new_js.replace(f, 'undefined')\n",
+    "new_js = new_js.replace('window.headless', 'undefined')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"cookie='__jsl_clearance=1589364371.438|0|'+(function(){var _1t=[function(_1v){return _1v},function(_1t){return _1t},function(_1v){return eval('String.fromCharCode('+_1v+')')},function(_1v){for(var _1t=0;_1t<_1v.length;_1t++){_1v[_1t]=parseInt(_1v[_1t]).toString(36)};return _1v.join('')}],_1v=[[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],'S',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],'d',[[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')]+(-~[]-~[]+[[]][0])],'F',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))],'Hkav',[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[~~[]]],'G',[((-~{}<<-~{})^-~~~'')-~~~''-~!{}+((-~{}<<-~{})^-~~~'')],'D',[[-~{}]+[-~[-~-~~~''-~-~~~''-~-~~~'']]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+[((-~{}<<-~{})<<-~{})]],[(-~[]-~[]+[[]][0])+[(-~~~''+[-~-~~~'']>>-~-~~~'')],[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[(-~~~''+[-~-~~~'']>>-~-~~~'')]],[[-~[-~-~~~''-~-~~~''-~-~~~'']]+(-~[]-~[]+[[]][0])],[(-~[]-~[]+[[]][0])+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'WO',[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+[-~-~~~''+((-~{}<<-~{})^-~~~'')]],[[(-~~~''+[-~-~~~'']>>-~-~~~'')]+[((-~{}<<-~{})<<-~{})]],[[[(-~{}<<-~{})]*(((-~{}<<-~{})^-~~~''))]+(-~!{}+(-~[]-~[]<<-~[]-~[])+[]+[[]][0])],'%',[(-~~~''+[-~-~~~'']>>-~-~~~'')],'D'];for(var _t=0;_t<_1v.length;_t++){_1v[_t]=_1t[[3,1,2,1,2,1,3,0,1,3,1,0,1,3,2,3,2,3,1,2,3,2,1,0,1][_t]](_1v[_t])};return _1v.join('')})()+';Expires=Wed, 13-May-20 11:06:11 '\""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取jsl_clearance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1589364371.438|0|zSWdRFz6HkavuG8DhJnxHtWOAyE%3D'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "js = \"\"\"\n",
+    "        function getJS() {\n",
+    "        %s\n",
+    "        return cookie\n",
+    "        }\n",
+    "    \"\"\" % new_js\n",
+    "exec_js = execjs.compile(js)\n",
+    "jsl = exec_js.call('getJS')\n",
+    "jsl = jsl.split(';')[0]\n",
+    "jsl_clearance = jsl.split('=')[1]\n",
+    "jsl_clearance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'8E11E8556264D5431C97B28A64F213DA-n2:-1'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 获取第一个jessionid\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};'\n",
+    "response2 = requests.get(index_url, headers=headers, proxies=proxy)\n",
+    "JSESSIONID1 = re.findall('JSESSIONID=(.*?);', response2.headers['Set-Cookie'])[0]\n",
+    "JSESSIONID1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'8E11E8556264D5431C97B28A64F213DA-n2:0'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 获取第二个jessionid\n",
+    "import time\n",
+    "\n",
+    "url = 'http://www.gsxt.gov.cn/SearchItemCaptcha?t=' + str(int(time.time()*1000))\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID1}'\n",
+    "response3 = requests.get(url, headers=headers, proxies=proxy)\n",
+    "JSESSIONID2 = re.findall('JSESSIONID=(.*?);', response3.headers['Set-Cookie'])[0]\n",
+    "JSESSIONID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试cookie，如果状态码是200，获取gt和challenge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID2}'\n",
+    "url = 'http://www.gsxt.gov.cn/SearchItemCaptcha?t=' + str(int(time.time()*1000))\n",
+    "response4 = requests.get(url, headers=headers, proxies=proxy)\n",
+    "response4.status_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gt_challenge_json = json.loads(response4.text)\n",
+    "gt = gt_challenge_json['gt']\n",
+    "challenge = gt_challenge_json['challenge']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'62756445cd524543f5a16418cd920ffd'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'8a7c7f8b5b53a72f95d50b9181c758e6'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "challenge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 下面开始搜索企业信息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 如果您看到这里，请给我一个star，谢谢，如有遗漏，请留言指正，感激！"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里先走一遍采集流程，本地打码服务的实现稍后整理，要先整理完模型训练部分才能打码\n",
+    "服务实现流程：\n",
+    "1. 拿到gt和challenge获取验证码，验证码有两种\n",
+    "    - 滑动3.0\n",
+    "    - 文字点选，这部分的训练模型已经开源了，移步首页\n",
+    "2. 破解验证码，获取validate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 本地打码服务器，所有验证码类型在这里统一处理，返回validate即可\n",
+    "get_valid_server = 'http://127.0.0.1:5051'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Response [200]>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "url = f'{get_valid_server}/geetest?gt={gt}&challenge={challenge}'\n",
+    "resp = requests.get(url)\n",
+    "resp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'data': {'challenge': '8a7c7f8b5b53a72f95d50b9181c758e6',\n",
+       "  'result': 'success',\n",
+       "  'validate': 'fba9455b230064c63164bb06f453f52e'},\n",
+       " 'status': 'success'}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_resu = json.loads(resp.text)\n",
+    "\n",
+    "val_resu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "成功获取validate，开始进行搜索"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lxml.etree import _Element\n",
+    "from lxml import etree\n",
+    "\n",
+    "def parse_list(content):\n",
+    "    def try_catch(data, patten):\n",
+    "        if isinstance(data, _Element):\n",
+    "            try:\n",
+    "                target = re.sub('\\s', '', data.xpath(patten)[0].strip())\n",
+    "            except:\n",
+    "                target = None\n",
+    "            return target\n",
+    "        return data\n",
+    "    a_list = []\n",
+    "    content_list = content\n",
+    "    html = etree.HTML(content_list)\n",
+    "    list_len = int(html.xpath('//span[@class=\"search_result_span1\"]/text()')[0])\n",
+    "    if list_len == 0:\n",
+    "        return 'None'\n",
+    "    else:\n",
+    "        targit_len = 1\n",
+    "    for i in range(1, targit_len + 1):\n",
+    "        try:\n",
+    "            url = html.xpath('//div[@class=\"main-layout fw f14\"]/div[2]/a[' + str(i) + ']/@href')[0]\n",
+    "            href = 'http://www.gsxt.gov.cn{}'.format(url)\n",
+    "        except:\n",
+    "            href = None\n",
+    "        dic = {\n",
+    "            'href': href\n",
+    "        }\n",
+    "        a_list.append(dic)\n",
+    "    return a_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def list_page(val_resu, jsluid, jsl_clearance, JSESSIONID, search_word):\n",
+    "    time.sleep(1)\n",
+    "    validate = val_resu['data']['validate']\n",
+    "    chall = val_resu['data']['challenge']\n",
+    "    data = {\n",
+    "        'tab': 'ent_tab',\n",
+    "        'province': '',\n",
+    "        'geetest_challenge': chall,\n",
+    "        'geetest_validate': validate,\n",
+    "        'geetest_seccode': '{}|jordan'.format(validate),\n",
+    "        'token': '57571798',\n",
+    "        'searchword': f'{search_word}',\n",
+    "    }\n",
+    "    time.sleep(5)\n",
+    "    headers =index_header.copy()\n",
+    "    headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID}'\n",
+    "    post_url = 'http://www.gsxt.gov.cn/corp-query-search-1.html'\n",
+    "    content = requests.post(url=post_url, data=data, headers=headers, verify=False, timeout=(60, 60), proxies=proxy)\n",
+    "    if content.status_code == 200:\n",
+    "        if '您的IP地址' in content.text:\n",
+    "            print('请更换代理')\n",
+    "        else:\n",
+    "            return content.text\n",
+    "    else:\n",
+    "        print('cookies失效')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "配置要搜索的关键词"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_word = \"百度\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if val_resu['data']['result'] == 'fail':\n",
+    "    print('get valid error')\n",
+    "else:\n",
+    "    content_text = list_page(val_resu, jsluid, jsl_clearance, JSESSIONID2, search_word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "href = parse_list(content_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'href': 'http://www.gsxt.gov.cn/%7BC132146AA913B5B85A24D3698BCF37F48AE6B456EC80C4B6902AE997FB525D23D4B8FC8EA812404D51929F83D12674CE8B7534DAFFFB498F8BCA2CEAEEE4298329BF29BF29CF7DB57DEBF41294709611BD80320BCAAD4BAF6EC2DD5BBD3ABC7A1D9AA720E1C3AB53FA3310F2B64E09FE3DDF8822880F998AD8D5C99BF70FEDBA108610861086-1589363839854%7D'}]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "href"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "headers =index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID2}'\n",
+    "detail = requests.get(url=href[0]['href'], headers = headers, proxies = proxy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "输出结果正确"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'北京百度网讯科技有限公司'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "etree.HTML(detail.text).xpath(\"//h1\")[0].text.strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 如果您看到这里，请给我一个star，谢谢，如有遗漏，请留言指正，感激！"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "blog",
+   "language": "python",
+   "name": "blog"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/doc/.ipynb_checkpoints/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213-checkpoint.ipynb" "b/doc/.ipynb_checkpoints/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213-checkpoint.ipynb"
new file mode 100644
index 0000000..af73677
--- /dev/null
+++ "b/doc/.ipynb_checkpoints/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213-checkpoint.ipynb"
@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 在Python中使用我们训练好点模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "前面我们实现了`汉字位置检测`和`汉字分类识别`模型，实际使用肯定要结合封装成接口来使用，会比较方便。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在darknet的官方项目中，已经为我们提供了一个demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ctypes import *\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "def sample(probs):\n",
+    "    s = sum(probs)\n",
+    "    probs = [a/s for a in probs]\n",
+    "    r = random.uniform(0, 1)\n",
+    "    for i in range(len(probs)):\n",
+    "        r = r - probs[i]\n",
+    "        if r <= 0:\n",
+    "            return i\n",
+    "    return len(probs)-1\n",
+    "\n",
+    "def c_array(ctype, values):\n",
+    "    arr = (ctype*len(values))()\n",
+    "    arr[:] = values\n",
+    "    return arr\n",
+    "\n",
+    "class BOX(Structure):\n",
+    "    _fields_ = [(\"x\", c_float),\n",
+    "                (\"y\", c_float),\n",
+    "                (\"w\", c_float),\n",
+    "                (\"h\", c_float)]\n",
+    "\n",
+    "class DETECTION(Structure):\n",
+    "    _fields_ = [(\"bbox\", BOX),\n",
+    "                (\"classes\", c_int),\n",
+    "                (\"prob\", POINTER(c_float)),\n",
+    "                (\"mask\", POINTER(c_float)),\n",
+    "                (\"objectness\", c_float),\n",
+    "                (\"sort_class\", c_int)]\n",
+    "\n",
+    "\n",
+    "class IMAGE(Structure):\n",
+    "    _fields_ = [(\"w\", c_int),\n",
+    "                (\"h\", c_int),\n",
+    "                (\"c\", c_int),\n",
+    "                (\"data\", POINTER(c_float))]\n",
+    "\n",
+    "class METADATA(Structure):\n",
+    "    _fields_ = [(\"classes\", c_int),\n",
+    "                (\"names\", POINTER(c_char_p))]\n",
+    "\n",
+    "    \n",
+    "\n",
+    "lib = CDLL(\"libdarknet.so\", RTLD_GLOBAL) # 配置so文件路径，最好是绝对路径\n",
+    "lib.network_width.argtypes = [c_void_p]\n",
+    "lib.network_width.restype = c_int\n",
+    "lib.network_height.argtypes = [c_void_p]\n",
+    "lib.network_height.restype = c_int\n",
+    "\n",
+    "predict = lib.network_predict\n",
+    "predict.argtypes = [c_void_p, POINTER(c_float)]\n",
+    "predict.restype = POINTER(c_float)\n",
+    "\n",
+    "set_gpu = lib.cuda_set_device\n",
+    "set_gpu.argtypes = [c_int]\n",
+    "\n",
+    "make_image = lib.make_image\n",
+    "make_image.argtypes = [c_int, c_int, c_int]\n",
+    "make_image.restype = IMAGE\n",
+    "\n",
+    "get_network_boxes = lib.get_network_boxes\n",
+    "get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]\n",
+    "get_network_boxes.restype = POINTER(DETECTION)\n",
+    "\n",
+    "make_network_boxes = lib.make_network_boxes\n",
+    "make_network_boxes.argtypes = [c_void_p]\n",
+    "make_network_boxes.restype = POINTER(DETECTION)\n",
+    "\n",
+    "free_detections = lib.free_detections\n",
+    "free_detections.argtypes = [POINTER(DETECTION), c_int]\n",
+    "\n",
+    "free_ptrs = lib.free_ptrs\n",
+    "free_ptrs.argtypes = [POINTER(c_void_p), c_int]\n",
+    "\n",
+    "network_predict = lib.network_predict\n",
+    "network_predict.argtypes = [c_void_p, POINTER(c_float)]\n",
+    "\n",
+    "reset_rnn = lib.reset_rnn\n",
+    "reset_rnn.argtypes = [c_void_p]\n",
+    "\n",
+    "load_net = lib.load_network\n",
+    "load_net.argtypes = [c_char_p, c_char_p, c_int]\n",
+    "load_net.restype = c_void_p\n",
+    "\n",
+    "do_nms_obj = lib.do_nms_obj\n",
+    "do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]\n",
+    "\n",
+    "do_nms_sort = lib.do_nms_sort\n",
+    "do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]\n",
+    "\n",
+    "free_image = lib.free_image\n",
+    "free_image.argtypes = [IMAGE]\n",
+    "\n",
+    "letterbox_image = lib.letterbox_image\n",
+    "letterbox_image.argtypes = [IMAGE, c_int, c_int]\n",
+    "letterbox_image.restype = IMAGE\n",
+    "\n",
+    "load_meta = lib.get_metadata\n",
+    "lib.get_metadata.argtypes = [c_char_p]\n",
+    "lib.get_metadata.restype = METADATA\n",
+    "\n",
+    "load_image = lib.load_image_color\n",
+    "load_image.argtypes = [c_char_p, c_int, c_int]\n",
+    "load_image.restype = IMAGE\n",
+    "\n",
+    "rgbgr_image = lib.rgbgr_image\n",
+    "rgbgr_image.argtypes = [IMAGE]\n",
+    "\n",
+    "predict_image = lib.network_predict_image\n",
+    "predict_image.argtypes = [c_void_p, IMAGE]\n",
+    "predict_image.restype = POINTER(c_float)\n",
+    "\n",
+    "def classify(net, meta, im):\n",
+    "    out = predict_image(net, im)\n",
+    "    res = []\n",
+    "    for i in range(meta.classes):\n",
+    "        res.append((meta.names[i], out[i]))\n",
+    "    res = sorted(res, key=lambda x: -x[1])\n",
+    "    return res\n",
+    "\n",
+    "def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):\n",
+    "    im = load_image(image, 0, 0)\n",
+    "    num = c_int(0)\n",
+    "    pnum = pointer(num)\n",
+    "    predict_image(net, im)\n",
+    "    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)\n",
+    "    num = pnum[0]\n",
+    "    if (nms): do_nms_obj(dets, num, meta.classes, nms);\n",
+    "\n",
+    "    res = []\n",
+    "    for j in range(num):\n",
+    "        for i in range(meta.classes):\n",
+    "            if dets[j].prob[i] > 0:\n",
+    "                b = dets[j].bbox\n",
+    "                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))\n",
+    "    res = sorted(res, key=lambda x: -x[1])\n",
+    "    free_image(im)\n",
+    "    free_detections(dets, num)\n",
+    "    return res\n",
+    "\n",
+    "def run():\n",
+    "    net = load_net(\n",
+    "        \"\".encode(), # cfg文件路径\n",
+    "        \"\".encode(), # weights权重文件路径\n",
+    "        0  # 默认0\n",
+    "    )\n",
+    "    meta = load_meta(\"\".encode()) # data文件路径\n",
+    "    # 执行预测\n",
+    "    r = detect(\n",
+    "        net, \n",
+    "        meta, \n",
+    "        \"\".encode()\n",
+    "    )\n",
+    "    print(r)\n",
+    "    \n",
+    "    \n",
+    "if __name__ == \"__main__\":\n",
+    "    run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里几个要注意的点：\n",
+    "\n",
+    "- 文件路径编码\n",
+    "- libdarknet.so 文件的路径配置"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "blog",
+   "language": "python",
+   "name": "blog"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Ubuntu18.04 install darknet yolo-v3|cuda|cudnn|opencv|anaconda.md b/doc/Ubuntu18.04 install darknet yolo-v3|cuda|cudnn|opencv|anaconda.md
new file mode 100755
index 0000000..f279d6d
--- /dev/null
+++ b/doc/Ubuntu18.04 install darknet yolo-v3|cuda|cudnn|opencv|anaconda.md	
@@ -0,0 +1,350 @@
+# Ubuntu18.04安装测试darknet yolo-v3|cuda|cudnn|opencv|anaconda 
+
+> 机器配置：
+>
+> cpu：i3-9400
+>
+> gpu：gtx1650-4g
+>
+> 系统版本：Ubuntu18.04
+
+前段时间接触到darknet目标检测的项目，由于需要做效率优化，之前在mac上使用yolo3，项目开发很顺利，最终瓶颈是cpu的运算速度，故需要在有显卡的机器进行测试。
+
+[TOC]
+
+---
+
+## 一. Darknet项目
+
+项目官网：https://pjreddie.com/darknet/yolo/
+
+使用方式也很简单，官网都有介绍，这里就举个例子：
+
+- 下载编译项目：
+
+```shell
+git clone https://github.com/pjreddie/darknet
+cd darknet
+make
+```
+
+- 下载权重文件：
+
+```shell
+wget https://pjreddie.com/media/files/yolov3.weights
+```
+
+- 测试
+
+```shell
+./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
+```
+
+然后在项目根目录下会生成`predictions.png`，打开即可看到效果。
+
+到这里就可以进行二次开发了，但是都是基于CPU的，无论是前期的训练还是后期的测试，速度都是很慢的，GPU的速度在我测试下有200倍的提升（我的GPU一般，还有很大提升空间）。
+
+---
+
+## 二. Ubuntu安装NVIDIA显卡驱动|CUDA|CUDNN
+
+要使用GPU进行项目的运算，除了改项目的配置以外，最重要，也是比较麻烦的部分就是环境的安装，坑比较多，需要细心一点。
+
+### 2.1 显卡驱动
+
+输入命令看一下系统推荐的驱动版本号，再进行相应版本的驱动安装即可。
+
+命令：`ubuntu-drivers devices`
+
+```shell
+(base) iron@iron-H310M-S2-2-0:~/桌面/share/darknet$ ubuntu-drivers devices
+== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
+modalias : pci:v000010DEd00001F82sv00001B4Csd00001320bc03sc00i00
+vendor   : NVIDIA Corporation
+driver   : nvidia-driver-435 - distro non-free
+driver   : nvidia-driver-440 - third-party free recommended
+driver   : xserver-xorg-video-nouveau - distro free builtin
+```
+
+可以看到我的系统`recommended`版本是`nvidia-driver-440`，直接使用命令安装：
+
+`sudo apt-get install nvidia-driver-440`
+
+测试安装是否成功：
+
+使用`nvidia-smi`命令，输出显卡信息即成功。
+
+> 提示：显卡驱动也可以去NVIDIA官网查询对应的版本进行安装，下载Linux版本即可。我个人习惯使用上面的方式。
+
+### 2.2 CUDA
+
+CUDA官网下载页面：https://developer.nvidia.com/cuda-toolkit-archive
+
+目前出到了`10.2`版本，不过我下载的`10.1`，为了求稳，也为了遇到问题能在网上找到更多的解决办法。
+
+下载选择：Linux-->x86_64-->ubuntu-->18.04-->runfile
+
+这里将我下载的`10.1`版本传到百度了：`链接: https://pan.baidu.com/s/1PyQJcR3G-kKIHqOFtFiBHg 提取码: ssp9`
+
+- 安装依赖
+
+`sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev`
+
+- 安装CUDA
+
+进入CUDA的下载目录，执行安装：
+
+`sudo ./cuda_10.1.105_418.39_linux.run `
+
+安装过程没什么可说的，唯一要注意的是：在出现`Install NVIDIA Accelerated Graphics Driver for Linux-x86_64?`时，输入`no`，因为上一步我们装过显卡驱动了，这里不用安装，其他步骤都是`yes`或者`回车`。
+
+- 配置环境变量
+
+配置前确定指定的目录已经存在
+
+```bash
+sudo vi ~/.bashrc
+#文件末尾添加
+export PATH=/usr/local/cuda-10.1/bin:$PATH
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64
+```
+```shell
+#刷新配置文件
+sudo source ~/.bashrc
+```
+
+- 测试
+
+检查是否安装成功
+
+`cd  /usr/local/cuda-10.1/samples/1_Utilities/deviceQuery`
+`sudo make `
+`./deviceQuery`
+
+输出CUDA	VERSION等信息即成功
+
+### 2.3 CUDNN
+
+cudnn安装要注意的是版本要和cuda一致，下载页面：https://developer.nvidia.com/rdp/cudnn-download
+
+下载需要注册开发者账号，按照步骤注册即可。
+
+百度云`10.1`版本：`链接: https://pan.baidu.com/s/1oAsw6LUMpjW9NfNL_JVIJA 提取码: zvui`
+
+- 将cudann的压缩包解压
+- 解压后得到cuda文件夹，文件夹和cuda安装目录中的文件夹是对应的，将文件复制到cuda对应的文件夹中即可
+
+> 上一步的cuda安装完成以后路径为：/usr/local/cuda10.1
+
+示例：
+
+```shell
+tar -zxvf cudnn-10.1-linux-x64-v7.6.5.32.tgz
+
+sudo cp cuda/include/cudnn.h    /usr/local/cuda10.1/include
+sudo cp cuda/lib64/libcudnn*    /usr/local/cuda10.1/lib64
+sudo chmod a+r /usr/local/cuda/include/cudnn.h   /usr/local/cuda10.1/lib64/libcudnn*
+```
+
+最后更新软链接，这步如果不执行，后面会报错`xxxxxx.so.x 不是符号链接`
+
+> 这个so文件的版本要提前看好，我文件夹里的so文件是libcudnn.so.7.6.5
+
+```shell
+cd /usr/local/cuda-10.1/lib64/  
+
+sudo chmod +r libcudnn.so.7.6.5
+sudo ln -sf libcudnn.so.7.6.5 libcudnn.so.7  
+sudo ln -sf libcudnn.so.7 libcudnn.so  
+sudo ldconfig 
+```
+
+或者在遇到某某so文件不是符号链接的错误时，执行：`sudo ldconfig -v`，找到错误的so文件，创建一个软链。
+
+---
+
+## 三. 使用GPU测试运行项目
+
+前面装好了所需的环境，接下来测试yolo项目是否可以在GPU下运行。
+
+- 修改darknet项目配置文件
+
+在darknet项目根目录下有`Makefile`文件，修改其中配置：
+
+```shell
+GPU=1
+CUDNN=1
+```
+
+- 重新编译项目：
+
+`make clean`
+
+`make`
+
+- 测试：
+
+`./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg`
+
+这里是我的测试速度对比：质的飞跃
+
+- CPU
+
+```shell
+Loading weights from yolov3.weights...Done!
+data/dog.jpg: Predicted in 19.692001 seconds.
+dog: 6379%
+truck: 1609%
+truck: 226%
+bicycle: 3912%
+```
+
+- GPU
+
+```shell
+Loading weights from yolov3.weights...Done!
+data/dog.jpg: Predicted in 0.131651 seconds.
+dog: 100%
+truck: 92%
+bicycle: 99%
+```
+
+---
+
+## 四. 配置OPENCV
+
+出于对darknet视频实时检测的好奇，决定自己做一个目标检测的样例视频，自己拍摄剪辑然后做目标检测，所以又需要安装OPENCV来支持视频处理。
+
+### 4.1 文件下载
+
+文件需要下载两个，一个是`opencv`一个是`opencv_contrib`，版本要对应，我这里下载的是4.3版本
+
+- 官方
+
+opencv：https://opencv.org/releases/
+
+opencv_contrib：https://github.com/opencv/opencv_contrib/releases
+
+- 百度盘
+
+opencv：`链接: https://pan.baidu.com/s/163SbilBZdsS2MxqTdMaeEg 提取码: asja`
+
+opencv_contrib：`链接: https://pan.baidu.com/s/1LhMkqBr5U5b8b7qXTZDAtA 提取码: niqq`
+
+### 4.2 安装
+
+- 安装依赖
+
+```shell
+    sudo apt-get install build-essential
+    sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
+    sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
+```
+
+- 安装cmake-gui
+
+```shell
+    sudo apt-get install cmake-qt-gui
+```
+
+- 将`opencv`和`opencv_contrib`解压后放到统一目录下
+- 在`opencv`目录下创建`build`目录，用来存放编译文件
+- 进入`build`目录，执行：`cmake-gui`
+- 在界面上方配置源码和编译目录
+  - where is the source code：# 指定opencv的目录
+  - where to build the binaries：# 指定编译目录（即上一步创建的build目录）
+- 点击`configure`
+  - Unix Makefiles - > Use default native compilers- > Finish
+
+- 编译参数配置
+  - CMAKE_BUILD_TYPE：RELEASE
+  - CMAKE_INSTALL_PREFIX：# 配置安装目录
+  - OPENCV_EXTRA_MODULES_PATH：# 配置opencv_contrib的modules目录
+
+- 点击`Generate`
+- 在**终端**进入`build`目录，执行`make`，开始编译，没有意外会编译成功进行下一步，而现实是不可能没有意外...
+- 编译成功后，再执行`make install `进行安装
+
+#### 4.2.1 编译时的意外
+
+- fatal error：boostdesc_bgm.i 没有那个文件或目录
+
+文件下载：`链接: https://pan.baidu.com/s/1cjEtx0dIrmeUJdFDmb-KHw 提取码: ir4h `
+
+全部文件放入`opencv_contrib/modules/xfeatures2d/src/`目录下
+
+- fatal error : feature2d/test/test_detectors_regression.impl.hpp
+
+这是一类头文件的问题，提示找不到文件，可能错误地方不止一个，但是解决方法是一样的
+
+> 定位到出错的`cpp`文件
+>
+> 编辑这个文件，将`#include "feature2d/test/test_detectors_regression.impl.hpp"`改为`#include "test_detectors_regression.impl.hpp"`
+>
+> 然后将`test_detectors_regression.impl.hpp`文件放到当前`cpp`文件目录中就可以了
+>
+> 那这个文件在哪呢？
+>
+> 所有缺失的hpp文件在opencv4.3/modules/features2d/test文件夹中都能找到
+
+### 4.3 配置环境变量
+-   编辑文件sudo vim /etc/ld.so.conf.d/opencv.conf ，写入opencv安装目录下的lib目录路径
+-   编辑文件sudo vim /etc/bash.bashrc ，末尾添加两行内容：
+    - `PKG_CONFIG_PATH=$PKG_CONFIG_PATH:opencv安装目录/lib/pkgconfig `
+    - `export PKG_CONFIG_PATH`
+- 最后执行：source /etc/profile
+
+### 4.4 测试
+
+测试是否安装成功：pkg-config --cflags --libs opencv
+
+```shell
+(base) iron@iron-H310M-S2-2-0:/usr/local/cuda/lib64$ pkg-config --cflags --libs opencv
+-I/home/iron/opencv-4.3.0/include/opencv4 -L/home/iron/opencv-4.3.0/lib -lopencv_shape -lopencv_stitching -lopencv_objdetect -lopencv_superres -lopencv_videostab -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_video -lopencv_photo -lopencv_ml -lopencv_imgproc -lopencv_flann -lopencv_core
+```
+
+---
+
+## 五. 再次编译darknet项目
+
+我们这次加入了opencv的支持，和之前一样，先改配置文件，再重新编译项目
+
+- 改darknet项目下：Makefile文件
+
+```shell
+GPU=1
+CUDNN=1
+OPENCV=1
+```
+
+- 重新编译项目：
+
+`make clean`
+
+`make`
+
+不出意外的话直接编译成功
+
+### 5.1 darknet + opencv编译的意外
+
+- image_opencv.cpp:12:1: error: ‘IplImage’
+
+这看似是代码兼容问题，在我们的darknet项目下可以找到这个出错的文件：darknet/src/image_opencv.cpp
+
+最简单的解决办法是把文件替换成我的这个，我已经改好了：
+
+`链接: https://pan.baidu.com/s/1bns2HLAFd--pvBHJpTZaLQ 提取码: 929p`
+
+
+
+---
+
+## 六、参考文献
+
+> [opencv4.2.0+darknet安装 ./src/image_opencv.cpp:12:1: error: ‘IplImage’ 报错](https://blog.csdn.net/a135013563/article/details/104859395)
+>
+> [YOLO: Real-Time Object Detection](https://pjreddie.com/darknet/yolo/)
+
+---
+
+**如果您看到了这里，请给我一个star:star:谢谢，如果有遗漏，请留言指正，感激！** 
\ No newline at end of file
diff --git a/doc/classifier_train_handbook.md b/doc/classifier_train_handbook.md
new file mode 100644
index 0000000..34db618
--- /dev/null
+++ b/doc/classifier_train_handbook.md
@@ -0,0 +1,108 @@
+# 汉字分类识别模型训练手册
+
+理论上可以不用`汉字检测+汉字识别`分两步，一步就可以完成，目前局限在数据集上，我手里的一步模型识别效果不是很理想。
+
+此次我会挑几个字出来训练，训练数据我会放在`files/train`目录下，供大家练习和参考，更多讨论和需求欢迎加入我刚刚建立的QQ群：:rocket:`1040784971`:rocket:
+
+---
+
+[环境的安装和准备看这里](./Ubuntu18.04%20install%20darknet%20yolo-v3%7Ccuda%7Ccudnn%7Copencv%7Canaconda.md)
+
+[汉字检测定位模型训练手册看这里](./detector_train_handbook.md)
+
+---
+
+## 一、数据准备
+
+这次的数据集比较简单，我都放好了在`files/train/hanzi_classification`目录下，每个文件的文件名即标签。
+
+文件名如下：
+
+```shell
+0092e7d888a1ab411eabd50e9cc5b2fa旅_u65c5.jpg  8b9c6569c7e55291f1dcff7026da1806区_u533a.jpg
+036cac9f0db46babad19a9f129ed7587旅_u65c5.jpg  90e9fbca52b7faa571d430f9be27bfdd态_u6001.jpg
+...
+...
+```
+
+训练时读取的是`下划线_`后面的`Unicode`部分作为标签（中文支持不好），前面一段是为了方便区分。
+
+训练数据包含了`生态旅游区`五个字，每个字`50`张图。
+
+![](../media/0b7bbb3595309f7f9123704ef354a52d%E6%97%85_u65c5.jpg)
+
+接下来配置几个文件即可开始训练：
+
+- data文件：`data/hanzi_clas.data`
+
+  ```txt
+  classes = 5				# 分类（标签）数
+  train = data/hanzi_clas_train.txt		# 训练数据路径
+  labels = data/hanzi_clas.labels		# 标签文件
+  backup = backup/		# 模型保存文件
+  ```
+
+  - `hanzi_clas_train.txt`文件的生成：
+
+    这个文件内容是每个训练图片的绝对路径，一共250条记录对应250张图片（修改成自己电脑上的路径）
+
+    ```txt
+    /Users/hanzi_clas_train/719ce7a4febff78be03c33a6e11a3231生_u751f.jpg
+    /Users/hanzi_clas_train/fedb1de7214d3c429bcb05ea28e92862生_u751f.jpg
+    ...
+    ...
+    ```
+
+  - `data/hanzi_clas.labels`文件的生成：
+
+    记录需要分类的标签名称，我们有5个汉字需要分类，这里用`Unicode`编码来表示五个汉字
+
+    ```txt
+    u6001
+    u65c5
+    u6e38
+    u751f
+    u533a
+    ```
+
+- cfg文件：模型网络配置文件`cfg/hanzi_clas.cfg`，具体配置是什么意思Google吧，有感兴趣的童鞋再留言更新。
+
+---
+
+## 模型训练
+
+1. 训练
+
+   `./darknet classifier train data/hanzi_clas.data cfg/hanzi_clas.cfg`
+
+   ```shell
+   ...
+   ...
+   ...
+   99, 25.344: 1.554884, 1.496595 avg, 0.000000 rate, 0.353925 seconds, 6336 images
+   Loaded: 0.000017 seconds
+   100, 25.600: 1.523866, 1.499322 avg, 0.000000 rate, 0.419332 seconds, 6400 images
+   Saving weights to backup/hanzi_clas.weights
+   ```
+
+   训练完成后，会在`backup`文件夹下生成`weights`文件
+
+2. 测试
+
+   `./darknet classifier predict data/hanzi_clas.data cfg/hanzi_clas.cfg backup/hanzi_clas.weights `
+
+   ```shell
+   ...
+   ...
+   ...
+   Loading weights from backup/hanzi_clas.weights...Done!
+   top: Using default '1'
+   Enter Image Path: 
+   ```
+
+   输入要测试的图片地址即可。
+
+---
+
+**如果您看到了这里，给个star:star:，谢谢，如有遗漏请留言，感激**
+
diff --git a/doc/detector_train_handbook.md b/doc/detector_train_handbook.md
new file mode 100644
index 0000000..497ad3a
--- /dev/null
+++ b/doc/detector_train_handbook.md
@@ -0,0 +1,177 @@
+# 定位模型训练手册
+
+此次更新了汉字目标检测的训练内容，从基础的环境编译开始。顺便说一下，GPU的环境是必要的，CPU训练极慢...
+
+关于最终模型识别效率：我生产环境是0.08S一张图，模型文件200多M，用的垃圾显卡。如果没有显卡，可以进行模型优化，如果有反应这个问题的童鞋，后续更新优化。
+
+[环境的安装和准备看这里](./Ubuntu18.04%20install%20darknet%20yolo-v3%7Ccuda%7Ccudnn%7Copencv%7Canaconda.md)
+
+## 一、数据准备
+
+数据包括了训练数据集和验证集。训练集我放了1K份在`files/train`目录下；验证集放了500份在`files/valid`目录下。
+
+### 1.1 训练数据集的准备
+
+介绍一下数据集的格式。目录`files/train/jiyan_train_imgs_txt_1K`下的文件形式如下：
+
+```shell
+aba5bec4daa6c11fdd684594778e7737.jpg
+aba5bec4daa6c11fdd684594778e7737.txt
+5679c2443905cec1f43c9b2b69fd9efd.jpg 
+5679c2443905cec1f43c9b2b69fd9efd.txt 
+...
+...
+```
+
+我们本次的检测目标只有一个是汉字`hanzi`（classes=1），简单来说目标就是输出一个图片中所有汉字的坐标。达到这种效果：
+
+![预测结果图片](../hanzi_detection/predictions.jpg)
+
+在构建训练集的时候，我们要同时准备`图片`和`坐标`两个文件。
+
+一个`jpg`图片，对应一个同名的`txt`文件，图片文件没什么好说的，就是待检测的图片。
+
+`txt`文件内容如下：
+
+```txt
+0 0.6918604651162791 0.6875 0.20348837209302326 0.15625
+0 0.7441860465116279 0.1171875 0.20348837209302326 0.15625
+0 0.2616279069767442 0.203125 0.20348837209302326 0.15625
+0 0.3226744186046512 0.4895833333333333 0.20348837209302326 0.15625
+```
+
+这里的四行内容，对应图片上的四个汉字的`classes`和`position`信息，因为我们就只有一个（标签）`classes`是`hanzi`，所以所有的值都是`0`。如果我们有两个目标需要检测，比如`中文`和`英文`，classes=2，`txt`文件的每一行的第一列就可能是：`0`或者`1`，多个目标，以此类推。
+
+这个`txt`文件的构造需要说明一下，第一列上面已经说了；后面四列代表着汉字的`x,y,width,height`，而且是经过处理的值，这里介绍一下处理过程：
+
+我们在标记训练数据的时候，给每个汉字的位置信息都标注出来，如下：
+
+![ab9a8dcdda85f1d8789909803374fbea](../media/ab9a8dcdda85f1d8789909803374fbea.jpg)
+
+以`太`字为例，坐标为：`x1y1,x2y2`，整个图片大小为：`width:344，height:384`
+
+```python
+Python 3.7.4 (default, Aug 13 2019, 15:17:50) 
+[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
+Type "help", "copyright", "credits" or "license" for more information.
+>>> def convert(size, box):
+...     dw = 1. / (size[0])
+...     dh = 1. / (size[1])
+...     x = (box[0] + box[1]) / 2.0 - 1
+...     y = (box[2] + box[3]) / 2.0 - 1
+...     w = box[1] - box[0]
+...     h = box[3] - box[2]
+...     x = x * dw
+...     w = w * dw
+...     y = y * dh
+...     h = h * dh
+...     return (x, y, w, h)
+... 
+
+>>> convert((344,384),(22,82,195,265))
+(0.14825581395348836, 0.5963541666666666, 0.1744186046511628, 0.18229166666666666)
+```
+
+这里的`convert`参数为：`convert((width, height), (x1, x2, y1, y2))`，输出的结果即为`txt`文件后四列的内容。
+
+最重要的数据集准备完了，然后再配置一下模型数量参数即可开始训练，有如下几个文件：
+
+- data文件：data/detector.data，例子：
+
+  ```data
+  classes = 1											# 检测目标数量
+  train = train/train.txt					# 训练文件路径
+  valid = valid/valid.txt					# 验证文件路径
+  names = data/jiyan.names				# names文件路径
+  backup = backup/								# 训练完的模型保存路径
+  ```
+
+  - `train.txt`文件的生成，这个文件内容是`训练数据集`中每个`jpg`文件的`绝对路径`，只要`jpg`文件的路径即可，模型会找对应路径下的`txt`文件。
+
+    ```txt
+    /home/files/jiyan_train_imgs_txt_1K/c5ff5214ef37bcf7db239b02a94a34da.jpg
+    /home/files/jiyan_train_imgs_txt_1K/d088c915a03c77bc89a9e466074f10a8.jpg
+    /home/files/jiyan_train_imgs_txt_1K/814613e0ac5d67cd9f9fdce55c8ec0d4.jpg
+    ...
+    ...
+    ```
+
+    可以在Linux中直接生成：
+
+    `find path_to_train_dir -name '*.jpg' > path/train.txt`
+
+  - `valid.txt`文件生成同上
+
+- names文件，内容如下：要检测的目标名称
+
+  ```txt
+  hanzi
+  ```
+
+- 权重文件
+
+  在训练的时候可以基于官方的`卷积权重`来开始，下载到项目的`backup`目录下即可。
+
+  文件太大，请自行下载。
+
+  [下载地址](https://pjreddie.com/media/files/darknet53.conv.74)
+  
+- 配置文件：`cfg/detecter.cfg`
+
+  其中`max_batches = 1500`是训练的轮数，可以根据需要修改。
+
+
+
+## 训练模型
+
+当我们所有数据文件都准备好后，就可以编译项目，开始训练了。
+
+1. 首先进入项目文件夹：`cd hanzi_detection`
+
+2. 修改`Makefile`文件：`vim Makefile`
+
+   如果不用gpu训练，这步跳过。
+
+```shell
+GPU=1			# 使用GPU
+...
+...
+NVCC=/usr/local/cuda-10.1/bin/nvcc 			# nvcc路径改为cuda的安装目录下的nvcc
+```
+
+3. 编译项目：`make clean` `make`
+
+4. 训练：
+
+   `./darknet detector train data/detector.data cfg/detector.cfg backup/darknet53.conv.74`
+
+   训练完会在`backup`目录下生成`xxx_final.weights`文件
+
+5. 验证集
+
+   `./darknet detector valid cfg/detector.data cfg/detector.cfg backup/xxx_final.weights`
+
+   在`results`目录下生成结果
+
+6. 单张检测
+
+   `./darknet detector test cfg/detector.data cfg/detector.cfg backup/xxx_final.weights`
+
+   然后输入要检测的图片地址，即可在根目录下生成`predictions.jpg`
+
+   ![](../hanzi_detection/predictions.jpg)
+
+
+
+**如果您看到了这里，请给个star，谢谢，如有错误遗漏，请留言指正，感激**
+
+
+
+
+
+
+
+
+
+
+
diff --git a/doc/jiyan_gt_challenge.md b/doc/jiyan_gt_challenge.md
new file mode 100644
index 0000000..580939e
--- /dev/null
+++ b/doc/jiyan_gt_challenge.md
@@ -0,0 +1,45 @@
+### 项目介绍
+
+数据采集的过程中难免会遇到一些验证码需要解决，这里介绍一下我遇到jiyan平台的验证码是如何进行分析的。
+
+**声明**: 本项目仅供学习交流使用，严禁用于商业和违法行为，否则产生的一切后果与本人无关。
+
+利益相关，无法开放源码，为了方便说明，这里使用一些文字代替是必要的。
+
+内容比较草率，不过我相信真正研究过的人可以看懂，伸手党估计不合适。
+
+**如果对您有用，请给项目一个star，谢谢**
+
+> 感兴趣或者看不懂可以加项目首页的微信交流，记得备注
+
+### 参数分析
+
+jiyan的关键参数是`gt`和`challenge`
+
+1. 获取第一段加密JS和uid参数，解密后获得新的JS代码
+
+   ```python
+   s1 = re.findall('var x="(.*?)"', first_js)[0]
+   s2 = re.findall(',y="(.*?)"', first_js)[0]
+   second_js = self.wc_js.call('first_se_js', s1, s2)
+   ```
+
+2. 新的JS运行后生成的`__jsl_clearance`参数和第一步的uid，请求得到JSESSIONID
+
+   这一步关键的两个参数是`__jsluid_h`和`__jsl_clearance`
+
+   在cookie中携带请求，可获得JSESSIONID
+
+   ```python
+   headers['Cookie'] = '__jsluid_h=__jsluid_h; __jsl_clearance=__jsl_clearance;'
+   ```
+
+3. 此时我们有了三个关键参数，携带三个参数去请求验证码接口，获取新的JSESSIONID
+
+   ```python
+   headers['Cookie'] = '__jsluid_h=__jsluid_h; __jsl_clearance=jsl_clearance;JSESSIONID=JSESSIONID'
+   ```
+
+4. 这时候有了所有的参数了，再携带访问jiyan的接口，即可获得`gt`和`challenge`
+
+5. 根据`gt`和`challenge`分析验证码类型，进行相应的处理，这里可以通过统一接口进行处理，也可以对`滑动验证`、`点选验证码`进行分别处理（这部分内容涉及比较广，新手可以使用打码平台，或者像我一样通过机器学习，产生`轨迹数据`以及训练`识别模型`）
\ No newline at end of file
diff --git a/doc/jiyan_gt_challenge_demo.ipynb b/doc/jiyan_gt_challenge_demo.ipynb
new file mode 100644
index 0000000..b1ddfda
--- /dev/null
+++ b/doc/jiyan_gt_challenge_demo.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用企业公示网做例子\n",
+    "\n",
+    "获取gt和challenge两个参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 企业公示网做例子\n",
+    "index_url = 'http://www.gsxt.gov.cn/index.html'\n",
+    "index_header= {\n",
+    "            'accept': \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3\",\n",
+    "            'accept-encoding': \"gzip, deflate\",\n",
+    "            'accept-language': \"zh-CN,zh;q=0.9\",\n",
+    "            'connection': \"keep-alive\",\n",
+    "            'host': \"www.gsxt.gov.cn\",\n",
+    "            'upgrade-insecure-requests': \"1\",\n",
+    "            'Referer': 'http://www.gsxt.gov.cn/index.html',\n",
+    "            \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36\",\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 代理手动配置，避免本地的IP被封了\n",
+    "\n",
+    "# 代理服务器\n",
+    "proxyHost = \"forward.apeyun.com\"\n",
+    "proxyPort = \"9082\"\n",
+    "\n",
+    "# 代理隧道验证信息\n",
+    "proxyUser = \"2020070100845351047\"\n",
+    "proxyPass = \"G8oyqzh6zswpSMtk\"\n",
+    "\n",
+    "proxyMeta = \"http://%(user)s:%(pass)s@%(host)s:%(port)s\" % {\n",
+    "  \"host\" : proxyHost,\n",
+    "  \"port\" : proxyPort,\n",
+    "  \"user\" : proxyUser,\n",
+    "  \"pass\" : proxyPass,\n",
+    "}\n",
+    "\n",
+    "proxy = {\n",
+    "    \"http\"  : \"\",\n",
+    "    \"https\" : \"\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(index_url, headers=index_header, proxies=proxy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取到第一段JS代码"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<script>var x=\"String@challenge@@charAt@@@innerHTML@Array@@Expires@07@document@3D@https@@false@RHu@@@return@for@@toString@toLowerCase@@reverse@captcha@@11@@@@Thu@href@replace@@RegExp@@@@@8@1500@onreadystatechange@search@@join@22@window@r@@length@pathname@createElement@@try@@0@@GMT@addEventListener@attachEvent@@47V@while@if@a@JgSe0upZ@@@@firstChild@@else@OXM@chars@0xEDB88320@998@Path@APPlY@1593670282@function@__jsl_clearance@0xFF@@split@DOMContentLoaded@var@1@36@@2@@@@f@Jul@@@charCodeAt@e@setTimeout@20@div@parseInt@3@location@@g@@rOm9XFMtA3QKV7nYsPGT4lifyWwkq5vcjH2IdxUoCbhERLaz81DNB6@d@@@cookie@match@02@DX@substr@@eval@new@@@@catch@4@fromCharCode\".replace(/@*$/,\"\").split(\"@\"),y=\"2a 36=24(){2o(\\'2t.y=2t.1e+2t.16.z(/[\\\\\\\\?|&]r-2/,\\\\\\\\\\'\\\\\\\\\\')\\',14);c.2B=\\'25=23.20|1j|\\'+(24(){2a w=[24(36){k 36},24(w){k w},(24(){2a 36=c.1f(\\'2q\\');36.7=\\'<1s y=\\\\\\\\\\'/\\\\\\\\\\'>1u</1s>\\';36=36.1x.y;2a w=36.2C(/e?:\\\\\\\\/\\\\\\\\//)[1j];36=36.32(w.1d).o();k 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+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.text    # 获取第一段js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取jsluid参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'697b3a8c209b3fcfafa830c6bc637392'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jsluid = response.headers['Set-Cookie'].split('=')[1].split(';')[0]    \n",
+    "jsluid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "解密出第二段js代码"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"var _36=function(){setTimeout('location.href=location.pathname+location.search.replace(/[\\\\\\\\?|&]captcha-challenge/,\\\\\\\\'\\\\\\\\')',1500);document.cookie='__jsl_clearance=1593670282.998|0|'+(function(){var _w=[function(_36){return _36},function(_w){return _w},(function(){var _36=document.createElement('div');_36.innerHTML='<a href=\\\\\\\\'/\\\\\\\\'>_1u</a>';_36=_36.firstChild.href;var _w=_36.match(/https?:\\\\\\\\/\\\\\\\\//)[0];_36=_36.substr(_w.length).toLowerCase();return function(_w){for(var _1u=0;_1u<_w.length;_1u++){_w[_1u]=_36.charAt(_w[_1u])};return _w.join('')}})(),function(_36){for(var _w=0;_w<_36.length;_w++){_36[_w]=parseInt(_36[_w]).toString(36)};return _36.join('')}],_36=['DX',[(-~[2]+[])+(([-~-~{}]+(+[])>>-~-~{})+[]+[[]][0])],[(-~~~''+[]+[])+(-~[2]+[])],'r',[(-~~~''+[]+[])+(-~[2]+[])],'47V',[-~![]-~[((+!(+!{}))<<(+!(+!{})))-~{}-~(-~[-~[]-~[]])]],[(4+[]+[]),(-~[2]+4+[[]][0])],[(-~[2]+[])+(~~[]+[]+[]),(-~[2]+[])+(4+[]+[])],'OXM',[(-~~~''+[]+[])+(-~~~''+[]+[])],'RHu',[(-~~~''+[]+[])+(4+[]+[])],'APPlY%3D'];for(var _1u=0;_1u<_36.length;_1u++){_36[_1u]=_w[[1,3,2,1,3,1,0,2,3,1,2,1,2,1][_1u]](_36[_1u])};return _36.join('')})()+';Expires=Thu, 02-Jul-20 07:11:22 GMT;Path=/;'};if((function(){try{return !!window.addEventListener;}catch(e){return false;}})()){document.addEventListener('DOMContentLoaded',_36,false)}else{document.attachEvent('onreadystatechange',_36)}\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import execjs\n",
+    "\n",
+    "\n",
+    "des_js = 'function get_z(x, y) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    };\\n    x = x.replace(/@*$/, \"\").split(\"@\");\\n    z = f(y.match(/\\\\w/g).sort(function (x, y) {\\n        return f(x) - f(y)\\n    }).pop());\\n    return [x, y, z]\\n\\n}\\n\\nfunction get_js(x, y, z) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    };\\n    x = eval(x);\\n    z = z + 1;\\n    p = y.replace(/\\\\b\\\\w+\\\\b/g,\\n        function (y) {\\n            return x[f(y, z) - 1] || (\"_\" + y)\\n        });\\n    return p\\n\\n}\\n\\n\\nfunction once_js(x, y) {\\n    f = function (x, y) {\\n        var a = 0,\\n            b = 0,\\n            c = 0;\\n        x = x.split(\"\");\\n        y = y || 99;\\n        while ((a = x.shift()) && (b = a.charCodeAt(0) - 77.5)) c = (Math.abs(b) < 13 ? (b + 48.5) : parseInt(a, 36)) + y * c;\\n        return c\\n    },\\n        x = x.replace(/@*$/, \"\").split(\"@\");\\n    z = f(y.match(/\\\\w/g).sort(function (x, y) {\\n        return f(x) - f(y)\\n    }).pop());\\n    while (z++) try {\\n        g = y.replace(/\\\\b\\\\w+\\\\b/g,\\n            function (y) {\\n                return x[f(y, z) - 1] || (\"_\" + y)\\n            });\\n        return g\\n    } catch (_) {\\n    }\\n}'\n",
+    "des_js = execjs.compile(des_js)\n",
+    "first_js = response.text\n",
+    "x = re.findall('var x=\"(.*?)\"', first_js)[0]\n",
+    "y = re.findall(',y=\"(.*?)\"', first_js)[0]\n",
+    "second_js = des_js.call('once_js', x, y)\n",
+    "second_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "处理第二段js，去除document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cookie=\\'__jsl_clearance=1593670282.998|0|\\'+(function(){var _w=[function(_36){return _36},function(_w){return _w},(function(){var _36=\"www.gsxt.gov.cn/index.html\";var _w=\"http://\";return function(_w){for(var _1u=0;_1u<_w.length;_1u++){_w[_1u]=_36.charAt(_w[_1u])};return _w.join(\\'\\')}})(),function(_36){for(var _w=0;_w<_36.length;_w++){_36[_w]=parseInt(_36[_w]).toString(36)};return _36.join(\\'\\')}],_36=[\\'DX\\',[(-~[2]+[])+(([-~-~{}]+(+[])>>-~-~{})+[]+[[]][0])],[(-~~~\\'\\'+[]+[])+(-~[2]+[])],\\'r\\',[(-~~~\\'\\'+[]+[])+(-~[2]+[])],\\'47V\\',[-~![]-~[((+!(+!{}))<<(+!(+!{})))-~{}-~(-~[-~[]-~[]])]],[(4+[]+[]),(-~[2]+4+[[]][0])],[(-~[2]+[])+(~~[]+[]+[]),(-~[2]+[])+(4+[]+[])],\\'OXM\\',[(-~~~\\'\\'+[]+[])+(-~~~\\'\\'+[]+[])],\\'RHu\\',[(-~~~\\'\\'+[]+[])+(4+[]+[])],\\'APPlY%3D\\'];for(var _1u=0;_1u<_36.length;_1u++){_36[_1u]=_w[[1,3,2,1,3,1,0,2,3,1,2,1,2,1][_1u]](_36[_1u])};return _36.join(\\'\\')})()+\\';Expires=Thu, 02-Jul-20 07:11:22 \\''"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_js = second_js.replace('\\\\\\\\', '\\\\')\n",
+    "\n",
+    "new_js = 'cookie' + new_js.split('document.cookie')[1]\n",
+    "new_js = new_js.split('GMT;Path=/;')[0] + \"'\"\n",
+    "\n",
+    "if re.findall(\"(var .{0,5}=)document\\.createElement\\('div'\\);\", new_js):\n",
+    "        _3d = re.findall(\"(var .{0,5}=)document\\.createElement\\('div'\\);\", new_js)\n",
+    "        _2b = re.findall(\"(var .{0,5}=).{0,5}\\.match\\(/https\\?:\\\\\\/\\\\\\//\\)\\[0\\];\", new_js)\n",
+    "        new_js = re.sub(\n",
+    "            \"var .{0,5}=document\\.createElement\\('div'\\);\", \n",
+    "            _3d[0] + f'\"{index_url.replace(\"http://\", \"\")}\";',\n",
+    "            new_js\n",
+    "        )\n",
+    "        new_js = re.sub(\"_.{0,5}\\.innerHTML='<a href=.{0,25}</a>';\", \"\", new_js)\n",
+    "        new_js = re.sub(\"_.{0,5}=.{0,5}\\.firstChild\\.href;\", \"\", new_js)\n",
+    "        new_js = re.sub(\"var .{0,5}=.{0,5}\\.match\\(/https\\?:\\\\\\/\\\\\\//\\)\\[0\\];\", _2b[0] + '\"http://\";', new_js)\n",
+    "        new_js = re.sub(\"_.{0,5}=.{0,5}\\.substr\\(.{0,5}\\.length\\)\\.toLowerCase\\(\\);\", \"\", new_js)\n",
+    "new_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "处理js，去除Window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find = re.findall(\"!*window\\[.*?\\]\", new_js)\n",
+    "if find:\n",
+    "    for f in find:\n",
+    "        if '!' in f:\n",
+    "            if len(re.findall('!', f)) % 2 == 0:\n",
+    "                new_js = new_js.replace(f, 'false')\n",
+    "            else:\n",
+    "                new_js = new_js.replace(f, 'true')\n",
+    "        else:\n",
+    "            new_js = new_js.replace(f, 'undefined')\n",
+    "new_js = new_js.replace('window.headless', 'undefined')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cookie=\\'__jsl_clearance=1593670282.998|0|\\'+(function(){var _w=[function(_36){return _36},function(_w){return _w},(function(){var _36=\"www.gsxt.gov.cn/index.html\";var _w=\"http://\";return function(_w){for(var _1u=0;_1u<_w.length;_1u++){_w[_1u]=_36.charAt(_w[_1u])};return _w.join(\\'\\')}})(),function(_36){for(var _w=0;_w<_36.length;_w++){_36[_w]=parseInt(_36[_w]).toString(36)};return _36.join(\\'\\')}],_36=[\\'DX\\',[(-~[2]+[])+(([-~-~{}]+(+[])>>-~-~{})+[]+[[]][0])],[(-~~~\\'\\'+[]+[])+(-~[2]+[])],\\'r\\',[(-~~~\\'\\'+[]+[])+(-~[2]+[])],\\'47V\\',[-~![]-~[((+!(+!{}))<<(+!(+!{})))-~{}-~(-~[-~[]-~[]])]],[(4+[]+[]),(-~[2]+4+[[]][0])],[(-~[2]+[])+(~~[]+[]+[]),(-~[2]+[])+(4+[]+[])],\\'OXM\\',[(-~~~\\'\\'+[]+[])+(-~~~\\'\\'+[]+[])],\\'RHu\\',[(-~~~\\'\\'+[]+[])+(4+[]+[])],\\'APPlY%3D\\'];for(var _1u=0;_1u<_36.length;_1u++){_36[_1u]=_w[[1,3,2,1,3,1,0,2,3,1,2,1,2,1][_1u]](_36[_1u])};return _36.join(\\'\\')})()+\\';Expires=Thu, 02-Jul-20 07:11:22 \\''"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取jsl_clearance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1593670282.998|0|DXzcrd47V9gtuyOXMvRHunAPPlY%3D'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "js = \"\"\"\n",
+    "        function getJS() {\n",
+    "        %s\n",
+    "        return cookie\n",
+    "        }\n",
+    "    \"\"\" % new_js\n",
+    "exec_js = execjs.compile(js)\n",
+    "jsl = exec_js.call('getJS')\n",
+    "jsl = jsl.split(';')[0]\n",
+    "jsl_clearance = jsl.split('=')[1]\n",
+    "jsl_clearance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'e1d92a59a5b23981cbfa00048eea'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 获取第一个jessionid\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};'\n",
+    "response2 = requests.get(index_url, headers=headers, proxies=proxy)\n",
+    "JSESSIONID1 = re.findall('JSESSIONID=(.*?);', response2.headers['Set-Cookie'])[0]\n",
+    "JSESSIONID1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'e1d92a59a5b23981cbfa00048eea'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 获取第二个jessionid\n",
+    "import time\n",
+    "\n",
+    "url = 'http://www.gsxt.gov.cn/SearchItemCaptcha?t=' + str(int(time.time()*1000))\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID1}'\n",
+    "response3 = requests.get(url, headers=headers, proxies=proxy)\n",
+    "JSESSIONID2 = re.findall('JSESSIONID=(.*?);', response3.headers['Set-Cookie'])[0]\n",
+    "JSESSIONID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试cookie，如果状态码是200，获取gt和challenge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "headers = index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID2}'\n",
+    "url = 'http://www.gsxt.gov.cn/SearchItemCaptcha?t=' + str(int(time.time()*1000))\n",
+    "response4 = requests.get(url, headers=headers, proxies=proxy)\n",
+    "response4.status_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gt_challenge_json = json.loads(response4.text)\n",
+    "gt = gt_challenge_json['gt']\n",
+    "challenge = gt_challenge_json['challenge']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'62756445cd524543f5a16418cd920ffd'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'bc7be94649e930d1f8da98772e82060f'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "challenge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 下面开始搜索企业信息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 如果您看到这里，请给我一个star，谢谢，如有遗漏，请留言指正，感激！"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里先走一遍采集流程，本地打码服务的实现稍后整理，要先整理完模型训练部分才能打码\n",
+    "服务实现流程：\n",
+    "1. 拿到gt和challenge获取验证码，验证码有两种\n",
+    "    - 滑动3.0\n",
+    "    - 文字点选，这部分的训练模型已经开源了，移步首页\n",
+    "2. 破解验证码，获取validate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 本地打码服务器，所有验证码类型在这里统一处理，返回validate即可\n",
+    "get_valid_server = 'http://49.235.209.176:5050'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "JSONDecodeError",
+     "evalue": "Expecting value: line 1 column 1 (char 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-19-b28b7f0729d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/opt/anaconda3/envs/blog/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mjson\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    896\u001b[0m                     \u001b[0;31m# used.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcomplexjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/anaconda3/envs/blog/lib/python3.7/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    346\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/anaconda3/envs/blog/lib/python3.7/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/anaconda3/envs/blog/lib/python3.7/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
+     ]
+    }
+   ],
+   "source": [
+    "# url = f'{get_valid_server}/geetest?gt={gt}&challenge={challenge}'\n",
+    "# resp = requests.get(url)\n",
+    "# resp\n",
+    "\n",
+    "data = {\n",
+    "    'gt': gt,\n",
+    "    'challenge': challenge\n",
+    "}\n",
+    "url = 'http://3215952j9f.qicp.vip:10682/captcha'\n",
+    "\n",
+    "resp = requests.post(url, data=json.dumps(data))\n",
+    "print(resp.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_resu = json.loads(resp.text)\n",
+    "\n",
+    "# def get_validate_wander(challenge,gt, vtype=1):\n",
+    "#     data = {\n",
+    "#         'gt':gt,\n",
+    "#         'challenge':challenge\n",
+    "#     }\n",
+    "#     url='http://49.235.179.162:5000/geetest'\n",
+    "#     response = requests.post(url=url,data=json.dumps(data))\n",
+    "#     return response.json()\n",
+    "\n",
+    "# val_resu = get_validate_wander(challenge=challenge, gt=gt)\n",
+    "val_resu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "成功获取validate，开始进行搜索"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lxml.etree import _Element\n",
+    "from lxml import etree\n",
+    "\n",
+    "def parse_list(content):\n",
+    "    def try_catch(data, patten):\n",
+    "        if isinstance(data, _Element):\n",
+    "            try:\n",
+    "                target = re.sub('\\s', '', data.xpath(patten)[0].strip())\n",
+    "            except:\n",
+    "                target = None\n",
+    "            return target\n",
+    "        return data\n",
+    "    a_list = []\n",
+    "    content_list = content\n",
+    "    html = etree.HTML(content_list)\n",
+    "    list_len = int(html.xpath('//span[@class=\"search_result_span1\"]/text()')[0])\n",
+    "    if list_len == 0:\n",
+    "        return 'None'\n",
+    "    else:\n",
+    "        targit_len = 1\n",
+    "    for i in range(1, targit_len + 1):\n",
+    "        try:\n",
+    "            url = html.xpath('//div[@class=\"main-layout fw f14\"]/div[2]/a[' + str(i) + ']/@href')[0]\n",
+    "            href = 'http://www.gsxt.gov.cn{}'.format(url)\n",
+    "        except:\n",
+    "            href = None\n",
+    "        dic = {\n",
+    "            'href': href\n",
+    "        }\n",
+    "        a_list.append(dic)\n",
+    "    return a_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def list_page(val_resu, jsluid, jsl_clearance, JSESSIONID, search_word):\n",
+    "    time.sleep(1)\n",
+    "    validate = val_resu['data']['validate']\n",
+    "    chall = val_resu['data']['challenge']\n",
+    "    data = {\n",
+    "        'tab': 'ent_tab',\n",
+    "        'province': '',\n",
+    "        'geetest_challenge': chall,\n",
+    "        'geetest_validate': validate,\n",
+    "        'geetest_seccode': '{}|jordan'.format(validate),\n",
+    "        'token': '57571798',\n",
+    "        'searchword': f'{search_word}',\n",
+    "    }\n",
+    "    time.sleep(5)\n",
+    "    headers =index_header.copy()\n",
+    "    headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID}'\n",
+    "    post_url = 'http://www.gsxt.gov.cn/corp-query-search-1.html'\n",
+    "    content = requests.post(url=post_url, data=data, headers=headers, verify=False, timeout=(60, 60), proxies=proxy)\n",
+    "    if content.status_code == 200:\n",
+    "        if '您的IP地址' in content.text:\n",
+    "            print('请更换代理')\n",
+    "        else:\n",
+    "            return content.text\n",
+    "    else:\n",
+    "        print('cookies失效')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "配置要搜索的关键词"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_word = \"百度\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if val_resu['data']['result'] == 'fail':\n",
+    "    print('get valid error')\n",
+    "else:\n",
+    "    content_text = list_page(val_resu, jsluid, jsl_clearance, JSESSIONID2, search_word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "href = parse_list(content_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "href"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "headers =index_header.copy()\n",
+    "headers['Cookie'] = f'__jsluid_h={jsluid}; __jsl_clearance={jsl_clearance};JSESSIONID={JSESSIONID2}'\n",
+    "detail = requests.get(url=href[0]['href'], headers = headers, proxies = proxy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "输出结果正确"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etree.HTML(detail.text).xpath(\"//h1\")[0].text.strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 如果您看到这里，请给我一个star，谢谢，如有遗漏，请留言指正，感激！"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "blog",
+   "language": "python",
+   "name": "blog"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/doc/\344\271\235\345\256\253\346\240\274\345\233\276\347\211\207\351\252\214\350\257\201\347\240\201\350\256\255\347\273\203\351\233\206.md" "b/doc/\344\271\235\345\256\253\346\240\274\345\233\276\347\211\207\351\252\214\350\257\201\347\240\201\350\256\255\347\273\203\351\233\206.md"
new file mode 100644
index 0000000..2f14d6a
--- /dev/null
+++ "b/doc/\344\271\235\345\256\253\346\240\274\345\233\276\347\211\207\351\252\214\350\257\201\347\240\201\350\256\255\347\273\203\351\233\206.md"
@@ -0,0 +1,98 @@
+# 九宫格图片验证码训练集
+
+这篇主要想记录一下yolo训练数据集的准备过程，毕竟数据才是价值所在。
+
+## yolo训练集文件读取逻辑
+
+下面说的内容都是基于`darknet`，已测试。
+
+关于数据集准备过程我们之前说过，重复的内容就不说了([定位模型训练手册](./detector_train_handbook.md))，这里主要解决一下文件标注命名的问题，以及如何避免训练时遇到`Too many or too few labels`问题，这个问题的根源就是文件标签没有按yolo的规则整理好。
+
+这里简单梳理一下逻辑，方便理解我们要怎么给训练集里的数据命名。
+
+首先回忆一下我们训练分类器需要准备哪几个文件：
+
+1. xx.data：定义模型使用到的所有文件路径
+
+   ```t x t
+   classes = 144
+   train = data/nine_train.txt
+   labels = data/nine.labels
+   backup = backup/
+   top=1
+   ```
+
+2. xx.labels：所有分类的标签名称
+
+   ```t x t
+   斧头
+   螃蟹
+   沙发
+   帽子
+   老虎
+   气球
+   ...
+   ```
+
+3. xx.txt：所有训练集文件的绝对路径
+
+   ```txt
+   /Users/train_file/nine/笔记本电脑_2dae8897ecb032a7daac908c31c596562.jpg
+   /Users/train_file/nine/笔记本电脑_2dc1ad266d264d40bab65095a5ef86157.jpg
+   /Users/train_file/nine/笔记本电脑_2dd25a294a4c7157f02aa92724d78ef31.jpg
+   /Users/train_file/nine/笔记本电脑_2df0b329e546630547a4f1aa816d03ee4.jpg
+   ...
+   ```
+
+假设我们现在有一批训练数据是这样的：
+
+```text
+/Users/train_file/nine/笔记本电脑_2dae8897ecb032a7daac908c31c596562.jpg
+/Users/train_file/nine/笔记本电脑_2dc1ad266d264d40bab65095a5ef86157.jpg
+/Users/train_file/nine/笔记本电脑_2dd25a294a4c7157f02aa92724d78ef31.jpg
+/Users/train_file/nine/笔记本电脑_2df0b329e546630547a4f1aa816d03ee4.jpg
+...
+```
+
+在训练图片分类器时，图片的文件名要包含标注的标签信息，比如：
+
+`/Users/train_file/nine/笔记本电脑_2dae8897ecb032a7daac908c31c596562.jpg`，这个图片文件
+
+文件名分为三个部分
+
+- 2dae8897ecb032a7daac908c31c596562：随机标签，用来防止文件名重复，用随机字符串都可以
+- 笔记本电脑：标注的图片内容
+- Jpg：文件格式
+
+**这里要注意：yolo读取的是文件绝对路径，在整个路径中不能包含多个标签**
+
+什么意思呢？举个例子：
+
+- 在`/Users/train_file/nine/笔记本电脑_2dae8897ecb032a7daac908c31c596562.jpg`这个文件路径中，`笔记本电脑`是我们要分类的标签，因此不能用这种路径`/Users/train_file/nine/笔记本电脑/笔记本电脑_2dae8897ecb032a7daac908c31c596562.jpg`，这样的话yolo会在路径中读取到两个`笔记本电脑`标签，会提示`Too many or too few labels 2`；
+
+**分类的标签不能存在包含与被包含的关系**
+
+举个例子：
+
+- 我们当前的分类任务要分类这些标签：
+
+  ```txt
+  斧头
+  螃蟹
+  沙发
+  鳄鱼
+  ...
+  ```
+
+  但是如果我们现在要加一个分类，叫做`鱼`，此时就变成了：
+
+  ```txt
+  斧头
+  螃蟹
+  沙发
+  鳄鱼
+  鱼
+  ...
+  ```
+
+  这时候`鳄鱼`和`鱼`就组成了被包含的关系，也会报错。
\ No newline at end of file
diff --git "a/doc/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213.ipynb" "b/doc/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213.ipynb"
new file mode 100644
index 0000000..af73677
--- /dev/null
+++ "b/doc/\345\234\250Python\344\270\255\344\275\277\347\224\250\346\250\241\345\236\213.ipynb"
@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 在Python中使用我们训练好点模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "前面我们实现了`汉字位置检测`和`汉字分类识别`模型，实际使用肯定要结合封装成接口来使用，会比较方便。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在darknet的官方项目中，已经为我们提供了一个demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ctypes import *\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "def sample(probs):\n",
+    "    s = sum(probs)\n",
+    "    probs = [a/s for a in probs]\n",
+    "    r = random.uniform(0, 1)\n",
+    "    for i in range(len(probs)):\n",
+    "        r = r - probs[i]\n",
+    "        if r <= 0:\n",
+    "            return i\n",
+    "    return len(probs)-1\n",
+    "\n",
+    "def c_array(ctype, values):\n",
+    "    arr = (ctype*len(values))()\n",
+    "    arr[:] = values\n",
+    "    return arr\n",
+    "\n",
+    "class BOX(Structure):\n",
+    "    _fields_ = [(\"x\", c_float),\n",
+    "                (\"y\", c_float),\n",
+    "                (\"w\", c_float),\n",
+    "                (\"h\", c_float)]\n",
+    "\n",
+    "class DETECTION(Structure):\n",
+    "    _fields_ = [(\"bbox\", BOX),\n",
+    "                (\"classes\", c_int),\n",
+    "                (\"prob\", POINTER(c_float)),\n",
+    "                (\"mask\", POINTER(c_float)),\n",
+    "                (\"objectness\", c_float),\n",
+    "                (\"sort_class\", c_int)]\n",
+    "\n",
+    "\n",
+    "class IMAGE(Structure):\n",
+    "    _fields_ = [(\"w\", c_int),\n",
+    "                (\"h\", c_int),\n",
+    "                (\"c\", c_int),\n",
+    "                (\"data\", POINTER(c_float))]\n",
+    "\n",
+    "class METADATA(Structure):\n",
+    "    _fields_ = [(\"classes\", c_int),\n",
+    "                (\"names\", POINTER(c_char_p))]\n",
+    "\n",
+    "    \n",
+    "\n",
+    "lib = CDLL(\"libdarknet.so\", RTLD_GLOBAL) # 配置so文件路径，最好是绝对路径\n",
+    "lib.network_width.argtypes = [c_void_p]\n",
+    "lib.network_width.restype = c_int\n",
+    "lib.network_height.argtypes = [c_void_p]\n",
+    "lib.network_height.restype = c_int\n",
+    "\n",
+    "predict = lib.network_predict\n",
+    "predict.argtypes = [c_void_p, POINTER(c_float)]\n",
+    "predict.restype = POINTER(c_float)\n",
+    "\n",
+    "set_gpu = lib.cuda_set_device\n",
+    "set_gpu.argtypes = [c_int]\n",
+    "\n",
+    "make_image = lib.make_image\n",
+    "make_image.argtypes = [c_int, c_int, c_int]\n",
+    "make_image.restype = IMAGE\n",
+    "\n",
+    "get_network_boxes = lib.get_network_boxes\n",
+    "get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]\n",
+    "get_network_boxes.restype = POINTER(DETECTION)\n",
+    "\n",
+    "make_network_boxes = lib.make_network_boxes\n",
+    "make_network_boxes.argtypes = [c_void_p]\n",
+    "make_network_boxes.restype = POINTER(DETECTION)\n",
+    "\n",
+    "free_detections = lib.free_detections\n",
+    "free_detections.argtypes = [POINTER(DETECTION), c_int]\n",
+    "\n",
+    "free_ptrs = lib.free_ptrs\n",
+    "free_ptrs.argtypes = [POINTER(c_void_p), c_int]\n",
+    "\n",
+    "network_predict = lib.network_predict\n",
+    "network_predict.argtypes = [c_void_p, POINTER(c_float)]\n",
+    "\n",
+    "reset_rnn = lib.reset_rnn\n",
+    "reset_rnn.argtypes = [c_void_p]\n",
+    "\n",
+    "load_net = lib.load_network\n",
+    "load_net.argtypes = [c_char_p, c_char_p, c_int]\n",
+    "load_net.restype = c_void_p\n",
+    "\n",
+    "do_nms_obj = lib.do_nms_obj\n",
+    "do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]\n",
+    "\n",
+    "do_nms_sort = lib.do_nms_sort\n",
+    "do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]\n",
+    "\n",
+    "free_image = lib.free_image\n",
+    "free_image.argtypes = [IMAGE]\n",
+    "\n",
+    "letterbox_image = lib.letterbox_image\n",
+    "letterbox_image.argtypes = [IMAGE, c_int, c_int]\n",
+    "letterbox_image.restype = IMAGE\n",
+    "\n",
+    "load_meta = lib.get_metadata\n",
+    "lib.get_metadata.argtypes = [c_char_p]\n",
+    "lib.get_metadata.restype = METADATA\n",
+    "\n",
+    "load_image = lib.load_image_color\n",
+    "load_image.argtypes = [c_char_p, c_int, c_int]\n",
+    "load_image.restype = IMAGE\n",
+    "\n",
+    "rgbgr_image = lib.rgbgr_image\n",
+    "rgbgr_image.argtypes = [IMAGE]\n",
+    "\n",
+    "predict_image = lib.network_predict_image\n",
+    "predict_image.argtypes = [c_void_p, IMAGE]\n",
+    "predict_image.restype = POINTER(c_float)\n",
+    "\n",
+    "def classify(net, meta, im):\n",
+    "    out = predict_image(net, im)\n",
+    "    res = []\n",
+    "    for i in range(meta.classes):\n",
+    "        res.append((meta.names[i], out[i]))\n",
+    "    res = sorted(res, key=lambda x: -x[1])\n",
+    "    return res\n",
+    "\n",
+    "def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):\n",
+    "    im = load_image(image, 0, 0)\n",
+    "    num = c_int(0)\n",
+    "    pnum = pointer(num)\n",
+    "    predict_image(net, im)\n",
+    "    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)\n",
+    "    num = pnum[0]\n",
+    "    if (nms): do_nms_obj(dets, num, meta.classes, nms);\n",
+    "\n",
+    "    res = []\n",
+    "    for j in range(num):\n",
+    "        for i in range(meta.classes):\n",
+    "            if dets[j].prob[i] > 0:\n",
+    "                b = dets[j].bbox\n",
+    "                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))\n",
+    "    res = sorted(res, key=lambda x: -x[1])\n",
+    "    free_image(im)\n",
+    "    free_detections(dets, num)\n",
+    "    return res\n",
+    "\n",
+    "def run():\n",
+    "    net = load_net(\n",
+    "        \"\".encode(), # cfg文件路径\n",
+    "        \"\".encode(), # weights权重文件路径\n",
+    "        0  # 默认0\n",
+    "    )\n",
+    "    meta = load_meta(\"\".encode()) # data文件路径\n",
+    "    # 执行预测\n",
+    "    r = detect(\n",
+    "        net, \n",
+    "        meta, \n",
+    "        \"\".encode()\n",
+    "    )\n",
+    "    print(r)\n",
+    "    \n",
+    "    \n",
+    "if __name__ == \"__main__\":\n",
+    "    run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里几个要注意的点：\n",
+    "\n",
+    "- 文件路径编码\n",
+    "- libdarknet.so 文件的路径配置"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "blog",
+   "language": "python",
+   "name": "blog"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hanzi_detection/.gitignore b/hanzi_detection/.gitignore
new file mode 100755
index 0000000..bea19ff
--- /dev/null
+++ b/hanzi_detection/.gitignore
@@ -0,0 +1,27 @@
+*.o
+*.dSYM
+*.csv
+*.out
+*.png
+*.jpg
+*.pyc
+old/
+mnist/
+data/
+caffe/
+grasp/
+images/
+opencv/
+convnet/
+decaf/
+submission/
+cfg/
+darknet
+.fuse*
+
+# OS Generated #
+.DS_Store*
+ehthumbs.db
+Icon?
+Thumbs.db
+*.swp
diff --git a/hanzi_detection/LICENSE b/hanzi_detection/LICENSE
new file mode 100755
index 0000000..a50f7d7
--- /dev/null
+++ b/hanzi_detection/LICENSE
@@ -0,0 +1,12 @@
+                                  YOLO LICENSE
+                             Version 2, July 29 2016
+
+THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER
+SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN
+TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES
+LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S
+THE REAL LICENSE:
+
+0. Darknet is public domain.
+1. Do whatever you want with it.
+2. Stop emailing me about it!
diff --git a/hanzi_detection/LICENSE.fuck b/hanzi_detection/LICENSE.fuck
new file mode 100755
index 0000000..8b1a9d8
--- /dev/null
+++ b/hanzi_detection/LICENSE.fuck
@@ -0,0 +1,13 @@
+           DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
+                   Version 2, December 2004
+
+Copyright (C) 2004 Sam Hocevar <sam@hocevar.net>
+
+Everyone is permitted to copy and distribute verbatim or modified
+copies of this license document, and changing it is allowed as long
+as the name is changed.
+
+           DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
+  TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+ 0. You just DO WHAT THE FUCK YOU WANT TO.
diff --git a/hanzi_detection/LICENSE.gen b/hanzi_detection/LICENSE.gen
new file mode 100755
index 0000000..c541132
--- /dev/null
+++ b/hanzi_detection/LICENSE.gen
@@ -0,0 +1,91 @@
+RNN LICENSE Version 3, June 21 2017
+
+Copyright (c) 1990, 1989, 1999 Free87337 May 48 THIRD PARTIES OR ANY OTHER THE
+COMPLAIN OR CONSEQUENTIAL DAMAGES AND REGARDLESS OF WHETHER IN CONTRACT, TO THE
+EXTENT REPAIR OR AGENTS (NOT THE IN ANY EVENT). THE SOFTWARE WILL BE
+UNINTERRUPTED OR ERROR-FREE OR ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF ALL THE WORK (GOVERNED CODE) HIM RESPONSES, OR OF FINES,
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+SOFTWARE OR THE USE OR ANYTHING PROVIDED IN THIS PRODUCT, COMMIS AND SERVICES
+ARE LICENSED SOFTWARE AND ANY RESULE OR ANY OTHER THE COPYRIGHT HOLDERS BE
+LIABLE FOR ANY SPECIAL, INCIDENTAL, CASE, SUCH WARRANTIES, EXPRESS OR IMPLIED,
+INCLUDING, WITHOUT LIMITATION, WARRANTIES THAT THE COPYRIGHT HOLDERS AND/OR ANY
+PERSON FOR ANY INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY
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+
+6\. TERMINATION. TO THE EXTENT PERMITTED BY LAW, NO USE OF THE COVERED CODE IS
+WITH YOU. SHOULD ANY COVERED CODE PROVE DEFECTIVE IN ANY RESPECT, YOU (NOT THE
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+
+
+This paragraph Agreement constitutes the entire agreement between the parties
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diff --git a/hanzi_detection/LICENSE.gpl b/hanzi_detection/LICENSE.gpl
new file mode 100755
index 0000000..9cecc1d
--- /dev/null
+++ b/hanzi_detection/LICENSE.gpl
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
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+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
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+  12. No Surrender of Others' Freedom.
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+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
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+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+
+Also add information on how to contact you by electronic and paper mail.
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+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
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+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/hanzi_detection/LICENSE.meta b/hanzi_detection/LICENSE.meta
new file mode 100755
index 0000000..6728bd2
--- /dev/null
+++ b/hanzi_detection/LICENSE.meta
@@ -0,0 +1,8 @@
+                          META-LICENSE
+                    Version 1, June 21 2017
+
+Any and all licenses may be applied to the software either individually
+or in concert. Any issues, ambiguities, paradoxes, or metaphysical quandries
+arising from this combination should be discussed with a local faith leader,
+hermit, or guru. The Oxford comma shall be used.
+
diff --git a/hanzi_detection/LICENSE.mit b/hanzi_detection/LICENSE.mit
new file mode 100755
index 0000000..5bd806c
--- /dev/null
+++ b/hanzi_detection/LICENSE.mit
@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2017 Joseph Redmon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
diff --git a/hanzi_detection/LICENSE.v1 b/hanzi_detection/LICENSE.v1
new file mode 100755
index 0000000..5b8709a
--- /dev/null
+++ b/hanzi_detection/LICENSE.v1
@@ -0,0 +1,13 @@
+                                  YOLO LICENSE
+                             Version 1, July 10 2015
+
+THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER
+SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN
+TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES
+LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY SUBJECT TO
+THE FOLLOWING CONDITIONS:
+
+1. #yolo
+2. #swag
+3. #blazeit
+
diff --git a/hanzi_detection/Makefile b/hanzi_detection/Makefile
new file mode 100755
index 0000000..534dcab
--- /dev/null
+++ b/hanzi_detection/Makefile
@@ -0,0 +1,105 @@
+GPU=0
+CUDNN=0
+OPENCV=0
+OPENMP=0
+DEBUG=0
+
+ARCH= -gencode arch=compute_30,code=sm_30 \
+      -gencode arch=compute_35,code=sm_35 \
+      -gencode arch=compute_50,code=[sm_50,compute_50] \
+      -gencode arch=compute_52,code=[sm_52,compute_52]
+#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
+
+# This is what I use, uncomment if you know your arch and want to specify
+# ARCH= -gencode arch=compute_52,code=compute_52
+
+VPATH=./src/:./examples
+SLIB=libdarknet.so
+ALIB=libdarknet.a
+EXEC=darknet
+OBJDIR=./obj/
+
+CC=gcc
+CPP=g++
+NVCC=/usr/local/cuda-10.1/bin/nvcc 
+AR=ar
+ARFLAGS=rcs
+OPTS=-Ofast
+LDFLAGS= -lm -pthread 
+COMMON= -Iinclude/ -Isrc/
+CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
+
+ifeq ($(OPENMP), 1) 
+CFLAGS+= -fopenmp
+endif
+
+ifeq ($(DEBUG), 1) 
+OPTS=-O0 -g
+endif
+
+CFLAGS+=$(OPTS)
+
+ifeq ($(OPENCV), 1) 
+COMMON+= -DOPENCV
+CFLAGS+= -DOPENCV
+LDFLAGS+= `pkg-config --libs opencv` -lstdc++
+COMMON+= `pkg-config --cflags opencv` 
+endif
+
+ifeq ($(GPU), 1) 
+COMMON+= -DGPU -I/usr/local/cuda/include/
+CFLAGS+= -DGPU
+LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
+endif
+
+ifeq ($(CUDNN), 1) 
+COMMON+= -DCUDNN 
+CFLAGS+= -DCUDNN
+LDFLAGS+= -lcudnn
+endif
+
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o
+EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
+ifeq ($(GPU), 1) 
+LDFLAGS+= -lstdc++ 
+OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o
+endif
+
+EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
+OBJS = $(addprefix $(OBJDIR), $(OBJ))
+DEPS = $(wildcard src/*.h) Makefile include/darknet.h
+
+all: obj backup results $(SLIB) $(ALIB) $(EXEC)
+#all: obj  results $(SLIB) $(ALIB) $(EXEC)
+
+
+$(EXEC): $(EXECOBJ) $(ALIB)
+	$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)
+
+$(ALIB): $(OBJS)
+	$(AR) $(ARFLAGS) $@ $^
+
+$(SLIB): $(OBJS)
+	$(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)
+
+$(OBJDIR)%.o: %.cpp $(DEPS)
+	$(CPP) $(COMMON) $(CFLAGS) -c $< -o $@
+
+$(OBJDIR)%.o: %.c $(DEPS)
+	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
+
+$(OBJDIR)%.o: %.cu $(DEPS)
+	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
+
+obj:
+	mkdir -p obj
+backup:
+	mkdir -p backup
+results:
+	mkdir -p results
+
+.PHONY: clean
+
+clean:
+	rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*
+
diff --git a/hanzi_detection/README.md b/hanzi_detection/README.md
new file mode 100755
index 0000000..97d9bda
--- /dev/null
+++ b/hanzi_detection/README.md
@@ -0,0 +1,75 @@
+## 汉字位置检测模型文档
+
+模型我已经训练好了，直接可以用。使用的darknet，模型使用方法见官网。**有问题咨询或者需要训练数据的童鞋可以加首页的微信跟我要，Git传不了这么多文件。**
+
+> 现已更新QQ群，请看首页
+
+提示：backup/ 文件夹下缺少权重文件，不是我不想传，是大于100M的文件Git传不了。如果需要，微信跟我要吧。
+
+Git star 过500+，微信关注人数1000+会推出油管视频，谢谢支持！
+
+### 使用方法
+
+1. 编译
+
+   打开终端
+
+   进入项目文件夹：`cd hanzi_detection`
+
+   编译：`make`
+
+2. 测试
+
+   编译成功后执行：`./darknet detector test data/jiyan.data model/jiyan_yolov3.cfg backup/jiyan_yolov3_final.weights `
+
+   出现如下日志：
+
+   ```shell
+     100 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
+     101 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
+     102 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
+     103 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
+     104 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
+     105 conv     18  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x  18  0.053 BFLOPs
+     106 yolo
+   Loading weights from backup/jiyan_yolov3_final.weights...Done!
+   
+   ```
+
+   模型加载完成，输入一个图片地址进行测试：
+
+   ```shell
+   Enter Image Path: ./809859eb7dbf70338f6c0dfa7c510c3a.jpg: Predicted in 18.303259 seconds.
+   hanzi: 100%
+   hanzi: 100%
+   hanzi: 100%
+   hanzi: 99%
+   ```
+
+   **识别结果会保存到当前文件夹下，名叫：predictions.jpg**
+
+   可以看到识别这个结果用了18.3秒，以及识别到四个结果。
+
+   因为目前是直接用CPU识别的，速度慢，用GPU速度会提升几百倍。关于GPU的使用待稍后整理发出，也可加微信交流。
+
+   贴一个效果图：
+
+   ![predicted img](./predictions.jpg)
+   
+   ### 关于环境搭建以及GPU训练方面文档稍后整理发出
+   
+   内容涉及：
+   
+   - Ubuntu18.04下的环境搭建
+     - cuda + cudnn + opencv
+     - Python3
+     - NVIDIA 显卡驱动
+   - GPU环境下模型的编译和使用
+   
+   GPU的使用在生产环境是必须的，速度提升了几百倍...
+   
+   
+   
+   **如果您看到这里，请给我一个star:star:谢谢，如果有遗漏，请留言指正，感激！**
+   
+   
\ No newline at end of file
diff --git a/hanzi_detection/examples/art.c b/hanzi_detection/examples/art.c
new file mode 100755
index 0000000..932688e
--- /dev/null
+++ b/hanzi_detection/examples/art.c
@@ -0,0 +1,59 @@
+#include "darknet.h"
+
+#include <sys/time.h>
+
+void demo_art(char *cfgfile, char *weightfile, int cam_index)
+{
+#ifdef OPENCV
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+
+    void * cap = open_video_stream(0, cam_index, 0,0,0);
+
+    char *window = "ArtJudgementBot9000!!!";
+    if(!cap) error("Couldn't connect to webcam.\n");
+    int i;
+    int idx[] = {37, 401, 434};
+    int n = sizeof(idx)/sizeof(idx[0]);
+
+    while(1){
+        image in = get_image_from_stream(cap);
+        image in_s = resize_image(in, net->w, net->h);
+
+        float *p = network_predict(net, in_s.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+
+        float score = 0;
+        for(i = 0; i < n; ++i){
+            float s = p[idx[i]];
+            if (s > score) score = s;
+        }
+        score = score;
+        printf("I APPRECIATE THIS ARTWORK: %10.7f%%\n", score*100);
+        printf("[");
+	int upper = 30;
+        for(i = 0; i < upper; ++i){
+            printf("%c", ((i+.5) < score*upper) ? 219 : ' ');
+        }
+        printf("]\n");
+
+        show_image(in, window, 1);
+        free_image(in_s);
+        free_image(in);
+    }
+#endif
+}
+
+
+void run_art(int argc, char **argv)
+{
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    char *cfg = argv[2];
+    char *weights = argv[3];
+    demo_art(cfg, weights, cam_index);
+}
+
diff --git a/hanzi_detection/examples/attention.c b/hanzi_detection/examples/attention.c
new file mode 100755
index 0000000..cd1e579
--- /dev/null
+++ b/hanzi_detection/examples/attention.c
@@ -0,0 +1,459 @@
+#include "darknet.h"
+
+#include <sys/time.h>
+#include <assert.h>
+
+void extend_data_truth(data *d, int n, float val)
+{
+    int i, j;
+    for(i = 0; i < d->y.rows; ++i){
+        d->y.vals[i] = realloc(d->y.vals[i], (d->y.cols+n)*sizeof(float));
+        for(j = 0; j < n; ++j){
+            d->y.vals[i][d->y.cols + j] = val;
+        }
+    }
+    d->y.cols += n;
+}
+
+matrix network_loss_data(network *net, data test)
+{
+    int i,b;
+    int k = 1;
+    matrix pred = make_matrix(test.X.rows, k);
+    float *X = calloc(net->batch*test.X.cols, sizeof(float));
+    float *y = calloc(net->batch*test.y.cols, sizeof(float));
+    for(i = 0; i < test.X.rows; i += net->batch){
+        for(b = 0; b < net->batch; ++b){
+            if(i+b == test.X.rows) break;
+            memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+            memcpy(y+b*test.y.cols, test.y.vals[i+b], test.y.cols*sizeof(float));
+        }
+
+        network orig = *net;
+        net->input = X;
+        net->truth = y;
+        net->train = 0;
+        net->delta = 0;
+        forward_network(net);
+        *net = orig;
+
+        float *delta = net->layers[net->n-1].output;
+        for(b = 0; b < net->batch; ++b){
+            if(i+b == test.X.rows) break;
+            int t = max_index(y + b*test.y.cols, 1000);
+            float err = sum_array(delta + b*net->outputs, net->outputs);
+            pred.vals[i+b][0] = -err;
+            //pred.vals[i+b][0] = 1-delta[b*net->outputs + t];
+        }
+    }
+    free(X);
+    free(y);
+    return pred;   
+}
+
+void train_attention(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+{
+    int i, j;
+
+    float avg_cls_loss = -1;
+    float avg_att_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+    double time;
+
+    int divs=3;
+    int size=2;
+
+    load_args args = {0};
+    args.w = divs*net->w/size;
+    args.h = divs*net->h/size;
+    args.size = divs*net->w/size;
+    args.threads = 32;
+    args.hierarchy = net->hierarchy;
+
+    args.min = net->min_ratio*args.w;
+    args.max = net->max_ratio*args.w;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+
+    args.paths = paths;
+    args.classes = classes;
+    args.n = imgs;
+    args.m = N;
+    args.labels = labels;
+    args.type = CLASSIFICATION_DATA;
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+        data resized = resize_data(train, net->w, net->h);
+        extend_data_truth(&resized, divs*divs, 0);
+        data *tiles = tile_data(train, divs, size);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float aloss = 0;
+        float closs = 0;
+        int z;
+        for (i = 0; i < divs*divs/ngpus; ++i) {
+#pragma omp parallel for
+            for(j = 0; j < ngpus; ++j){
+                int index = i*ngpus + j;
+                extend_data_truth(tiles+index, divs*divs, SECRET_NUM);
+                matrix deltas = network_loss_data(nets[j], tiles[index]);
+                for(z = 0; z < resized.y.rows; ++z){
+                    resized.y.vals[z][train.y.cols + index] = deltas.vals[z][0];
+                }
+                free_matrix(deltas);
+            }
+        }
+        int *inds = calloc(resized.y.rows, sizeof(int));
+        for(z = 0; z < resized.y.rows; ++z){
+            int index = max_index(resized.y.vals[z] + train.y.cols, divs*divs);
+            inds[z] = index;
+            for(i = 0; i < divs*divs; ++i){
+                resized.y.vals[z][train.y.cols + i] = (i == index)? 1 : 0;
+            }
+        }
+        data best = select_data(tiles, inds);
+        free(inds);
+        #ifdef GPU
+        if (ngpus == 1) {
+            closs = train_network(net, best);
+        } else {
+            closs = train_networks(nets, ngpus, best, 4);
+        }
+        #endif
+        for (i = 0; i < divs*divs; ++i) {
+            printf("%.2f ", resized.y.vals[0][train.y.cols + i]);
+            if((i+1)%divs == 0) printf("\n");
+            free_data(tiles[i]);
+        }
+        free_data(best);
+        printf("\n");
+        image im = float_to_image(64,64,3,resized.X.vals[0]);
+        //show_image(im, "orig");
+        //cvWaitKey(100);
+        /*
+           image im1 = float_to_image(64,64,3,tiles[i].X.vals[0]);
+           image im2 = float_to_image(64,64,3,resized.X.vals[0]);
+           show_image(im1, "tile");
+           show_image(im2, "res");
+         */
+#ifdef GPU
+        if (ngpus == 1) {
+            aloss = train_network(net, resized);
+        } else {
+            aloss = train_networks(nets, ngpus, resized, 4);
+        }
+#endif
+        for(i = 0; i < divs*divs; ++i){
+            printf("%f ", nets[0]->output[1000 + i]);
+            if ((i+1) % divs == 0) printf("\n");
+        }
+        printf("\n");
+
+        free_data(resized);
+        free_data(train);
+        if(avg_cls_loss == -1) avg_cls_loss = closs;
+        if(avg_att_loss == -1) avg_att_loss = aloss;
+        avg_cls_loss = avg_cls_loss*.9 + closs*.1;
+        avg_att_loss = avg_att_loss*.9 + aloss*.1;
+
+        printf("%ld, %.3f: Att: %f, %f avg, Class: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, aloss, avg_att_loss, closs, avg_cls_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%1000 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+    pthread_join(load_thread, 0);
+
+    free_network(net);
+    free_ptrs((void**)labels, classes);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void validate_attention_single(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *leaf_list = option_find_str(options, "leaves", 0);
+    if(leaf_list) change_leaves(net->hierarchy, leaf_list);
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+    int divs = 4;
+    int size = 2;
+    int extra = 0;
+    float *avgs = calloc(classes, sizeof(float));
+    int *inds = calloc(divs*divs, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, net->w*divs/size);
+        image crop = crop_image(resized, (resized.w - net->w*divs/size)/2, (resized.h - net->h*divs/size)/2, net->w*divs/size, net->h*divs/size);
+        image rcrop = resize_image(crop, net->w, net->h);
+        //show_image(im, "orig");
+        //show_image(crop, "cropped");
+        //cvWaitKey(0);
+        float *pred = network_predict(net, rcrop.data);
+        //pred[classes + 56] = 0;
+        for(j = 0; j < divs*divs; ++j){
+            printf("%.2f ", pred[classes + j]);
+            if((j+1)%divs == 0) printf("\n");
+        }
+        printf("\n");
+        copy_cpu(classes, pred, 1, avgs, 1);
+        top_k(pred + classes, divs*divs, divs*divs, inds);
+        show_image(crop, "crop");
+        for(j = 0; j < extra; ++j){
+            int index = inds[j];
+            int row = index / divs;
+            int col = index % divs;
+            int y = row * crop.h / divs - (net->h - crop.h/divs)/2;
+            int x = col * crop.w / divs - (net->w - crop.w/divs)/2;
+            printf("%d %d %d %d\n", row, col, y, x);
+            image tile = crop_image(crop, x, y, net->w, net->h);
+            float *pred = network_predict(net, tile.data);
+            axpy_cpu(classes, 1., pred, 1, avgs, 1);
+            show_image(tile, "tile");
+            //cvWaitKey(10);
+        }
+        if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
+
+        if(rcrop.data != resized.data) free_image(rcrop);
+        if(resized.data != im.data) free_image(resized);
+        free_image(im);
+        free_image(crop);
+        top_k(pred, classes, topk, indexes);
+
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void validate_attention_multi(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+    int scales[] = {224, 288, 320, 352, 384};
+    int nscales = sizeof(scales)/sizeof(scales[0]);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        float *pred = calloc(classes, sizeof(float));
+        image im = load_image_color(paths[i], 0, 0);
+        for(j = 0; j < nscales; ++j){
+            image r = resize_min(im, scales[j]);
+            resize_network(net, r.w, r.h);
+            float *p = network_predict(net, r.data);
+            if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            flip_image(r);
+            p = network_predict(net, r.data);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            if(r.data != im.data) free_image(r);
+        }
+        free_image(im);
+        top_k(pred, classes, topk, indexes);
+        free(pred);
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void predict_attention(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    list *options = read_data_cfg(datacfg);
+
+    char *name_list = option_find_str(options, "names", 0);
+    if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
+    if(top == 0) top = option_find_int(options, "top", 1);
+
+    int i = 0;
+    char **names = get_labels(name_list);
+    clock_t time;
+    int *indexes = calloc(top, sizeof(int));
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image r = letterbox_image(im, net->w, net->h);
+        //resize_network(&net, r.w, r.h);
+        //printf("%d %d\n", r.w, r.h);
+
+        float *X = r.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_k(predictions, net->outputs, top, indexes);
+        fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        for(i = 0; i < top; ++i){
+            int index = indexes[i];
+            //if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root");
+            //else printf("%s: %f\n",names[index], predictions[index]);
+            printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
+        }
+        if(r.data != im.data) free_image(r);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void run_attention(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int ngpus;
+    int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
+
+
+    int top = find_int_arg(argc, argv, "-t", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    char *layer_s = (argc > 7) ? argv[7]: 0;
+    if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top);
+    else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights);
+    else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights);
+}
+
+
diff --git a/hanzi_detection/examples/captcha.c b/hanzi_detection/examples/captcha.c
new file mode 100755
index 0000000..41d6d07
--- /dev/null
+++ b/hanzi_detection/examples/captcha.c
@@ -0,0 +1,353 @@
+#include "darknet.h"
+
+void fix_data_captcha(data d, int mask)
+{
+    matrix labels = d.y;
+    int i, j;
+    for(i = 0; i < d.y.rows; ++i){
+        for(j = 0; j < d.y.cols; j += 2){
+            if (mask){
+                if(!labels.vals[i][j]){
+                    labels.vals[i][j] = SECRET_NUM;
+                    labels.vals[i][j+1] = SECRET_NUM;
+                }else if(labels.vals[i][j+1]){
+                    labels.vals[i][j] = 0;
+                }
+            } else{
+                if (labels.vals[i][j]) {
+                    labels.vals[i][j+1] = 0;
+                } else {
+                    labels.vals[i][j+1] = 1;
+                }
+            }
+        }
+    }
+}
+
+void train_captcha(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network *net = load_network(cfgfile, weightfile, 0);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = 1024;
+    int i = *net->seen/imgs;
+    int solved = 1;
+    list *plist;
+    char **labels = get_labels("/data/captcha/reimgs.labels.list");
+    if (solved){
+        plist = get_paths("/data/captcha/reimgs.solved.list");
+    }else{
+        plist = get_paths("/data/captcha/reimgs.raw.list");
+    }
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    clock_t time;
+    pthread_t load_thread;
+    data train;
+    data buffer;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.paths = paths;
+    args.classes = 26;
+    args.n = imgs;
+    args.m = plist->size;
+    args.labels = labels;
+    args.d = &buffer;
+    args.type = CLASSIFICATION_DATA;
+
+    load_thread = load_data_in_thread(args);
+    while(1){
+        ++i;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        fix_data_captcha(train, solved);
+
+        /*
+           image im = float_to_image(256, 256, 3, train.X.vals[114]);
+           show_image(im, "training");
+           cvWaitKey(0);
+         */
+
+        load_thread = load_data_in_thread(args);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net->seen);
+        free_data(train);
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+            save_weights(net, buff);
+        }
+    }
+}
+
+void test_captcha(char *cfgfile, char *weightfile, char *filename)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+    int i = 0;
+    char **names = get_labels("/data/captcha/reimgs.labels.list");
+    char buff[256];
+    char *input = buff;
+    int indexes[26];
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            //printf("Enter Image Path: ");
+            //fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, net->w, net->h);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 26, indexes);
+        //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        for(i = 0; i < 26; ++i){
+            int index = indexes[i];
+            if(i != 0) printf(", ");
+            printf("%s %f", names[index], predictions[index]);
+        }
+        printf("\n");
+        fflush(stdout);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+void valid_captcha(char *cfgfile, char *weightfile, char *filename)
+{
+    char **labels = get_labels("/data/captcha/reimgs.labels.list");
+    network *net = load_network(cfgfile, weightfile, 0);
+    list *plist = get_paths("/data/captcha/reimgs.fg.list");
+    char **paths = (char **)list_to_array(plist);
+    int N = plist->size;
+    int outputs = net->outputs;
+
+    set_batch_network(net, 1);
+    srand(2222222);
+    int i, j;
+    for(i = 0; i < N; ++i){
+        if (i%100 == 0) fprintf(stderr, "%d\n", i);
+        image im = load_image_color(paths[i], net->w, net->h);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        int truth = -1;
+        for(j = 0; j < 13; ++j){
+            if (strstr(paths[i], labels[j])) truth = j;
+        }
+        if (truth == -1){
+            fprintf(stderr, "bad: %s\n", paths[i]);
+            return;
+        }
+        printf("%d, ", truth);
+        for(j = 0; j < outputs; ++j){
+            if (j != 0) printf(", ");
+            printf("%f", predictions[j]);
+        }
+        printf("\n");
+        fflush(stdout);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+/*
+   void train_captcha(char *cfgfile, char *weightfile)
+   {
+   float avg_loss = -1;
+   srand(time(0));
+   char *base = basecfg(cfgfile);
+   printf("%s\n", base);
+   network net = parse_network_cfg(cfgfile);
+   if(weightfile){
+   load_weights(&net, weightfile);
+   }
+   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+   int imgs = 1024;
+   int i = net->seen/imgs;
+   list *plist = get_paths("/data/captcha/train.auto5");
+   char **paths = (char **)list_to_array(plist);
+   printf("%d\n", plist->size);
+   clock_t time;
+   while(1){
+   ++i;
+   time=clock();
+   data train = load_data_captcha(paths, imgs, plist->size, 10, 200, 60);
+   translate_data_rows(train, -128);
+   scale_data_rows(train, 1./128);
+   printf("Loaded: %lf seconds\n", sec(clock()-time));
+   time=clock();
+   float loss = train_network(net, train);
+   net->seen += imgs;
+   if(avg_loss == -1) avg_loss = loss;
+   avg_loss = avg_loss*.9 + loss*.1;
+   printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net->seen);
+   free_data(train);
+   if(i%10==0){
+   char buff[256];
+   sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+   save_weights(net, buff);
+   }
+   }
+   }
+
+   void decode_captcha(char *cfgfile, char *weightfile)
+   {
+   setbuf(stdout, NULL);
+   srand(time(0));
+   network net = parse_network_cfg(cfgfile);
+   set_batch_network(&net, 1);
+   if(weightfile){
+   load_weights(&net, weightfile);
+   }
+   char filename[256];
+   while(1){
+   printf("Enter filename: ");
+   fgets(filename, 256, stdin);
+   strtok(filename, "\n");
+   image im = load_image_color(filename, 300, 57);
+   scale_image(im, 1./255.);
+   float *X = im.data;
+   float *predictions = network_predict(net, X);
+   image out  = float_to_image(300, 57, 1, predictions);
+   show_image(out, "decoded");
+#ifdef OPENCV
+cvWaitKey(0);
+#endif
+free_image(im);
+}
+}
+
+void encode_captcha(char *cfgfile, char *weightfile)
+{
+float avg_loss = -1;
+srand(time(0));
+char *base = basecfg(cfgfile);
+printf("%s\n", base);
+network net = parse_network_cfg(cfgfile);
+if(weightfile){
+    load_weights(&net, weightfile);
+}
+printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+int imgs = 1024;
+int i = net->seen/imgs;
+list *plist = get_paths("/data/captcha/encode.list");
+char **paths = (char **)list_to_array(plist);
+printf("%d\n", plist->size);
+clock_t time;
+while(1){
+    ++i;
+    time=clock();
+    data train = load_data_captcha_encode(paths, imgs, plist->size, 300, 57);
+    scale_data_rows(train, 1./255);
+    printf("Loaded: %lf seconds\n", sec(clock()-time));
+    time=clock();
+    float loss = train_network(net, train);
+    net->seen += imgs;
+    if(avg_loss == -1) avg_loss = loss;
+    avg_loss = avg_loss*.9 + loss*.1;
+    printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net->seen);
+    free_matrix(train.X);
+    if(i%100==0){
+        char buff[256];
+        sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+        save_weights(net, buff);
+    }
+}
+}
+
+void validate_captcha(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int numchars = 37;
+    list *plist = get_paths("/data/captcha/solved.hard");
+    char **paths = (char **)list_to_array(plist);
+    int imgs = plist->size;
+    data valid = load_data_captcha(paths, imgs, 0, 10, 200, 60);
+    translate_data_rows(valid, -128);
+    scale_data_rows(valid, 1./128);
+    matrix pred = network_predict_data(net, valid);
+    int i, k;
+    int correct = 0;
+    int total = 0;
+    int accuracy = 0;
+    for(i = 0; i < imgs; ++i){
+        int allcorrect = 1;
+        for(k = 0; k < 10; ++k){
+            char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars));
+            char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars));
+            if (truth != prediction) allcorrect=0;
+            if (truth != '.' && truth == prediction) ++correct;
+            if (truth != '.' || truth != prediction) ++total;
+        }
+        accuracy += allcorrect;
+    }
+    printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total);
+    free_data(valid);
+}
+
+void test_captcha(char *cfgfile, char *weightfile)
+{
+    setbuf(stdout, NULL);
+    srand(time(0));
+    //char *base = basecfg(cfgfile);
+    //printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    set_batch_network(&net, 1);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    char filename[256];
+    while(1){
+        //printf("Enter filename: ");
+        fgets(filename, 256, stdin);
+        strtok(filename, "\n");
+        image im = load_image_color(filename, 200, 60);
+        translate_image(im, -128);
+        scale_image(im, 1/128.);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        print_letters(predictions, 10);
+        free_image(im);
+    }
+}
+    */
+void run_captcha(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "train")) train_captcha(cfg, weights);
+    else if(0==strcmp(argv[2], "test")) test_captcha(cfg, weights, filename);
+    else if(0==strcmp(argv[2], "valid")) valid_captcha(cfg, weights, filename);
+    //if(0==strcmp(argv[2], "test")) test_captcha(cfg, weights);
+    //else if(0==strcmp(argv[2], "encode")) encode_captcha(cfg, weights);
+    //else if(0==strcmp(argv[2], "decode")) decode_captcha(cfg, weights);
+    //else if(0==strcmp(argv[2], "valid")) validate_captcha(cfg, weights);
+}
+
diff --git a/hanzi_detection/examples/cifar.c b/hanzi_detection/examples/cifar.c
new file mode 100755
index 0000000..a5f5f24
--- /dev/null
+++ b/hanzi_detection/examples/cifar.c
@@ -0,0 +1,251 @@
+#include "darknet.h"
+
+void train_cifar(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network *net = load_network(cfgfile, weightfile, 0);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+
+    char *backup_directory = "/home/pjreddie/backup/";
+    int classes = 10;
+    int N = 50000;
+
+    char **labels = get_labels("data/cifar/labels.txt");
+    int epoch = (*net->seen)/N;
+    data train = load_all_cifar10();
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        clock_t time=clock();
+
+        float loss = train_network_sgd(net, train, 1);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.95 + loss*.05;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)labels, classes);
+    free(base);
+    free_data(train);
+}
+
+void train_cifar_distill(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network *net = load_network(cfgfile, weightfile, 0);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+
+    char *backup_directory = "/home/pjreddie/backup/";
+    int classes = 10;
+    int N = 50000;
+
+    char **labels = get_labels("data/cifar/labels.txt");
+    int epoch = (*net->seen)/N;
+
+    data train = load_all_cifar10();
+    matrix soft = csv_to_matrix("results/ensemble.csv");
+
+    float weight = .9;
+    scale_matrix(soft, weight);
+    scale_matrix(train.y, 1. - weight);
+    matrix_add_matrix(soft, train.y);
+
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        clock_t time=clock();
+
+        float loss = train_network_sgd(net, train, 1);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.95 + loss*.05;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)labels, classes);
+    free(base);
+    free_data(train);
+}
+
+void test_cifar_multi(char *filename, char *weightfile)
+{
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    float avg_acc = 0;
+    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+    int i;
+    for(i = 0; i < test.X.rows; ++i){
+        image im = float_to_image(32, 32, 3, test.X.vals[i]);
+
+        float pred[10] = {0};
+
+        float *p = network_predict(net, im.data);
+        axpy_cpu(10, 1, p, 1, pred, 1);
+        flip_image(im);
+        p = network_predict(net, im.data);
+        axpy_cpu(10, 1, p, 1, pred, 1);
+
+        int index = max_index(pred, 10);
+        int class = max_index(test.y.vals[i], 10);
+        if(index == class) avg_acc += 1;
+        free_image(im);
+        printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
+    }
+}
+
+void test_cifar(char *filename, char *weightfile)
+{
+    network *net = load_network(filename, weightfile, 0);
+    srand(time(0));
+
+    clock_t time;
+    float avg_acc = 0;
+    float avg_top5 = 0;
+    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+    time=clock();
+
+    float *acc = network_accuracies(net, test, 2);
+    avg_acc += acc[0];
+    avg_top5 += acc[1];
+    printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows);
+    free_data(test);
+}
+
+void extract_cifar()
+{
+char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"};
+    int i;
+    data train = load_all_cifar10();
+    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+    for(i = 0; i < train.X.rows; ++i){
+        image im = float_to_image(32, 32, 3, train.X.vals[i]);
+        int class = max_index(train.y.vals[i], 10);
+        char buff[256];
+        sprintf(buff, "data/cifar/train/%d_%s",i,labels[class]);
+        save_image_options(im, buff, PNG, 0);
+    }
+    for(i = 0; i < test.X.rows; ++i){
+        image im = float_to_image(32, 32, 3, test.X.vals[i]);
+        int class = max_index(test.y.vals[i], 10);
+        char buff[256];
+        sprintf(buff, "data/cifar/test/%d_%s",i,labels[class]);
+        save_image_options(im, buff, PNG, 0);
+    }
+}
+
+void test_cifar_csv(char *filename, char *weightfile)
+{
+    network *net = load_network(filename, weightfile, 0);
+    srand(time(0));
+
+    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+    matrix pred = network_predict_data(net, test);
+
+    int i;
+    for(i = 0; i < test.X.rows; ++i){
+        image im = float_to_image(32, 32, 3, test.X.vals[i]);
+        flip_image(im);
+    }
+    matrix pred2 = network_predict_data(net, test);
+    scale_matrix(pred, .5);
+    scale_matrix(pred2, .5);
+    matrix_add_matrix(pred2, pred);
+
+    matrix_to_csv(pred);
+    fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+    free_data(test);
+}
+
+void test_cifar_csvtrain(char *cfg, char *weights)
+{
+    network *net = load_network(cfg, weights, 0);
+    srand(time(0));
+
+    data test = load_all_cifar10();
+
+    matrix pred = network_predict_data(net, test);
+
+    int i;
+    for(i = 0; i < test.X.rows; ++i){
+        image im = float_to_image(32, 32, 3, test.X.vals[i]);
+        flip_image(im);
+    }
+    matrix pred2 = network_predict_data(net, test);
+    scale_matrix(pred, .5);
+    scale_matrix(pred2, .5);
+    matrix_add_matrix(pred2, pred);
+
+    matrix_to_csv(pred);
+    fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+    free_data(test);
+}
+
+void eval_cifar_csv()
+{
+    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+    matrix pred = csv_to_matrix("results/combined.csv");
+    fprintf(stderr, "%d %d\n", pred.rows, pred.cols);
+
+    fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+    free_data(test);
+    free_matrix(pred);
+}
+
+
+void run_cifar(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
+    else if(0==strcmp(argv[2], "extract")) extract_cifar();
+    else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights);
+    else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
+    else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights);
+    else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights);
+    else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights);
+    else if(0==strcmp(argv[2], "eval")) eval_cifar_csv();
+}
+
+
diff --git a/hanzi_detection/examples/classifier.c b/hanzi_detection/examples/classifier.c
new file mode 100755
index 0000000..df91a08
--- /dev/null
+++ b/hanzi_detection/examples/classifier.c
@@ -0,0 +1,1098 @@
+#include "darknet.h"
+
+#include <sys/time.h>
+#include <assert.h>
+
+float *get_regression_values(char **labels, int n)
+{
+    float *v = calloc(n, sizeof(float));
+    int i;
+    for(i = 0; i < n; ++i){
+        char *p = strchr(labels[i], ' ');
+        *p = 0;
+        v[i] = atof(p+1);
+    }
+    return v;
+}
+
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    int tag = option_find_int_quiet(options, "tag", 0);
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+    char *tree = option_find_str(options, "tree", 0);
+    if (tree) net->hierarchy = read_tree(tree);
+    int classes = option_find_int(options, "classes", 2);
+
+    char **labels = 0;
+    if(!tag){
+        labels = get_labels(label_list);
+    }
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+    double time;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.hierarchy = net->hierarchy;
+
+    args.min = net->min_ratio*net->w;
+    args.max = net->max_ratio*net->w;
+    printf("%d %d\n", args.min, args.max);
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+
+    args.paths = paths;
+    args.classes = classes;
+    args.n = imgs;
+    args.m = N;
+    args.labels = labels;
+    if (tag){
+        args.type = TAG_DATA;
+    } else {
+        args.type = CLASSIFICATION_DATA;
+    }
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int count = 0;
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        if(net->random && count++%40 == 0){
+            printf("Resizing\n");
+            int dim = (rand() % 11 + 4) * 32;
+            //if (get_current_batch(net)+200 > net->max_batches) dim = 608;
+            //int dim = (rand() % 4 + 16) * 32;
+            printf("%d\n", dim);
+            args.w = dim;
+            args.h = dim;
+            args.size = dim;
+            args.min = net->min_ratio*dim;
+            args.max = net->max_ratio*dim;
+            printf("%d %d\n", args.min, args.max);
+
+            pthread_join(load_thread, 0);
+            train = buffer;
+            free_data(train);
+            load_thread = load_data(args);
+
+            for(i = 0; i < ngpus; ++i){
+                resize_network(nets[i], dim, dim);
+            }
+            net = nets[0];
+        }
+        time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%1000 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+    pthread_join(load_thread, 0);
+
+    free_network(net);
+    if(labels) free_ptrs((void**)labels, classes);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
+{
+    int i = 0;
+    network *net = load_network(filename, weightfile, 0);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    clock_t time;
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int splits = m/1000;
+    int num = (i+1)*m/splits - i*m/splits;
+
+    data val, buffer;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+
+    args.paths = paths;
+    args.classes = classes;
+    args.n = num;
+    args.m = 0;
+    args.labels = labels;
+    args.d = &buffer;
+    args.type = OLD_CLASSIFICATION_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    for(i = 1; i <= splits; ++i){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        val = buffer;
+
+        num = (i+1)*m/splits - i*m/splits;
+        char **part = paths+(i*m/splits);
+        if(i != splits){
+            args.paths = part;
+            load_thread = load_data_in_thread(args);
+        }
+        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+        time=clock();
+        float *acc = network_accuracies(net, val, topk);
+        avg_acc += acc[0];
+        avg_topk += acc[1];
+        printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows);
+        free_data(val);
+    }
+}
+
+void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        int w = net->w;
+        int h = net->h;
+        int shift = 32;
+        image im = load_image_color(paths[i], w+shift, h+shift);
+        image images[10];
+        images[0] = crop_image(im, -shift, -shift, w, h);
+        images[1] = crop_image(im, shift, -shift, w, h);
+        images[2] = crop_image(im, 0, 0, w, h);
+        images[3] = crop_image(im, -shift, shift, w, h);
+        images[4] = crop_image(im, shift, shift, w, h);
+        flip_image(im);
+        images[5] = crop_image(im, -shift, -shift, w, h);
+        images[6] = crop_image(im, shift, -shift, w, h);
+        images[7] = crop_image(im, 0, 0, w, h);
+        images[8] = crop_image(im, -shift, shift, w, h);
+        images[9] = crop_image(im, shift, shift, w, h);
+        float *pred = calloc(classes, sizeof(float));
+        for(j = 0; j < 10; ++j){
+            float *p = network_predict(net, images[j].data);
+            if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1, 1);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            free_image(images[j]);
+        }
+        free_image(im);
+        top_k(pred, classes, topk, indexes);
+        free(pred);
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+
+    int size = net->w;
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, size);
+        resize_network(net, resized.w, resized.h);
+        //show_image(im, "orig");
+        //show_image(crop, "cropped");
+        //cvWaitKey(0);
+        float *pred = network_predict(net, resized.data);
+        if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
+
+        free_image(im);
+        free_image(resized);
+        top_k(pred, classes, topk, indexes);
+
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+
+void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *leaf_list = option_find_str(options, "leaves", 0);
+    if(leaf_list) change_leaves(net->hierarchy, leaf_list);
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        image im = load_image_color(paths[i], 0, 0);
+        image crop = center_crop_image(im, net->w, net->h);
+        //grayscale_image_3c(crop);
+        //show_image(im, "orig");
+        //show_image(crop, "cropped");
+        //cvWaitKey(0);
+        float *pred = network_predict(net, crop.data);
+        if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
+
+        free_image(im);
+        free_image(crop);
+        top_k(pred, classes, topk, indexes);
+
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%s, %d, %f, %f, \n", paths[i], class, pred[0], pred[1]);
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void validate_classifier_multi(char *datacfg, char *cfg, char *weights)
+{
+    int i, j;
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *valid_list = option_find_str(options, "valid", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk = option_find_int(options, "top", 1);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(valid_list);
+    //int scales[] = {224, 288, 320, 352, 384};
+    int scales[] = {224, 256, 288, 320};
+    int nscales = sizeof(scales)/sizeof(scales[0]);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int *indexes = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        float *pred = calloc(classes, sizeof(float));
+        image im = load_image_color(paths[i], 0, 0);
+        for(j = 0; j < nscales; ++j){
+            image r = resize_max(im, scales[j]);
+            resize_network(net, r.w, r.h);
+            float *p = network_predict(net, r.data);
+            if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            flip_image(r);
+            p = network_predict(net, r.data);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            if(r.data != im.data) free_image(r);
+        }
+        free_image(im);
+        top_k(pred, classes, topk, indexes);
+        free(pred);
+        if(indexes[0] == class) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    list *options = read_data_cfg(datacfg);
+
+    char *name_list = option_find_str(options, "names", 0);
+    if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
+    int top = option_find_int(options, "top", 1);
+
+    int i = 0;
+    char **names = get_labels(name_list);
+    clock_t time;
+    int *indexes = calloc(top, sizeof(int));
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image orig = load_image_color(input, 0, 0);
+        image r = resize_min(orig, 256);
+        image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224);
+        float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
+        float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
+        float var[3];
+        var[0] = std[0]*std[0];
+        var[1] = std[1]*std[1];
+        var[2] = std[2]*std[2];
+
+        normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h);
+
+        float *X = im.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+
+        layer l = net->layers[layer_num];
+        for(i = 0; i < l.c; ++i){
+            if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
+        }
+#ifdef GPU
+        cuda_pull_array(l.output_gpu, l.output, l.outputs);
+#endif
+        for(i = 0; i < l.outputs; ++i){
+            printf("%f\n", l.output[i]);
+        }
+        /*
+
+           printf("\n\nWeights\n");
+           for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
+           printf("%f\n", l.filters[i]);
+           }
+
+           printf("\n\nBiases\n");
+           for(i = 0; i < l.n; ++i){
+           printf("%f\n", l.biases[i]);
+           }
+         */
+
+        top_predictions(net, top, indexes);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        for(i = 0; i < top; ++i){
+            int index = indexes[i];
+            printf("%s: %f\n", names[index], predictions[index]);
+        }
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    list *options = read_data_cfg(datacfg);
+
+    char *name_list = option_find_str(options, "names", 0);
+    if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
+    if(top == 0) top = option_find_int(options, "top", 1);
+
+    int i = 0;
+    char **names = get_labels(name_list);
+    clock_t time;
+    int *indexes = calloc(top, sizeof(int));
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image r = letterbox_image(im, net->w, net->h);
+        //image r = resize_min(im, 320);
+        //printf("%d %d\n", r.w, r.h);
+        //resize_network(net, r.w, r.h);
+        //printf("%d %d\n", r.w, r.h);
+
+        float *X = r.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_k(predictions, net->outputs, top, indexes);
+        fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        for(i = 0; i < top; ++i){
+            int index = indexes[i];
+            //if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root");
+            //else printf("%s: %f\n",names[index], predictions[index]);
+            printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
+        }
+        if(r.data != im.data) free_image(r);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void label_classifier(char *datacfg, char *filename, char *weightfile)
+{
+    int i;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *label_list = option_find_str(options, "names", "data/labels.list");
+    char *test_list = option_find_str(options, "test", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    char **labels = get_labels(label_list);
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    for(i = 0; i < m; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, net->w);
+        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
+        float *pred = network_predict(net, crop.data);
+
+        if(resized.data != im.data) free_image(resized);
+        free_image(im);
+        free_image(crop);
+        int ind = max_index(pred, classes);
+
+        printf("%s\n", labels[ind]);
+    }
+}
+
+void csv_classifier(char *datacfg, char *cfgfile, char *weightfile)
+{
+    int i,j;
+    network *net = load_network(cfgfile, weightfile, 0);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *test_list = option_find_str(options, "test", "data/test.list");
+    int top = option_find_int(options, "top", 1);
+
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+    int *indexes = calloc(top, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        double time = what_time_is_it_now();
+        char *path = paths[i];
+        image im = load_image_color(path, 0, 0);
+        image r = letterbox_image(im, net->w, net->h);
+        float *predictions = network_predict(net, r.data);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_k(predictions, net->outputs, top, indexes);
+
+        printf("%s", path);
+        for(j = 0; j < top; ++j){
+            printf("\t%d", indexes[j]);
+        }
+        printf("\n");
+
+        free_image(im);
+        free_image(r);
+
+        fprintf(stderr, "%lf seconds, %d images, %d total\n", what_time_is_it_now() - time, i+1, m);
+    }
+}
+
+void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
+{
+    int curr = 0;
+    network *net = load_network(cfgfile, weightfile, 0);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *test_list = option_find_str(options, "test", "data/test.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    clock_t time;
+
+    data val, buffer;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.paths = paths;
+    args.classes = classes;
+    args.n = net->batch;
+    args.m = 0;
+    args.labels = 0;
+    args.d = &buffer;
+    args.type = OLD_CLASSIFICATION_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    for(curr = net->batch; curr < m; curr += net->batch){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        val = buffer;
+
+        if(curr < m){
+            args.paths = paths + curr;
+            if (curr + net->batch > m) args.n = m - curr;
+            load_thread = load_data_in_thread(args);
+        }
+        fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+        time=clock();
+        matrix pred = network_predict_data(net, val);
+
+        int i, j;
+        if (target_layer >= 0){
+            //layer l = net->layers[target_layer];
+        }
+
+        for(i = 0; i < pred.rows; ++i){
+            printf("%s", paths[curr-net->batch+i]);
+            for(j = 0; j < pred.cols; ++j){
+                printf("\t%g", pred.vals[i][j]);
+            }
+            printf("\n");
+        }
+
+        free_matrix(pred);
+
+        fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
+        free_data(val);
+    }
+}
+
+void file_output_classifier(char *datacfg, char *filename, char *weightfile, char *listfile)
+{
+    int i,j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    //char *label_list = option_find_str(options, "names", "data/labels.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    list *plist = get_paths(listfile);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    for(i = 0; i < m; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, net->w);
+        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
+
+        float *pred = network_predict(net, crop.data);
+        if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 0, 1);
+
+        if(resized.data != im.data) free_image(resized);
+        free_image(im);
+        free_image(crop);
+
+        printf("%s", paths[i]);
+        for(j = 0; j < classes; ++j){
+            printf("\t%g", pred[j]);
+        }
+        printf("\n");
+    }
+}
+
+
+void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    float threat = 0;
+    float roll = .2;
+
+    printf("Classifier Demo\n");
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    list *options = read_data_cfg(datacfg);
+
+    srand(2222222);
+    void * cap = open_video_stream(filename, cam_index, 0,0,0);
+
+    int top = option_find_int(options, "top", 1);
+
+    char *name_list = option_find_str(options, "names", 0);
+    char **names = get_labels(name_list);
+
+    int *indexes = calloc(top, sizeof(int));
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    //cvNamedWindow("Threat", CV_WINDOW_NORMAL); 
+    //cvResizeWindow("Threat", 512, 512);
+    float fps = 0;
+    int i;
+
+    int count = 0;
+
+    while(1){
+        ++count;
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        if(!in.data) break;
+        image in_s = resize_image(in, net->w, net->h);
+
+        image out = in;
+        int x1 = out.w / 20;
+        int y1 = out.h / 20;
+        int x2 = 2*x1;
+        int y2 = out.h - out.h/20;
+
+        int border = .01*out.h;
+        int h = y2 - y1 - 2*border;
+        int w = x2 - x1 - 2*border;
+
+        float *predictions = network_predict(net, in_s.data);
+        float curr_threat = 0;
+        if(1){
+            curr_threat = predictions[0] * 0 + 
+                predictions[1] * .6 + 
+                predictions[2];
+        } else {
+            curr_threat = predictions[218] +
+                predictions[539] + 
+                predictions[540] + 
+                predictions[368] + 
+                predictions[369] + 
+                predictions[370];
+        }
+        threat = roll * curr_threat + (1-roll) * threat;
+
+        draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
+        if(threat > .97) {
+            draw_box_width(out,  x2 + .5 * w + border,
+                    y1 + .02*h - 2*border, 
+                    x2 + .5 * w + 6*border, 
+                    y1 + .02*h + 3*border, 3*border, 1,0,0);
+        }
+        draw_box_width(out,  x2 + .5 * w + border,
+                y1 + .02*h - 2*border, 
+                x2 + .5 * w + 6*border, 
+                y1 + .02*h + 3*border, .5*border, 0,0,0);
+        draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
+        if(threat > .57) {
+            draw_box_width(out,  x2 + .5 * w + border,
+                    y1 + .42*h - 2*border, 
+                    x2 + .5 * w + 6*border, 
+                    y1 + .42*h + 3*border, 3*border, 1,1,0);
+        }
+        draw_box_width(out,  x2 + .5 * w + border,
+                y1 + .42*h - 2*border, 
+                x2 + .5 * w + 6*border, 
+                y1 + .42*h + 3*border, .5*border, 0,0,0);
+
+        draw_box_width(out, x1, y1, x2, y2, border, 0,0,0);
+        for(i = 0; i < threat * h ; ++i){
+            float ratio = (float) i / h;
+            float r = (ratio < .5) ? (2*(ratio)) : 1;
+            float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5);
+            draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
+        }
+        top_predictions(net, top, indexes);
+        char buff[256];
+        sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count);
+        //save_image(out, buff);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        for(i = 0; i < top; ++i){
+            int index = indexes[i];
+            printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+        }
+
+        if(1){
+            show_image(out, "Threat", 10);
+        }
+        free_image(in_s);
+        free_image(in);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
+
+    printf("Classifier Demo\n");
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    list *options = read_data_cfg(datacfg);
+
+    srand(2222222);
+    void * cap = open_video_stream(filename, cam_index, 0,0,0);
+
+    int top = option_find_int(options, "top", 1);
+
+    char *name_list = option_find_str(options, "names", 0);
+    char **names = get_labels(name_list);
+
+    int *indexes = calloc(top, sizeof(int));
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    float fps = 0;
+    int i;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        image in_s = resize_image(in, net->w, net->h);
+
+        float *predictions = network_predict(net, in_s.data);
+        top_predictions(net, top, indexes);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+
+        int threat = 0;
+        for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+            int index = bad_cats[i];
+            if(predictions[index] > .01){
+                printf("Threat Detected!\n");
+                threat = 1;
+                break;
+            }
+        }
+        if(!threat) printf("Scanning...\n");
+        for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+            int index = bad_cats[i];
+            if(predictions[index] > .01){
+                printf("%s\n", names[index]);
+            }
+        }
+
+        show_image(in, "Threat Detection", 10);
+        free_image(in_s);
+        free_image(in);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    char *base = basecfg(cfgfile);
+    image **alphabet = load_alphabet();
+    printf("Classifier Demo\n");
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    list *options = read_data_cfg(datacfg);
+
+    srand(2222222);
+
+    int w = 1280;
+    int h = 720;
+    void * cap = open_video_stream(filename, cam_index, w, h, 0);
+
+    int top = option_find_int(options, "top", 1);
+
+    char *label_list = option_find_str(options, "labels", 0);
+    char *name_list = option_find_str(options, "names", label_list);
+    char **names = get_labels(name_list);
+
+    int *indexes = calloc(top, sizeof(int));
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    float fps = 0;
+    int i;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        //image in_s = resize_image(in, net->w, net->h);
+        image in_s = letterbox_image(in, net->w, net->h);
+
+        float *predictions = network_predict(net, in_s.data);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_predictions(net, top, indexes);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        int lh = in.h*.03;
+        int toph = 3*lh;
+
+        float rgb[3] = {1,1,1};
+        for(i = 0; i < top; ++i){
+            printf("%d\n", toph);
+            int index = indexes[i];
+            printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+
+            char buff[1024];
+            sprintf(buff, "%3.1f%%: %s\n", predictions[index]*100, names[index]);
+            image label = get_label(alphabet, buff, lh);
+            draw_label(in, toph, lh, label, rgb);
+            toph += 2*lh;
+            free_image(label);
+        }
+
+        show_image(in, base, 10);
+        free_image(in_s);
+        free_image(in);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void run_classifier(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int ngpus;
+    int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
+
+
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int top = find_int_arg(argc, argv, "-t", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    char *layer_s = (argc > 7) ? argv[7]: 0;
+    int layer = layer_s ? atoi(layer_s) : -1;
+    if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
+    else if(0==strcmp(argv[2], "fout")) file_output_classifier(data, cfg, weights, filename);
+    else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
+    else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
+    else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
+    else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
+    else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
+    else if(0==strcmp(argv[2], "csv")) csv_classifier(data, cfg, weights);
+    else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights);
+    else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
+    else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
+    else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
+    else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
+}
+
+
diff --git a/hanzi_detection/examples/coco.c b/hanzi_detection/examples/coco.c
new file mode 100755
index 0000000..6a50b89
--- /dev/null
+++ b/hanzi_detection/examples/coco.c
@@ -0,0 +1,357 @@
+#include "darknet.h"
+
+#include <stdio.h>
+
+char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
+
+int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
+
+void train_coco(char *cfgfile, char *weightfile)
+{
+    //char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+    //char *train_images = "/home/pjreddie/data/coco/train.txt";
+    char *train_images = "data/coco.trainval.txt";
+    //char *train_images = "data/bags.train.list";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = net->batch*net->subdivisions;
+    int i = *net->seen/imgs;
+    data train, buffer;
+
+
+    layer l = net->layers[net->n - 1];
+
+    int side = l.side;
+    int classes = l.classes;
+    float jitter = l.jitter;
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.classes = classes;
+    args.jitter = jitter;
+    args.num_boxes = side;
+    args.d = &buffer;
+    args.type = REGION_DATA;
+
+    args.angle = net->angle;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net->max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        /*
+           image im = float_to_image(net->w, net->h, 3, train.X.vals[113]);
+           image copy = copy_image(im);
+           draw_coco(copy, train.y.vals[113], 7, "truth");
+           cvWaitKey(0);
+           free_image(copy);
+         */
+
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0 || (i < 1000 && i%100 == 0)){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+static void print_cocos(FILE *fp, int image_id, detection *dets, int num_boxes, int classes, int w, int h)
+{
+    int i, j;
+    for(i = 0; i < num_boxes; ++i){
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
+
+        if (xmin < 0) xmin = 0;
+        if (ymin < 0) ymin = 0;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        float bx = xmin;
+        float by = ymin;
+        float bw = xmax - xmin;
+        float bh = ymax - ymin;
+
+        for(j = 0; j < classes; ++j){
+            if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
+        }
+    }
+}
+
+int get_coco_image_id(char *filename)
+{
+    char *p = strrchr(filename, '_');
+    return atoi(p+1);
+}
+
+void validate_coco(char *cfg, char *weights)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    char *base = "results/";
+    list *plist = get_paths("data/coco_val_5k.list");
+    //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt");
+    //list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+
+    char buff[1024];
+    snprintf(buff, 1024, "%s/coco_results.json", base);
+    FILE *fp = fopen(buff, "w");
+    fprintf(fp, "[\n");
+
+    int m = plist->size;
+    int i=0;
+    int t;
+
+    float thresh = .01;
+    int nms = 1;
+    float iou_thresh = .5;
+
+    int nthreads = 8;
+    image *val = calloc(nthreads, sizeof(image));
+    image *val_resized = calloc(nthreads, sizeof(image));
+    image *buf = calloc(nthreads, sizeof(image));
+    image *buf_resized = calloc(nthreads, sizeof(image));
+    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.type = IMAGE_DATA;
+
+    for(t = 0; t < nthreads; ++t){
+        args.path = paths[i+t];
+        args.im = &buf[t];
+        args.resized = &buf_resized[t];
+        thr[t] = load_data_in_thread(args);
+    }
+    time_t start = time(0);
+    for(i = nthreads; i < m+nthreads; i += nthreads){
+        fprintf(stderr, "%d\n", i);
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            pthread_join(thr[t], 0);
+            val[t] = buf[t];
+            val_resized[t] = buf_resized[t];
+        }
+        for(t = 0; t < nthreads && i+t < m; ++t){
+            args.path = paths[i+t];
+            args.im = &buf[t];
+            args.resized = &buf_resized[t];
+            thr[t] = load_data_in_thread(args);
+        }
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            char *path = paths[i+t-nthreads];
+            int image_id = get_coco_image_id(path);
+            float *X = val_resized[t].data;
+            network_predict(net, X);
+            int w = val[t].w;
+            int h = val[t].h;
+            int nboxes = 0;
+            detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes);
+            if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh);
+            print_cocos(fp, image_id, dets, l.side*l.side*l.n, classes, w, h);
+            free_detections(dets, nboxes);
+            free_image(val[t]);
+            free_image(val_resized[t]);
+        }
+    }
+    fseek(fp, -2, SEEK_CUR); 
+    fprintf(fp, "\n]\n");
+    fclose(fp);
+
+    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+}
+
+void validate_coco_recall(char *cfgfile, char *weightfile)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    char *base = "results/comp4_det_test_";
+    list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+    int side = l.side;
+
+    int j, k;
+    FILE **fps = calloc(classes, sizeof(FILE *));
+    for(j = 0; j < classes; ++j){
+        char buff[1024];
+        snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]);
+        fps[j] = fopen(buff, "w");
+    }
+
+    int m = plist->size;
+    int i=0;
+
+    float thresh = .001;
+    int nms = 0;
+    float iou_thresh = .5;
+
+    int total = 0;
+    int correct = 0;
+    int proposals = 0;
+    float avg_iou = 0;
+
+    for(i = 0; i < m; ++i){
+        char *path = paths[i];
+        image orig = load_image_color(path, 0, 0);
+        image sized = resize_image(orig, net->w, net->h);
+        char *id = basecfg(path);
+        network_predict(net, sized.data);
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
+        if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
+
+        char labelpath[4096];
+        find_replace(path, "images", "labels", labelpath);
+        find_replace(labelpath, "JPEGImages", "labels", labelpath);
+        find_replace(labelpath, ".jpg", ".txt", labelpath);
+        find_replace(labelpath, ".JPEG", ".txt", labelpath);
+
+        int num_labels = 0;
+        box_label *truth = read_boxes(labelpath, &num_labels);
+        for(k = 0; k < side*side*l.n; ++k){
+            if(dets[k].objectness > thresh){
+                ++proposals;
+            }
+        }
+        for (j = 0; j < num_labels; ++j) {
+            ++total;
+            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+            float best_iou = 0;
+            for(k = 0; k < side*side*l.n; ++k){
+                float iou = box_iou(dets[k].bbox, t);
+                if(dets[k].objectness > thresh && iou > best_iou){
+                    best_iou = iou;
+                }
+            }
+            avg_iou += best_iou;
+            if(best_iou > iou_thresh){
+                ++correct;
+            }
+        }
+        free_detections(dets, nboxes);
+        fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
+        free(id);
+        free_image(orig);
+        free_image(sized);
+    }
+}
+
+void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
+{
+    image **alphabet = load_alphabet();
+    network *net = load_network(cfgfile, weightfile, 0);
+    layer l = net->layers[net->n-1];
+    set_batch_network(net, 1);
+    srand(2222222);
+    float nms = .4;
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        } else {
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input,0,0);
+        image sized = resize_image(im, net->w, net->h);
+        float *X = sized.data;
+        time=clock();
+        network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
+        if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
+
+        draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
+        save_image(im, "prediction");
+        show_image(im, "predictions", 0);
+        free_detections(dets, nboxes);
+        free_image(im);
+        free_image(sized);
+        if (filename) break;
+    }
+}
+
+void run_coco(int argc, char **argv)
+{
+    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+    float thresh = find_float_arg(argc, argv, "-thresh", .2);
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int frame_skip = find_int_arg(argc, argv, "-s", 0);
+
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    int avg = find_int_arg(argc, argv, "-avg", 1);
+    if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh);
+    else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
+    else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
+    else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix, avg, .5, 0,0,0,0);
+}
diff --git a/hanzi_detection/examples/darknet.c b/hanzi_detection/examples/darknet.c
new file mode 100755
index 0000000..d538359
--- /dev/null
+++ b/hanzi_detection/examples/darknet.c
@@ -0,0 +1,503 @@
+#include "darknet.h"
+
+#include <time.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
+extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen);
+extern void run_yolo(int argc, char **argv);
+extern void run_detector(int argc, char **argv);
+extern void run_coco(int argc, char **argv);
+extern void run_nightmare(int argc, char **argv);
+extern void run_classifier(int argc, char **argv);
+extern void run_regressor(int argc, char **argv);
+extern void run_segmenter(int argc, char **argv);
+extern void run_isegmenter(int argc, char **argv);
+extern void run_char_rnn(int argc, char **argv);
+extern void run_tag(int argc, char **argv);
+extern void run_cifar(int argc, char **argv);
+extern void run_go(int argc, char **argv);
+extern void run_art(int argc, char **argv);
+extern void run_super(int argc, char **argv);
+extern void run_lsd(int argc, char **argv);
+
+void average(int argc, char *argv[])
+{
+    char *cfgfile = argv[2];
+    char *outfile = argv[3];
+    gpu_index = -1;
+    network *net = parse_network_cfg(cfgfile);
+    network *sum = parse_network_cfg(cfgfile);
+
+    char *weightfile = argv[4];   
+    load_weights(sum, weightfile);
+
+    int i, j;
+    int n = argc - 5;
+    for(i = 0; i < n; ++i){
+        weightfile = argv[i+5];   
+        load_weights(net, weightfile);
+        for(j = 0; j < net->n; ++j){
+            layer l = net->layers[j];
+            layer out = sum->layers[j];
+            if(l.type == CONVOLUTIONAL){
+                int num = l.n*l.c*l.size*l.size;
+                axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
+                axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
+                if(l.batch_normalize){
+                    axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
+                    axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
+                    axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
+                }
+            }
+            if(l.type == CONNECTED){
+                axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
+                axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
+            }
+        }
+    }
+    n = n+1;
+    for(j = 0; j < net->n; ++j){
+        layer l = sum->layers[j];
+        if(l.type == CONVOLUTIONAL){
+            int num = l.n*l.c*l.size*l.size;
+            scal_cpu(l.n, 1./n, l.biases, 1);
+            scal_cpu(num, 1./n, l.weights, 1);
+                if(l.batch_normalize){
+                    scal_cpu(l.n, 1./n, l.scales, 1);
+                    scal_cpu(l.n, 1./n, l.rolling_mean, 1);
+                    scal_cpu(l.n, 1./n, l.rolling_variance, 1);
+                }
+        }
+        if(l.type == CONNECTED){
+            scal_cpu(l.outputs, 1./n, l.biases, 1);
+            scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
+        }
+    }
+    save_weights(sum, outfile);
+}
+
+long numops(network *net)
+{
+    int i;
+    long ops = 0;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL){
+            ops += 2l * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w;
+        } else if(l.type == CONNECTED){
+            ops += 2l * l.inputs * l.outputs;
+        } else if (l.type == RNN){
+            ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
+            ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
+            ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
+        } else if (l.type == GRU){
+            ops += 2l * l.uz->inputs * l.uz->outputs;
+            ops += 2l * l.uh->inputs * l.uh->outputs;
+            ops += 2l * l.ur->inputs * l.ur->outputs;
+            ops += 2l * l.wz->inputs * l.wz->outputs;
+            ops += 2l * l.wh->inputs * l.wh->outputs;
+            ops += 2l * l.wr->inputs * l.wr->outputs;
+        } else if (l.type == LSTM){
+            ops += 2l * l.uf->inputs * l.uf->outputs;
+            ops += 2l * l.ui->inputs * l.ui->outputs;
+            ops += 2l * l.ug->inputs * l.ug->outputs;
+            ops += 2l * l.uo->inputs * l.uo->outputs;
+            ops += 2l * l.wf->inputs * l.wf->outputs;
+            ops += 2l * l.wi->inputs * l.wi->outputs;
+            ops += 2l * l.wg->inputs * l.wg->outputs;
+            ops += 2l * l.wo->inputs * l.wo->outputs;
+        }
+    }
+    return ops;
+}
+
+void speed(char *cfgfile, int tics)
+{
+    if (tics == 0) tics = 1000;
+    network *net = parse_network_cfg(cfgfile);
+    set_batch_network(net, 1);
+    int i;
+    double time=what_time_is_it_now();
+    image im = make_image(net->w, net->h, net->c*net->batch);
+    for(i = 0; i < tics; ++i){
+        network_predict(net, im.data);
+    }
+    double t = what_time_is_it_now() - time;
+    long ops = numops(net);
+    printf("\n%d evals, %f Seconds\n", tics, t);
+    printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
+    printf("FLOPS: %.2f Bn\n", (float)ops/1000000000.*tics/t);
+    printf("Speed: %f sec/eval\n", t/tics);
+    printf("Speed: %f Hz\n", tics/t);
+}
+
+void operations(char *cfgfile)
+{
+    gpu_index = -1;
+    network *net = parse_network_cfg(cfgfile);
+    long ops = numops(net);
+    printf("Floating Point Operations: %ld\n", ops);
+    printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
+}
+
+void oneoff(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = parse_network_cfg(cfgfile);
+    int oldn = net->layers[net->n - 2].n;
+    int c = net->layers[net->n - 2].c;
+    scal_cpu(oldn*c, .1, net->layers[net->n - 2].weights, 1);
+    scal_cpu(oldn, 0, net->layers[net->n - 2].biases, 1);
+    net->layers[net->n - 2].n = 11921;
+    net->layers[net->n - 2].biases += 5;
+    net->layers[net->n - 2].weights += 5*c;
+    if(weightfile){
+        load_weights(net, weightfile);
+    }
+    net->layers[net->n - 2].biases -= 5;
+    net->layers[net->n - 2].weights -= 5*c;
+    net->layers[net->n - 2].n = oldn;
+    printf("%d\n", oldn);
+    layer l = net->layers[net->n - 2];
+    copy_cpu(l.n/3, l.biases, 1, l.biases +   l.n/3, 1);
+    copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
+    copy_cpu(l.n/3*l.c, l.weights, 1, l.weights +   l.n/3*l.c, 1);
+    copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
+    *net->seen = 0;
+    save_weights(net, outfile);
+}
+
+void oneoff2(char *cfgfile, char *weightfile, char *outfile, int l)
+{
+    gpu_index = -1;
+    network *net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights_upto(net, weightfile, 0, net->n);
+        load_weights_upto(net, weightfile, l, net->n);
+    }
+    *net->seen = 0;
+    save_weights_upto(net, outfile, net->n);
+}
+
+void partial(char *cfgfile, char *weightfile, char *outfile, int max)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 1);
+    save_weights_upto(net, outfile, max);
+}
+
+void print_weights(char *cfgfile, char *weightfile, int n)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 1);
+    layer l = net->layers[n];
+    int i, j;
+    //printf("[");
+    for(i = 0; i < l.n; ++i){
+        //printf("[");
+        for(j = 0; j < l.size*l.size*l.c; ++j){
+            //if(j > 0) printf(",");
+            printf("%g ", l.weights[i*l.size*l.size*l.c + j]);
+        }
+        printf("\n");
+        //printf("]%s\n", (i == l.n-1)?"":",");
+    }
+    //printf("]");
+}
+
+void rescale_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL){
+            rescale_weights(l, 2, -.5);
+            break;
+        }
+    }
+    save_weights(net, outfile);
+}
+
+void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL){
+            rgbgr_weights(l);
+            break;
+        }
+    }
+    save_weights(net, outfile);
+}
+
+void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for (i = 0; i < net->n; ++i) {
+        layer l = net->layers[i];
+        if (l.type == CONVOLUTIONAL && l.batch_normalize) {
+            denormalize_convolutional_layer(l);
+        }
+        if (l.type == CONNECTED && l.batch_normalize) {
+            denormalize_connected_layer(l);
+        }
+        if (l.type == GRU && l.batch_normalize) {
+            denormalize_connected_layer(*l.input_z_layer);
+            denormalize_connected_layer(*l.input_r_layer);
+            denormalize_connected_layer(*l.input_h_layer);
+            denormalize_connected_layer(*l.state_z_layer);
+            denormalize_connected_layer(*l.state_r_layer);
+            denormalize_connected_layer(*l.state_h_layer);
+        }
+    }
+    save_weights(net, outfile);
+}
+
+layer normalize_layer(layer l, int n)
+{
+    int j;
+    l.batch_normalize=1;
+    l.scales = calloc(n, sizeof(float));
+    for(j = 0; j < n; ++j){
+        l.scales[j] = 1;
+    }
+    l.rolling_mean = calloc(n, sizeof(float));
+    l.rolling_variance = calloc(n, sizeof(float));
+    return l;
+}
+
+void normalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL && !l.batch_normalize){
+            net->layers[i] = normalize_layer(l, l.n);
+        }
+        if (l.type == CONNECTED && !l.batch_normalize) {
+            net->layers[i] = normalize_layer(l, l.outputs);
+        }
+        if (l.type == GRU && l.batch_normalize) {
+            *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
+            *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
+            *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
+            *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
+            *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
+            *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
+            net->layers[i].batch_normalize=1;
+        }
+    }
+    save_weights(net, outfile);
+}
+
+void statistics_net(char *cfgfile, char *weightfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for (i = 0; i < net->n; ++i) {
+        layer l = net->layers[i];
+        if (l.type == CONNECTED && l.batch_normalize) {
+            printf("Connected Layer %d\n", i);
+            statistics_connected_layer(l);
+        }
+        if (l.type == GRU && l.batch_normalize) {
+            printf("GRU Layer %d\n", i);
+            printf("Input Z\n");
+            statistics_connected_layer(*l.input_z_layer);
+            printf("Input R\n");
+            statistics_connected_layer(*l.input_r_layer);
+            printf("Input H\n");
+            statistics_connected_layer(*l.input_h_layer);
+            printf("State Z\n");
+            statistics_connected_layer(*l.state_z_layer);
+            printf("State R\n");
+            statistics_connected_layer(*l.state_r_layer);
+            printf("State H\n");
+            statistics_connected_layer(*l.state_h_layer);
+        }
+        printf("\n");
+    }
+}
+
+void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    int i;
+    for (i = 0; i < net->n; ++i) {
+        layer l = net->layers[i];
+        if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {
+            denormalize_convolutional_layer(l);
+            net->layers[i].batch_normalize=0;
+        }
+        if (l.type == CONNECTED && l.batch_normalize) {
+            denormalize_connected_layer(l);
+            net->layers[i].batch_normalize=0;
+        }
+        if (l.type == GRU && l.batch_normalize) {
+            denormalize_connected_layer(*l.input_z_layer);
+            denormalize_connected_layer(*l.input_r_layer);
+            denormalize_connected_layer(*l.input_h_layer);
+            denormalize_connected_layer(*l.state_z_layer);
+            denormalize_connected_layer(*l.state_r_layer);
+            denormalize_connected_layer(*l.state_h_layer);
+            l.input_z_layer->batch_normalize = 0;
+            l.input_r_layer->batch_normalize = 0;
+            l.input_h_layer->batch_normalize = 0;
+            l.state_z_layer->batch_normalize = 0;
+            l.state_r_layer->batch_normalize = 0;
+            l.state_h_layer->batch_normalize = 0;
+            net->layers[i].batch_normalize=0;
+        }
+    }
+    save_weights(net, outfile);
+}
+
+void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    image *ims = get_weights(net->layers[0]);
+    int n = net->layers[0].n;
+    int z;
+    for(z = 0; z < num; ++z){
+        image im = make_image(h, w, 3);
+        fill_image(im, .5);
+        int i;
+        for(i = 0; i < 100; ++i){
+            image r = copy_image(ims[rand()%n]);
+            rotate_image_cw(r, rand()%4);
+            random_distort_image(r, 1, 1.5, 1.5);
+            int dx = rand()%(w-r.w);
+            int dy = rand()%(h-r.h);
+            ghost_image(r, im, dx, dy);
+            free_image(r);
+        }
+        char buff[256];
+        sprintf(buff, "%s/gen_%d", prefix, z);
+        save_image(im, buff);
+        free_image(im);
+    }
+}
+
+void visualize(char *cfgfile, char *weightfile)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    visualize_network(net);
+}
+
+int main(int argc, char **argv)
+{
+    //test_resize("data/bad.jpg");
+    //test_box();
+    //test_convolutional_layer();
+    if(argc < 2){
+        fprintf(stderr, "usage: %s <function>\n", argv[0]);
+        return 0;
+    }
+    gpu_index = find_int_arg(argc, argv, "-i", 0);
+    if(find_arg(argc, argv, "-nogpu")) {
+        gpu_index = -1;
+    }
+
+#ifndef GPU
+    gpu_index = -1;
+#else
+    if(gpu_index >= 0){
+        cuda_set_device(gpu_index);
+    }
+#endif
+
+    if (0 == strcmp(argv[1], "average")){
+        average(argc, argv);
+    } else if (0 == strcmp(argv[1], "yolo")){
+        run_yolo(argc, argv);
+    } else if (0 == strcmp(argv[1], "super")){
+        run_super(argc, argv);
+    } else if (0 == strcmp(argv[1], "lsd")){
+        run_lsd(argc, argv);
+    } else if (0 == strcmp(argv[1], "detector")){
+        run_detector(argc, argv);
+    } else if (0 == strcmp(argv[1], "detect")){
+        float thresh = find_float_arg(argc, argv, "-thresh", .5);
+        char *filename = (argc > 4) ? argv[4]: 0;
+        char *outfile = find_char_arg(argc, argv, "-out", 0);
+        int fullscreen = find_arg(argc, argv, "-fullscreen");
+        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
+    } else if (0 == strcmp(argv[1], "cifar")){
+        run_cifar(argc, argv);
+    } else if (0 == strcmp(argv[1], "go")){
+        run_go(argc, argv);
+    } else if (0 == strcmp(argv[1], "rnn")){
+        run_char_rnn(argc, argv);
+    } else if (0 == strcmp(argv[1], "coco")){
+        run_coco(argc, argv);
+    } else if (0 == strcmp(argv[1], "classify")){
+        predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
+    } else if (0 == strcmp(argv[1], "classifier")){
+        run_classifier(argc, argv);
+    } else if (0 == strcmp(argv[1], "regressor")){
+        run_regressor(argc, argv);
+    } else if (0 == strcmp(argv[1], "isegmenter")){
+        run_isegmenter(argc, argv);
+    } else if (0 == strcmp(argv[1], "segmenter")){
+        run_segmenter(argc, argv);
+    } else if (0 == strcmp(argv[1], "art")){
+        run_art(argc, argv);
+    } else if (0 == strcmp(argv[1], "tag")){
+        run_tag(argc, argv);
+    } else if (0 == strcmp(argv[1], "3d")){
+        composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
+    } else if (0 == strcmp(argv[1], "test")){
+        test_resize(argv[2]);
+    } else if (0 == strcmp(argv[1], "nightmare")){
+        run_nightmare(argc, argv);
+    } else if (0 == strcmp(argv[1], "rgbgr")){
+        rgbgr_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "reset")){
+        reset_normalize_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "denormalize")){
+        denormalize_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "statistics")){
+        statistics_net(argv[2], argv[3]);
+    } else if (0 == strcmp(argv[1], "normalize")){
+        normalize_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "rescale")){
+        rescale_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "ops")){
+        operations(argv[2]);
+    } else if (0 == strcmp(argv[1], "speed")){
+        speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
+    } else if (0 == strcmp(argv[1], "oneoff")){
+        oneoff(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "oneoff2")){
+        oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
+    } else if (0 == strcmp(argv[1], "print")){
+        print_weights(argv[2], argv[3], atoi(argv[4]));
+    } else if (0 == strcmp(argv[1], "partial")){
+        partial(argv[2], argv[3], argv[4], atoi(argv[5]));
+    } else if (0 == strcmp(argv[1], "average")){
+        average(argc, argv);
+    } else if (0 == strcmp(argv[1], "visualize")){
+        visualize(argv[2], (argc > 3) ? argv[3] : 0);
+    } else if (0 == strcmp(argv[1], "mkimg")){
+        mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
+    } else if (0 == strcmp(argv[1], "imtest")){
+        test_resize(argv[2]);
+    } else {
+        fprintf(stderr, "Not an option: %s\n", argv[1]);
+    }
+    return 0;
+}
+
diff --git a/hanzi_detection/examples/detector-scipy-opencv.py b/hanzi_detection/examples/detector-scipy-opencv.py
new file mode 100755
index 0000000..684d6e1
--- /dev/null
+++ b/hanzi_detection/examples/detector-scipy-opencv.py
@@ -0,0 +1,56 @@
+# Stupid python path shit.
+# Instead just add darknet.py to somewhere in your python path
+# OK actually that might not be a great idea, idk, work in progress
+# Use at your own risk. or don't, i don't care
+
+from scipy.misc import imread
+import cv2
+
+def array_to_image(arr):
+    arr = arr.transpose(2,0,1)
+    c = arr.shape[0]
+    h = arr.shape[1]
+    w = arr.shape[2]
+    arr = (arr/255.0).flatten()
+    data = dn.c_array(dn.c_float, arr)
+    im = dn.IMAGE(w,h,c,data)
+    return im
+
+def detect2(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
+    boxes = dn.make_boxes(net)
+    probs = dn.make_probs(net)
+    num =   dn.num_boxes(net)
+    dn.network_detect(net, image, thresh, hier_thresh, nms, boxes, probs)
+    res = []
+    for j in range(num):
+        for i in range(meta.classes):
+            if probs[j][i] > 0:
+                res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
+    res = sorted(res, key=lambda x: -x[1])
+    dn.free_ptrs(dn.cast(probs, dn.POINTER(dn.c_void_p)), num)
+    return res
+
+import sys, os
+sys.path.append(os.path.join(os.getcwd(),'python/'))
+
+import nine as dn
+
+# Darknet
+net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
+meta = dn.load_meta("cfg/coco.data")
+r = dn.detect(net, meta, "data/dog.jpg")
+print r
+
+# scipy
+arr= imread('data/dog.jpg')
+im = array_to_image(arr)
+r = detect2(net, meta, im)
+print r
+
+# OpenCV
+arr = cv2.imread('data/dog.jpg')
+im = array_to_image(arr)
+dn.rgbgr_image(im)
+r = detect2(net, meta, im)
+print r
+
diff --git a/hanzi_detection/examples/detector.c b/hanzi_detection/examples/detector.c
new file mode 100755
index 0000000..318f7fb
--- /dev/null
+++ b/hanzi_detection/examples/detector.c
@@ -0,0 +1,850 @@
+#include "darknet.h"
+
+static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
+
+
+void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+{
+    list *options = read_data_cfg(datacfg);
+    char *train_images = option_find_str(options, "train", "data/train.list");
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network **nets = calloc(ngpus, sizeof(network));
+
+    srand(time(0));
+    int seed = rand();
+    int i;
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    data train, buffer;
+
+    layer l = net->layers[net->n - 1];
+
+    int classes = l.classes;
+    float jitter = l.jitter;
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = get_base_args(net);
+    args.coords = l.coords;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.classes = classes;
+    args.jitter = jitter;
+    args.num_boxes = l.max_boxes;
+    args.d = &buffer;
+    args.type = DETECTION_DATA;
+    //args.type = INSTANCE_DATA;
+    args.threads = 64;
+
+    pthread_t load_thread = load_data(args);
+    double time;
+    int count = 0;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net->max_batches){
+        if(l.random && count++%10 == 0){
+            printf("Resizing\n");
+            int dim = (rand() % 10 + 10) * 32;
+            if (get_current_batch(net)+200 > net->max_batches) dim = 608;
+            //int dim = (rand() % 4 + 16) * 32;
+            printf("%d\n", dim);
+            args.w = dim;
+            args.h = dim;
+
+            pthread_join(load_thread, 0);
+            train = buffer;
+            free_data(train);
+            load_thread = load_data(args);
+
+            #pragma omp parallel for
+            for(i = 0; i < ngpus; ++i){
+                resize_network(nets[i], dim, dim);
+            }
+            net = nets[0];
+        }
+        time=what_time_is_it_now();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        /*
+           int k;
+           for(k = 0; k < l.max_boxes; ++k){
+           box b = float_to_box(train.y.vals[10] + 1 + k*5);
+           if(!b.x) break;
+           printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
+           }
+         */
+        /*
+           int zz;
+           for(zz = 0; zz < train.X.cols; ++zz){
+           image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]);
+           int k;
+           for(k = 0; k < l.max_boxes; ++k){
+           box b = float_to_box(train.y.vals[zz] + k*5, 1);
+           printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
+           draw_bbox(im, b, 1, 1,0,0);
+           }
+           show_image(im, "truth11");
+           cvWaitKey(0);
+           save_image(im, "truth11");
+           }
+         */
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+
+        time=what_time_is_it_now();
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        i = get_current_batch(net);
+        printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
+        if(i%100==0){
+#ifdef GPU
+            if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+        if(i%10000==0 || (i < 1000 && i%100 == 0)){
+#ifdef GPU
+            if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+#ifdef GPU
+    if(ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+
+static int get_coco_image_id(char *filename)
+{
+    char *p = strrchr(filename, '/');
+    char *c = strrchr(filename, '_');
+    if(c) p = c;
+    return atoi(p+1);
+}
+
+static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
+{
+    int i, j;
+    int image_id = get_coco_image_id(image_path);
+    for(i = 0; i < num_boxes; ++i){
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
+
+        if (xmin < 0) xmin = 0;
+        if (ymin < 0) ymin = 0;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        float bx = xmin;
+        float by = ymin;
+        float bw = xmax - xmin;
+        float bh = ymax - ymin;
+
+        for(j = 0; j < classes; ++j){
+            if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
+        }
+    }
+}
+
+void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
+{
+    int i, j;
+    for(i = 0; i < total; ++i){
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2. + 1;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2. + 1;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2. + 1;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2. + 1;
+
+        if (xmin < 1) xmin = 1;
+        if (ymin < 1) ymin = 1;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        for(j = 0; j < classes; ++j){
+            if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
+                    xmin, ymin, xmax, ymax);
+        }
+    }
+}
+
+void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
+{
+    int i, j;
+    for(i = 0; i < total; ++i){
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
+
+        if (xmin < 0) xmin = 0;
+        if (ymin < 0) ymin = 0;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        for(j = 0; j < classes; ++j){
+            int class = j;
+            if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, dets[i].prob[class],
+                    xmin, ymin, xmax, ymax);
+        }
+    }
+}
+
+void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
+{
+    int j;
+    list *options = read_data_cfg(datacfg);
+    char *valid_images = option_find_str(options, "valid", "data/train.list");
+    char *name_list = option_find_str(options, "names", "data/names.list");
+    char *prefix = option_find_str(options, "results", "results");
+    char **names = get_labels(name_list);
+    char *mapf = option_find_str(options, "map", 0);
+    int *map = 0;
+    if (mapf) map = read_map(mapf);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 2);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    list *plist = get_paths(valid_images);
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+
+    char buff[1024];
+    char *type = option_find_str(options, "eval", "voc");
+    FILE *fp = 0;
+    FILE **fps = 0;
+    int coco = 0;
+    int imagenet = 0;
+    if(0==strcmp(type, "coco")){
+        if(!outfile) outfile = "coco_results";
+        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
+        fp = fopen(buff, "w");
+        fprintf(fp, "[\n");
+        coco = 1;
+    } else if(0==strcmp(type, "imagenet")){
+        if(!outfile) outfile = "imagenet-detection";
+        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
+        fp = fopen(buff, "w");
+        imagenet = 1;
+        classes = 200;
+    } else {
+        if(!outfile) outfile = "comp4_det_test_";
+        fps = calloc(classes, sizeof(FILE *));
+        for(j = 0; j < classes; ++j){
+            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
+            fps[j] = fopen(buff, "w");
+        }
+    }
+
+    int m = plist->size;
+    int i=0;
+    int t;
+
+    float thresh = .005;
+    float nms = .45;
+
+    int nthreads = 4;
+    image *val = calloc(nthreads, sizeof(image));
+    image *val_resized = calloc(nthreads, sizeof(image));
+    image *buf = calloc(nthreads, sizeof(image));
+    image *buf_resized = calloc(nthreads, sizeof(image));
+    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+    image input = make_image(net->w, net->h, net->c*2);
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    //args.type = IMAGE_DATA;
+    args.type = LETTERBOX_DATA;
+
+    for(t = 0; t < nthreads; ++t){
+        args.path = paths[i+t];
+        args.im = &buf[t];
+        args.resized = &buf_resized[t];
+        thr[t] = load_data_in_thread(args);
+    }
+    double start = what_time_is_it_now();
+    for(i = nthreads; i < m+nthreads; i += nthreads){
+        fprintf(stderr, "%d\n", i);
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            pthread_join(thr[t], 0);
+            val[t] = buf[t];
+            val_resized[t] = buf_resized[t];
+        }
+        for(t = 0; t < nthreads && i+t < m; ++t){
+            args.path = paths[i+t];
+            args.im = &buf[t];
+            args.resized = &buf_resized[t];
+            thr[t] = load_data_in_thread(args);
+        }
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            char *path = paths[i+t-nthreads];
+            char *id = basecfg(path);
+            copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1);
+            flip_image(val_resized[t]);
+            copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1);
+
+            network_predict(net, input.data);
+            int w = val[t].w;
+            int h = val[t].h;
+            int num = 0;
+            detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &num);
+            if (nms) do_nms_sort(dets, num, classes, nms);
+            if (coco){
+                print_cocos(fp, path, dets, num, classes, w, h);
+            } else if (imagenet){
+                print_imagenet_detections(fp, i+t-nthreads+1, dets, num, classes, w, h);
+            } else {
+                print_detector_detections(fps, id, dets, num, classes, w, h);
+            }
+            free_detections(dets, num);
+            free(id);
+            free_image(val[t]);
+            free_image(val_resized[t]);
+        }
+    }
+    for(j = 0; j < classes; ++j){
+        if(fps) fclose(fps[j]);
+    }
+    if(coco){
+        fseek(fp, -2, SEEK_CUR); 
+        fprintf(fp, "\n]\n");
+        fclose(fp);
+    }
+    fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
+}
+
+
+void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
+{
+    int j;
+    list *options = read_data_cfg(datacfg);
+    char *valid_images = option_find_str(options, "valid", "data/train.list");
+    char *name_list = option_find_str(options, "names", "data/names.list");
+    char *prefix = option_find_str(options, "results", "results");
+    char **names = get_labels(name_list);
+    char *mapf = option_find_str(options, "map", 0);
+    int *map = 0;
+    if (mapf) map = read_map(mapf);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    list *plist = get_paths(valid_images);
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+
+    char buff[1024];
+    char *type = option_find_str(options, "eval", "voc");
+    FILE *fp = 0;
+    FILE **fps = 0;
+    int coco = 0;
+    int imagenet = 0;
+    if(0==strcmp(type, "coco")){
+        if(!outfile) outfile = "coco_results";
+        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
+        fp = fopen(buff, "w");
+        fprintf(fp, "[\n");
+        coco = 1;
+    } else if(0==strcmp(type, "imagenet")){
+        if(!outfile) outfile = "imagenet-detection";
+        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
+        fp = fopen(buff, "w");
+        imagenet = 1;
+        classes = 200;
+    } else {
+        if(!outfile) outfile = "comp4_det_test_";
+        fps = calloc(classes, sizeof(FILE *));
+        for(j = 0; j < classes; ++j){
+            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
+            fps[j] = fopen(buff, "w");
+        }
+    }
+
+
+    int m = plist->size;
+    int i=0;
+    int t;
+
+    float thresh = .005;
+    float nms = .45;
+
+    int nthreads = 4;
+    image *val = calloc(nthreads, sizeof(image));
+    image *val_resized = calloc(nthreads, sizeof(image));
+    image *buf = calloc(nthreads, sizeof(image));
+    image *buf_resized = calloc(nthreads, sizeof(image));
+    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    //args.type = IMAGE_DATA;
+    args.type = LETTERBOX_DATA;
+
+    for(t = 0; t < nthreads; ++t){
+        args.path = paths[i+t];
+        args.im = &buf[t];
+        args.resized = &buf_resized[t];
+        thr[t] = load_data_in_thread(args);
+    }
+    double start = what_time_is_it_now();
+    for(i = nthreads; i < m+nthreads; i += nthreads){
+        fprintf(stderr, "%d\n", i);
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            pthread_join(thr[t], 0);
+            val[t] = buf[t];
+            val_resized[t] = buf_resized[t];
+        }
+        for(t = 0; t < nthreads && i+t < m; ++t){
+            args.path = paths[i+t];
+            args.im = &buf[t];
+            args.resized = &buf_resized[t];
+            thr[t] = load_data_in_thread(args);
+        }
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            char *path = paths[i+t-nthreads];
+            char *id = basecfg(path);
+            float *X = val_resized[t].data;
+            network_predict(net, X);
+            int w = val[t].w;
+            int h = val[t].h;
+            int nboxes = 0;
+            detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &nboxes);
+            if (nms) do_nms_sort(dets, nboxes, classes, nms);
+            if (coco){
+                print_cocos(fp, path, dets, nboxes, classes, w, h);
+            } else if (imagenet){
+                print_imagenet_detections(fp, i+t-nthreads+1, dets, nboxes, classes, w, h);
+            } else {
+                print_detector_detections(fps, id, dets, nboxes, classes, w, h);
+            }
+            free_detections(dets, nboxes);
+            free(id);
+            free_image(val[t]);
+            free_image(val_resized[t]);
+        }
+    }
+    for(j = 0; j < classes; ++j){
+        if(fps) fclose(fps[j]);
+    }
+    if(coco){
+        fseek(fp, -2, SEEK_CUR); 
+        fprintf(fp, "\n]\n");
+        fclose(fp);
+    }
+    fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
+}
+
+void validate_detector_recall(char *cfgfile, char *weightfile)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    list *plist = get_paths("data/coco_val_5k.list");
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+
+    int j, k;
+
+    int m = plist->size;
+    int i=0;
+
+    float thresh = .001;
+    float iou_thresh = .5;
+    float nms = .4;
+
+    int total = 0;
+    int correct = 0;
+    int proposals = 0;
+    float avg_iou = 0;
+
+    for(i = 0; i < m; ++i){
+        char *path = paths[i];
+        image orig = load_image_color(path, 0, 0);
+        image sized = resize_image(orig, net->w, net->h);
+        char *id = basecfg(path);
+        network_predict(net, sized.data);
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes);
+        if (nms) do_nms_obj(dets, nboxes, 1, nms);
+
+        char labelpath[4096];
+        find_replace(path, "images", "labels", labelpath);
+        find_replace(labelpath, "JPEGImages", "labels", labelpath);
+        find_replace(labelpath, ".jpg", ".txt", labelpath);
+        find_replace(labelpath, ".JPEG", ".txt", labelpath);
+
+        int num_labels = 0;
+        box_label *truth = read_boxes(labelpath, &num_labels);
+        for(k = 0; k < nboxes; ++k){
+            if(dets[k].objectness > thresh){
+                ++proposals;
+            }
+        }
+        for (j = 0; j < num_labels; ++j) {
+            ++total;
+            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+            float best_iou = 0;
+            for(k = 0; k < l.w*l.h*l.n; ++k){
+                float iou = box_iou(dets[k].bbox, t);
+                if(dets[k].objectness > thresh && iou > best_iou){
+                    best_iou = iou;
+                }
+            }
+            avg_iou += best_iou;
+            if(best_iou > iou_thresh){
+                ++correct;
+            }
+        }
+
+        fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
+        free(id);
+        free_image(orig);
+        free_image(sized);
+    }
+}
+
+
+void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
+{
+    list *options = read_data_cfg(datacfg);
+    char *name_list = option_find_str(options, "names", "data/names.list");
+    char **names = get_labels(name_list);
+
+    image **alphabet = load_alphabet();
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+    double time;
+    char buff[256];
+    char *input = buff;
+    float nms=.45;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        } else {
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input,0,0);
+        image sized = letterbox_image(im, net->w, net->h);
+        //image sized = resize_image(im, net->w, net->h);
+        //image sized2 = resize_max(im, net->w);
+        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
+        //resize_network(net, sized.w, sized.h);
+        layer l = net->layers[net->n-1];
+
+
+        float *X = sized.data;
+        time=what_time_is_it_now();
+        network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
+        //printf("%d\n", nboxes);
+        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
+        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+        draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
+        free_detections(dets, nboxes);
+        if(outfile){
+            save_image(im, outfile);
+        }
+        else{
+            save_image(im, "predictions");
+#ifdef OPENCV
+            make_window("predictions", 512, 512, 0);
+            show_image(im, "predictions", 0);
+#endif
+        }
+
+        free_image(im);
+        free_image(sized);
+        if (filename) break;
+    }
+}
+
+/*
+void censor_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
+{
+#ifdef OPENCV
+    char *base = basecfg(cfgfile);
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    CvCapture * cap;
+
+    int w = 1280;
+    int h = 720;
+
+    if(filename){
+        cap = cvCaptureFromFile(filename);
+    }else{
+        cap = cvCaptureFromCAM(cam_index);
+    }
+
+    if(w){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
+    }
+    if(h){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
+    }
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    cvNamedWindow(base, CV_WINDOW_NORMAL); 
+    cvResizeWindow(base, 512, 512);
+    float fps = 0;
+    int i;
+    float nms = .45;
+
+    while(1){
+        image in = get_image_from_stream(cap);
+        //image in_s = resize_image(in, net->w, net->h);
+        image in_s = letterbox_image(in, net->w, net->h);
+        layer l = net->layers[net->n-1];
+
+        float *X = in_s.data;
+        network_predict(net, X);
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 0, &nboxes);
+        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
+        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+
+        for(i = 0; i < nboxes; ++i){
+            if(dets[i].prob[class] > thresh){
+                box b = dets[i].bbox;
+                int left  = b.x-b.w/2.;
+                int top   = b.y-b.h/2.;
+                censor_image(in, left, top, b.w, b.h);
+            }
+        }
+        show_image(in, base);
+        cvWaitKey(10);
+        free_detections(dets, nboxes);
+
+
+        free_image(in_s);
+        free_image(in);
+
+
+        float curr = 0;
+        fps = .9*fps + .1*curr;
+        for(i = 0; i < skip; ++i){
+            image in = get_image_from_stream(cap);
+            free_image(in);
+        }
+    }
+    #endif
+}
+
+void extract_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
+{
+#ifdef OPENCV
+    char *base = basecfg(cfgfile);
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    CvCapture * cap;
+
+    int w = 1280;
+    int h = 720;
+
+    if(filename){
+        cap = cvCaptureFromFile(filename);
+    }else{
+        cap = cvCaptureFromCAM(cam_index);
+    }
+
+    if(w){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
+    }
+    if(h){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
+    }
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    cvNamedWindow(base, CV_WINDOW_NORMAL); 
+    cvResizeWindow(base, 512, 512);
+    float fps = 0;
+    int i;
+    int count = 0;
+    float nms = .45;
+
+    while(1){
+        image in = get_image_from_stream(cap);
+        //image in_s = resize_image(in, net->w, net->h);
+        image in_s = letterbox_image(in, net->w, net->h);
+        layer l = net->layers[net->n-1];
+
+        show_image(in, base);
+
+        int nboxes = 0;
+        float *X = in_s.data;
+        network_predict(net, X);
+        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 1, &nboxes);
+        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
+        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+
+        for(i = 0; i < nboxes; ++i){
+            if(dets[i].prob[class] > thresh){
+                box b = dets[i].bbox;
+                int size = b.w*in.w > b.h*in.h ? b.w*in.w : b.h*in.h;
+                int dx  = b.x*in.w-size/2.;
+                int dy  = b.y*in.h-size/2.;
+                image bim = crop_image(in, dx, dy, size, size);
+                char buff[2048];
+                sprintf(buff, "results/extract/%07d", count);
+                ++count;
+                save_image(bim, buff);
+                free_image(bim);
+            }
+        }
+        free_detections(dets, nboxes);
+
+
+        free_image(in_s);
+        free_image(in);
+
+
+        float curr = 0;
+        fps = .9*fps + .1*curr;
+        for(i = 0; i < skip; ++i){
+            image in = get_image_from_stream(cap);
+            free_image(in);
+        }
+    }
+    #endif
+}
+*/
+
+/*
+void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets)
+{
+    network_predict_image(net, im);
+    layer l = net->layers[net->n-1];
+    int nboxes = num_boxes(net);
+    fill_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 0, dets);
+    if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+}
+*/
+
+void run_detector(int argc, char **argv)
+{
+    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+    float thresh = find_float_arg(argc, argv, "-thresh", .5);
+    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int frame_skip = find_int_arg(argc, argv, "-s", 0);
+    int avg = find_int_arg(argc, argv, "-avg", 3);
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    char *outfile = find_char_arg(argc, argv, "-out", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+
+    int clear = find_arg(argc, argv, "-clear");
+    int fullscreen = find_arg(argc, argv, "-fullscreen");
+    int width = find_int_arg(argc, argv, "-w", 0);
+    int height = find_int_arg(argc, argv, "-h", 0);
+    int fps = find_int_arg(argc, argv, "-fps", 0);
+    //int class = find_int_arg(argc, argv, "-class", 0);
+
+    char *datacfg = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);
+    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
+    else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);
+    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
+    else if(0==strcmp(argv[2], "demo")) {
+        list *options = read_data_cfg(datacfg);
+        int classes = option_find_int(options, "classes", 20);
+        char *name_list = option_find_str(options, "names", "data/names.list");
+        char **names = get_labels(name_list);
+        demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);
+    }
+    //else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
+    //else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
+}
diff --git a/hanzi_detection/examples/detector.py b/hanzi_detection/examples/detector.py
new file mode 100755
index 0000000..1ed32d2
--- /dev/null
+++ b/hanzi_detection/examples/detector.py
@@ -0,0 +1,27 @@
+# Stupid python path shit.
+# Instead just add darknet.py to somewhere in your python path
+# OK actually that might not be a great idea, idk, work in progress
+# Use at your own risk. or don't, i don't care
+
+import sys, os
+sys.path.append(os.path.join(os.getcwd(),'python/'))
+
+import nine as dn
+import pdb
+
+dn.set_gpu(0)
+net = dn.load_net("cfg/yolo-thor.cfg", "/home/pjreddie/backup/yolo-thor_final.weights", 0)
+meta = dn.load_meta("cfg/thor.data")
+r = dn.detect(net, meta, "data/bedroom.jpg")
+print r
+
+# And then down here you could detect a lot more images like:
+r = dn.detect(net, meta, "data/eagle.jpg")
+print r
+r = dn.detect(net, meta, "data/giraffe.jpg")
+print r
+r = dn.detect(net, meta, "data/horses.jpg")
+print r
+r = dn.detect(net, meta, "data/person.jpg")
+print r
+
diff --git a/hanzi_detection/examples/dice.c b/hanzi_detection/examples/dice.c
new file mode 100755
index 0000000..f56d76c
--- /dev/null
+++ b/hanzi_detection/examples/dice.c
@@ -0,0 +1,116 @@
+#include "darknet.h"
+
+char *dice_labels[] = {"face1","face2","face3","face4","face5","face6"};
+
+void train_dice(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    char *backup_directory = "/home/pjreddie/backup/";
+    printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = 1024;
+    int i = *net.seen/imgs;
+    char **labels = dice_labels;
+    list *plist = get_paths("data/dice/dice.train.list");
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    clock_t time;
+    while(1){
+        ++i;
+        time=clock();
+        data train = load_data_old(paths, imgs, plist->size, labels, 6, net.w, net.h);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen);
+        free_data(train);
+        if((i % 100) == 0) net.learning_rate *= .1;
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
+            save_weights(net, buff);
+        }
+    }
+}
+
+void validate_dice(char *filename, char *weightfile)
+{
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+
+    char **labels = dice_labels;
+    list *plist = get_paths("data/dice/dice.val.list");
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    data val = load_data_old(paths, m, 0, labels, 6, net.w, net.h);
+    float *acc = network_accuracies(net, val, 2);
+    printf("Validation Accuracy: %f, %d images\n", acc[0], m);
+    free_data(val);
+}
+
+void test_dice(char *cfgfile, char *weightfile, char *filename)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    srand(2222222);
+    int i = 0;
+    char **names = dice_labels;
+    char buff[256];
+    char *input = buff;
+    int indexes[6];
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, net.w, net.h);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 6, indexes);
+        for(i = 0; i < 6; ++i){
+            int index = indexes[i];
+            printf("%s: %f\n", names[index], predictions[index]);
+        }
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+void run_dice(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "test")) test_dice(cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_dice(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_dice(cfg, weights);
+}
+
diff --git a/hanzi_detection/examples/go.c b/hanzi_detection/examples/go.c
new file mode 100755
index 0000000..688579d
--- /dev/null
+++ b/hanzi_detection/examples/go.c
@@ -0,0 +1,1370 @@
+#include "darknet.h"
+
+#include <assert.h>
+#include <math.h>
+#include <unistd.h>
+
+int inverted = 1;
+int noi = 1;
+static const int nind = 10;
+int legal_go(float *b, float *ko, int p, int r, int c);
+int check_ko(float *x, float *ko);
+
+typedef struct {
+    char **data;
+    int n;
+} moves;
+
+char *fgetgo(FILE *fp)
+{
+    if(feof(fp)) return 0;
+    size_t size = 96;
+    char *line = malloc(size*sizeof(char));
+    if(size != fread(line, sizeof(char), size, fp)){
+        free(line);
+        return 0;
+    }
+
+    return line;
+}
+
+moves load_go_moves(char *filename)
+{
+    moves m;
+    m.n = 128;
+    m.data = calloc(128, sizeof(char*));
+    FILE *fp = fopen(filename, "rb");
+    int count = 0;
+    char *line = 0;
+    while ((line = fgetgo(fp))) {
+        if (count >= m.n) {
+            m.n *= 2;
+            m.data = realloc(m.data, m.n*sizeof(char*));
+        }
+        m.data[count] = line;
+        ++count;
+    }
+    printf("%d\n", count);
+    m.n = count;
+    m.data = realloc(m.data, count*sizeof(char*));
+    return m;
+}
+
+void string_to_board(char *s, float *board)
+{
+    int i, j;
+    memset(board, 0, 2*19*19*sizeof(float));
+    int count = 0;
+    for(i = 0; i < 91; ++i){
+        char c = s[i];
+        for(j = 0; j < 4; ++j){
+            int me = (c >> (2*j)) & 1;
+            int you = (c >> (2*j + 1)) & 1;
+            if (me) board[count] = 1;
+            else if (you) board[count + 19*19] = 1;
+            ++count;
+            if(count >= 19*19) break;
+        }
+    }
+}
+
+void board_to_string(char *s, float *board)
+{
+    int i, j;
+    memset(s, 0, (19*19/4+1)*sizeof(char));
+    int count = 0;
+    for(i = 0; i < 91; ++i){
+        for(j = 0; j < 4; ++j){
+            int me = (board[count] == 1);
+            int you = (board[count + 19*19] == 1);
+            if (me) s[i] = s[i] | (1<<(2*j));
+            if (you) s[i] = s[i] | (1<<(2*j + 1));
+            ++count;
+            if(count >= 19*19) break;
+        }
+    }
+}
+
+static int occupied(float *b, int i)
+{
+    if (b[i]) return 1;
+    if (b[i+19*19]) return -1;
+    return 0;
+}
+
+data random_go_moves(moves m, int n)
+{
+    data d = {0};
+    d.X = make_matrix(n, 19*19*3);
+    d.y = make_matrix(n, 19*19+2);
+    int i, j;
+    for(i = 0; i < n; ++i){
+        float *board = d.X.vals[i];
+        float *label = d.y.vals[i];
+        char *b = m.data[rand()%m.n];
+        int player = b[0] - '0';
+        int result = b[1] - '0';
+        int row = b[2];
+        int col = b[3];
+        string_to_board(b+4, board);
+        if(player > 0) for(j = 0; j < 19*19; ++j) board[19*19*2 + j] = 1;
+        label[19*19+1] = (player==result);
+        if(row >= 19 || col >= 19){
+            label[19*19] = 1;
+        } else {
+            label[col + 19*row] = 1;
+            if(occupied(board, col + 19*row)) printf("hey\n");
+        }
+
+        int flip = rand()%2;
+        int rotate = rand()%4;
+        image in = float_to_image(19, 19, 3, board);
+        image out = float_to_image(19, 19, 1, label);
+        if(flip){
+            flip_image(in);
+            flip_image(out);
+        }
+        rotate_image_cw(in, rotate);
+        rotate_image_cw(out, rotate);
+    }
+    return d;
+}
+
+
+void train_go(char *cfgfile, char *weightfile, char *filename, int *gpus, int ngpus, int clear)
+{
+    int i;
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    network *net = nets[0];
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+
+    char *backup_directory = "/home/pjreddie/backup/";
+
+    char buff[256];
+    moves m = load_go_moves(filename);
+    //moves m = load_go_moves("games.txt");
+
+    int N = m.n;
+    printf("Moves: %d\n", N);
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        double time=what_time_is_it_now();
+
+        data train = random_go_moves(m, net->batch*net->subdivisions*ngpus);
+        printf("Loaded: %lf seconds\n", what_time_is_it_now() - time);
+        time=what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 10);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        free_data(train);
+
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.95 + loss*.05;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory,base, epoch);
+            save_weights(net, buff);
+
+        }
+        if(get_current_batch(net)%1000 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%10000 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%ld.backup",backup_directory,base,get_current_batch(net));
+            save_weights(net, buff);
+        }
+    }
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free(base);
+}
+
+static void propagate_liberty(float *board, int *lib, int *visited, int row, int col, int side)
+{
+    if (row < 0 || row > 18 || col < 0 || col > 18) return;
+    int index = row*19 + col;
+    if (occupied(board,index) != side) return;
+    if (visited[index]) return;
+    visited[index] = 1;
+    lib[index] += 1;
+    propagate_liberty(board, lib, visited, row+1, col, side);
+    propagate_liberty(board, lib, visited, row-1, col, side);
+    propagate_liberty(board, lib, visited, row, col+1, side);
+    propagate_liberty(board, lib, visited, row, col-1, side);
+}
+
+
+static int *calculate_liberties(float *board)
+{
+    int *lib = calloc(19*19, sizeof(int));
+    int visited[19*19];
+    int i, j;
+    for(j = 0; j < 19; ++j){
+        for(i = 0; i < 19; ++i){
+            memset(visited, 0, 19*19*sizeof(int));
+            int index = j*19 + i;
+            if(!occupied(board,index)){
+                if ((i > 0)  && occupied(board,index - 1)) propagate_liberty(board, lib, visited, j, i-1, occupied(board,index-1));
+                if ((i < 18) && occupied(board,index + 1)) propagate_liberty(board, lib, visited, j, i+1, occupied(board,index+1));
+                if ((j > 0)  && occupied(board,index - 19)) propagate_liberty(board, lib, visited, j-1, i, occupied(board,index-19));
+                if ((j < 18) && occupied(board,index + 19)) propagate_liberty(board, lib, visited, j+1, i, occupied(board,index+19));
+            }
+        }
+    }
+    return lib;
+}
+
+void print_board(FILE *stream, float *board, int player, int *indexes)
+{
+    int i,j,n;
+    fprintf(stream, "   ");
+    for(i = 0; i < 19; ++i){
+        fprintf(stream, "%c ", 'A' + i + 1*(i > 7 && noi));
+    }
+    fprintf(stream, "\n");
+    for(j = 0; j < 19; ++j){
+        fprintf(stream, "%2d", (inverted) ? 19-j : j+1);
+        for(i = 0; i < 19; ++i){
+            int index = j*19 + i;
+            if(indexes){
+                int found = 0;
+                for(n = 0; n < nind; ++n){
+                    if(index == indexes[n]){
+                        found = 1;
+                        /*
+                           if(n == 0) fprintf(stream, "\uff11");
+                           else if(n == 1) fprintf(stream, "\uff12");
+                           else if(n == 2) fprintf(stream, "\uff13");
+                           else if(n == 3) fprintf(stream, "\uff14");
+                           else if(n == 4) fprintf(stream, "\uff15");
+                         */
+                        fprintf(stream, " %d", n+1);
+                    }
+                }
+                if(found) continue;
+            }
+            //if(board[index]*-swap > 0) fprintf(stream, "\u25C9 ");
+            //else if(board[index]*-swap < 0) fprintf(stream, "\u25EF ");
+            if      (occupied(board, index) == player) fprintf(stream, " X");
+            else if (occupied(board, index) ==-player) fprintf(stream, " O");
+            else fprintf(stream, " .");
+        }
+        fprintf(stream, "\n");
+    }
+}
+
+void flip_board(float *board)
+{
+    int i;
+    for(i = 0; i < 19*19; ++i){
+        float swap = board[i];
+        board[i] = board[i+19*19];
+        board[i+19*19] = swap;
+        board[i+19*19*2] = 1-board[i+19*19*2];
+    }
+}
+
+float predict_move2(network *net, float *board, float *move, int multi)
+{
+    float *output = network_predict(net, board);
+    copy_cpu(19*19+1, output, 1, move, 1);
+    float result = output[19*19 + 1];
+    int i;
+    if(multi){
+        image bim = float_to_image(19, 19, 3, board);
+        for(i = 1; i < 8; ++i){
+            rotate_image_cw(bim, i);
+            if(i >= 4) flip_image(bim);
+
+            float *output = network_predict(net, board);
+            image oim = float_to_image(19, 19, 1, output);
+            result += output[19*19 + 1];
+
+            if(i >= 4) flip_image(oim);
+            rotate_image_cw(oim, -i);
+
+            axpy_cpu(19*19+1, 1, output, 1, move, 1);
+
+            if(i >= 4) flip_image(bim);
+            rotate_image_cw(bim, -i);
+        }
+        result = result/8;
+        scal_cpu(19*19+1, 1./8., move, 1);
+    }
+    for(i = 0; i < 19*19; ++i){
+        if(board[i] || board[i+19*19]) move[i] = 0;
+    }
+    return result;
+}
+
+static void remove_connected(float *b, int *lib, int p, int r, int c)
+{
+    if (r < 0 || r >= 19 || c < 0 || c >= 19) return;
+    if (occupied(b, r*19 + c) != p) return;
+    if (lib[r*19 + c] != 1) return;
+    b[r*19 + c] = 0;
+    b[19*19 + r*19 + c] = 0;
+    remove_connected(b, lib, p, r+1, c);
+    remove_connected(b, lib, p, r-1, c);
+    remove_connected(b, lib, p, r, c+1);
+    remove_connected(b, lib, p, r, c-1);
+}
+
+
+void move_go(float *b, int p, int r, int c)
+{
+    int *l = calculate_liberties(b);
+    if(p > 0) b[r*19 + c] = 1;
+    else b[19*19 + r*19 + c] = 1;
+    remove_connected(b, l, -p, r+1, c);
+    remove_connected(b, l, -p, r-1, c);
+    remove_connected(b, l, -p, r, c+1);
+    remove_connected(b, l, -p, r, c-1);
+    free(l);
+}
+
+int compare_board(float *a, float *b)
+{
+    if(memcmp(a, b, 19*19*3*sizeof(float)) == 0) return 1;
+    return 0;
+}
+
+typedef struct mcts_tree{
+    float *board;
+    struct mcts_tree **children;
+    float *prior;
+    int *visit_count;
+    float *value;
+    float *mean;
+    float *prob;
+    int total_count;
+    float result;
+    int done;
+    int pass;
+} mcts_tree;
+
+void free_mcts(mcts_tree *root)
+{
+    if(!root) return;
+    int i;
+    free(root->board);
+    for(i = 0; i < 19*19+1; ++i){
+        if(root->children[i]) free_mcts(root->children[i]);
+    }
+    free(root->children);
+    free(root->prior);
+    free(root->visit_count);
+    free(root->value);
+    free(root->mean);
+    free(root->prob);
+    free(root);
+}
+
+float *network_predict_rotations(network *net, float *next)
+{
+    int n = net->batch;
+    float *in = calloc(19*19*3*n, sizeof(float));
+    image im = float_to_image(19, 19, 3, next);
+    int i,j;
+    int *inds = random_index_order(0, 8);
+    for(j = 0; j < n; ++j){
+        i = inds[j];
+        rotate_image_cw(im, i);
+        if(i >= 4) flip_image(im);
+        memcpy(in + 19*19*3*j, im.data, 19*19*3*sizeof(float));
+        if(i >= 4) flip_image(im);
+        rotate_image_cw(im, -i);
+    }
+    float *pred = network_predict(net, in);
+    for(j = 0; j < n; ++j){
+        i = inds[j];
+        image im = float_to_image(19, 19, 1, pred + j*(19*19 + 2));
+        if(i >= 4) flip_image(im);
+        rotate_image_cw(im, -i);
+        if(j > 0){
+            axpy_cpu(19*19+2, 1, im.data, 1, pred, 1);
+        }
+    }
+    free(in);
+    free(inds);
+    scal_cpu(19*19+2, 1./n, pred, 1);
+    return pred;
+}
+
+mcts_tree *expand(float *next, float *ko, network *net)
+{
+    mcts_tree *root = calloc(1, sizeof(mcts_tree));
+    root->board = next;
+    root->children = calloc(19*19+1, sizeof(mcts_tree*));
+    root->prior = calloc(19*19 + 1, sizeof(float));
+    root->prob = calloc(19*19 + 1, sizeof(float));
+    root->mean = calloc(19*19 + 1, sizeof(float));
+    root->value = calloc(19*19 + 1, sizeof(float));
+    root->visit_count = calloc(19*19 + 1, sizeof(int));
+    root->total_count = 1;
+    int i;
+    float *pred = network_predict_rotations(net, next);
+    copy_cpu(19*19+1, pred, 1, root->prior, 1);
+    float val = 2*pred[19*19 + 1] - 1;
+    root->result = val;
+    for(i = 0; i < 19*19+1; ++i) {
+        root->visit_count[i] = 0;
+        root->value[i] = 0;
+        root->mean[i] = val;
+        if(i < 19*19 && occupied(next, i)){
+            root->value[i] = -1;
+            root->mean[i] = -1;
+            root->prior[i] = 0;
+        }
+    }
+    //print_board(stderr, next, flip?-1:1, 0);
+    return root;
+}
+
+float *copy_board(float *board)
+{
+    float *next = calloc(19*19*3, sizeof(float));
+    copy_cpu(19*19*3, board, 1, next, 1);
+    return next;
+}
+
+float select_mcts(mcts_tree *root, network *net, float *prev, float cpuct)
+{
+    if(root->done) return -root->result;
+    int i;
+    float max = -1000;
+    int max_i = 0;
+    for(i = 0; i < 19*19+1; ++i){
+        root->prob[i] = root->mean[i] + cpuct*root->prior[i] * sqrt(root->total_count) / (1. + root->visit_count[i]);
+        if(root->prob[i] > max){
+            max = root->prob[i];
+            max_i = i;
+        }
+    }
+    float val;
+    i = max_i;
+    root->visit_count[i]++;
+    root->total_count++;
+    if (root->children[i]) {
+        val = select_mcts(root->children[i], net, root->board, cpuct);
+    } else {
+        if(max_i < 19*19 && !legal_go(root->board, prev, 1, max_i/19, max_i%19)) {
+            root->mean[i]  = -1;
+            root->value[i] = -1;
+            root->prior[i] = 0;
+            --root->total_count;
+            return select_mcts(root, net, prev, cpuct);
+            //printf("Detected ko\n");
+            //getchar();
+        } else {
+            float *next = copy_board(root->board);
+            if (max_i < 19*19) {
+                move_go(next, 1, max_i / 19, max_i % 19);
+            }
+            flip_board(next);
+            root->children[i] = expand(next, root->board, net);
+            val = -root->children[i]->result;
+            if(max_i == 19*19){
+                root->children[i]->pass = 1;
+                if (root->pass){
+                    root->children[i]->done = 1;
+                }
+            }
+        }
+    }
+    root->value[i] += val;
+    root->mean[i] = root->value[i]/root->visit_count[i];
+    return -val;
+}
+
+mcts_tree *run_mcts(mcts_tree *tree, network *net, float *board, float *ko, int player, int n, float cpuct, float secs)
+{
+    int i;
+    double t = what_time_is_it_now();
+    if(player < 0) flip_board(board);
+    if(!tree) tree = expand(copy_board(board), ko, net);
+    assert(compare_board(tree->board, board));
+    for(i = 0; i < n; ++i){
+        if (secs > 0 && (what_time_is_it_now() - t) > secs) break;
+        int max_i = max_int_index(tree->visit_count, 19*19+1);
+        if (tree->visit_count[max_i] >= n) break;
+        select_mcts(tree, net, ko, cpuct);
+    }
+    if(player < 0) flip_board(board);
+    //fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t);
+    return tree;
+}
+
+mcts_tree *move_mcts(mcts_tree *tree, int index)
+{
+    if(index < 0 || index > 19*19 || !tree || !tree->children[index]) {
+        free_mcts(tree);
+        tree = 0;
+    } else {
+        mcts_tree *swap = tree;
+        tree = tree->children[index];
+        swap->children[index] = 0;
+        free_mcts(swap);
+    }
+    return tree;
+}
+
+typedef struct {
+    float value;
+    float mcts;
+    int row;
+    int col;
+} move;
+
+move pick_move(mcts_tree *tree, float temp, int player)
+{
+    int i;
+    float probs[19*19+1] = {0};
+    move m = {0};
+    double sum = 0;
+    /*
+    for(i = 0; i < 19*19+1; ++i){
+        probs[i] = tree->visit_count[i];
+    }
+    */
+    //softmax(probs, 19*19+1, temp, 1, probs);
+    for(i = 0; i < 19*19+1; ++i){
+        sum += pow(tree->visit_count[i], 1./temp);
+    }
+    for(i = 0; i < 19*19+1; ++i){
+        probs[i] = pow(tree->visit_count[i], 1./temp) / sum;
+    }
+
+    int index = sample_array(probs, 19*19+1);
+    m.row = index / 19;
+    m.col = index % 19;
+    m.value = (tree->result+1.)/2.;
+    m.mcts  = (tree->mean[index]+1.)/2.;
+
+    int indexes[nind];
+    top_k(probs, 19*19+1, nind, indexes);
+    print_board(stderr, tree->board, player, indexes);
+
+    fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", index/19, index%19, tree->result, tree->prior[index], probs[index], tree->mean[index], (tree->children[index])?tree->children[index]->result:0, tree->visit_count[index]);
+    int ind = max_index(probs, 19*19+1);
+    fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, tree->result, tree->prior[ind], probs[ind], tree->mean[ind], (tree->children[ind])?tree->children[ind]->result:0, tree->visit_count[ind]);
+    ind = max_index(tree->prior, 19*19+1);
+    fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, tree->result, tree->prior[ind], probs[ind], tree->mean[ind], (tree->children[ind])?tree->children[ind]->result:0, tree->visit_count[ind]);
+    return m;
+}
+
+/*
+   float predict_move(network *net, float *board, float *move, int multi, float *ko, float temp)
+   {
+
+   int i;
+
+   int max_v = 0;
+   int max_i = 0;
+   for(i = 0; i < 19*19+1; ++i){
+   if(root->visit_count[i] > max_v){
+   max_v = root->visit_count[i];
+   max_i = i;
+   }
+   }
+   fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t);
+   int ind = max_index(root->mean, 19*19+1);
+   fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", max_i/19, max_i%19, root->result, root->prior[max_i], root->prob[max_i], root->mean[max_i], (root->children[max_i])?root->children[max_i]->result:0, root->visit_count[max_i]);
+   fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, root->result, root->prior[ind], root->prob[ind], root->mean[ind], (root->children[ind])?root->children[ind]->result:0, root->visit_count[ind]);
+   ind = max_index(root->prior, 19*19+1);
+   fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, root->result, root->prior[ind], root->prob[ind], root->mean[ind], (root->children[ind])?root->children[ind]->result:0, root->visit_count[ind]);
+   if(root->result < -.9 && root->mean[max_i] < -.9) return -1000.f;
+
+   float val = root->result;
+   free_mcts(root);
+   return val;
+   }
+ */
+
+static int makes_safe_go(float *b, int *lib, int p, int r, int c){
+    if (r < 0 || r >= 19 || c < 0 || c >= 19) return 0;
+    if (occupied(b,r*19 + c) == -p){
+        if (lib[r*19 + c] > 1) return 0;
+        else return 1;
+    }
+    if (!occupied(b,r*19 + c)) return 1;
+    if (lib[r*19 + c] > 1) return 1;
+    return 0;
+}
+
+int suicide_go(float *b, int p, int r, int c)
+{
+    int *l = calculate_liberties(b);
+    int safe = 0;
+    safe = safe || makes_safe_go(b, l, p, r+1, c);
+    safe = safe || makes_safe_go(b, l, p, r-1, c);
+    safe = safe || makes_safe_go(b, l, p, r, c+1);
+    safe = safe || makes_safe_go(b, l, p, r, c-1);
+    free(l);
+    return !safe;
+}
+
+int check_ko(float *x, float *ko)
+{
+    if(!ko) return 0;
+    float curr[19*19*3];
+    copy_cpu(19*19*3, x, 1, curr, 1);
+    if(curr[19*19*2] != ko[19*19*2]) flip_board(curr);
+    if(compare_board(curr, ko)) return 1;
+    return 0;
+}
+
+int legal_go(float *b, float *ko, int p, int r, int c)
+{
+    if (occupied(b, r*19+c)) return 0;
+    float curr[19*19*3];
+    copy_cpu(19*19*3, b, 1, curr, 1);
+    move_go(curr, p, r, c);
+    if(check_ko(curr, ko)) return 0;
+    if(suicide_go(b, p, r, c)) return 0;
+    return 1;
+}
+
+/*
+   move generate_move(mcts_tree *root, network *net, int player, float *board, int multi, float temp, float *ko, int print)
+   {
+   move m = {0};
+//root = run_mcts(tree, network *net, float *board, float *ko, int n, float cpuct)
+int i, j;
+int empty = 1;
+for(i = 0; i < 19*19; ++i){
+if (occupied(board, i)) {
+empty = 0;
+break;
+}
+}
+if(empty) {
+m.value = .5;
+m.mcts = .5;
+m.row = 3;
+m.col = 15;
+return m;
+}
+
+float move[362];
+if (player < 0) flip_board(board);
+float result = predict_move(net, board, move, multi, ko, temp);
+if (player < 0) flip_board(board);
+if(result == -1000.f) return -2;
+
+for(i = 0; i < 19; ++i){
+for(j = 0; j < 19; ++j){
+if (!legal_go(board, ko, player, i, j)) move[i*19 + j] = 0;
+}
+}
+
+int indexes[nind];
+top_k(move, 19*19+1, nind, indexes);
+
+
+int max = max_index(move, 19*19+1);
+int row = max / 19;
+int col = max % 19;
+int index = sample_array(move, 19*19+1);
+
+if(print){
+top_k(move, 19*19+1, nind, indexes);
+for(i = 0; i < nind; ++i){
+if (!move[indexes[i]]) indexes[i] = -1;
+}
+print_board(stderr, board, 1, indexes);
+fprintf(stderr, "%s To Move\n", player > 0 ? "X" : "O");
+fprintf(stderr, "%.2f%% Win Chance\n", (result+1)/2*100);
+for(i = 0; i < nind; ++i){
+int index = indexes[i];
+int row = index / 19;
+int col = index % 19;
+if(row == 19){
+fprintf(stderr, "%d: Pass, %.2f%%\n", i+1, move[index]*100);
+} else {
+fprintf(stderr, "%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
+}
+}
+}
+if (row == 19) return -1;
+
+if (suicide_go(board, player, row, col)){
+return -1; 
+}
+
+if (suicide_go(board, player, index/19, index%19)){
+index = max;
+}
+if (index == 19*19) return -1;
+return index;
+}
+*/
+
+void valid_go(char *cfgfile, char *weightfile, int multi, char *filename)
+{
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+
+    float *board = calloc(19*19*3, sizeof(float));
+    float *move = calloc(19*19+2, sizeof(float));
+    // moves m = load_go_moves("/home/pjreddie/backup/go.test");
+    moves m = load_go_moves(filename);
+
+    int N = m.n;
+    int i,j;
+    int correct = 0;
+    for (i = 0; i <N; ++i) {
+        char *b = m.data[i];
+        int player = b[0] - '0';
+        //int result = b[1] - '0';
+        int row = b[2];
+        int col = b[3];
+        int truth = col + 19*row;
+        string_to_board(b+4, board);
+        if(player > 0) for(j = 0; j < 19*19; ++j) board[19*19*2 + j] = 1;
+        predict_move2(net, board, move, multi);
+        int index = max_index(move, 19*19+1);
+        if(index == truth) ++correct;
+        printf("%d Accuracy %f\n", i, (float) correct/(i+1));
+    }
+}
+
+int print_game(float *board, FILE *fp)
+{
+    int i, j;
+    int count = 3;
+    fprintf(fp, "komi 6.5\n");
+    fprintf(fp, "boardsize 19\n");
+    fprintf(fp, "clear_board\n");
+    for(j = 0; j < 19; ++j){
+        for(i = 0; i < 19; ++i){
+            if(occupied(board,j*19 + i) == 1) fprintf(fp, "play black %c%d\n", 'A'+i+(i>=8), 19-j);
+            if(occupied(board,j*19 + i) == -1) fprintf(fp, "play white %c%d\n", 'A'+i+(i>=8), 19-j);
+            if(occupied(board,j*19 + i)) ++count;
+        }
+    }
+    return count;
+}
+
+
+int stdin_ready()
+{
+    fd_set readfds;
+    FD_ZERO(&readfds);
+
+    struct timeval timeout;
+    timeout.tv_sec = 0;
+    timeout.tv_usec = 0;
+    FD_SET(STDIN_FILENO, &readfds);
+
+    if (select(1, &readfds, NULL, NULL, &timeout)){
+        return 1;
+    }
+    return 0;
+}
+
+mcts_tree *ponder(mcts_tree *tree, network *net, float *b, float *ko, int player, float cpuct)
+{
+    double t = what_time_is_it_now();
+    int count = 0;
+    if (tree) count = tree->total_count;
+    while(!stdin_ready()){
+        if (what_time_is_it_now() - t > 120) break;
+        tree = run_mcts(tree, net, b, ko, player, 100000, cpuct, .1);
+    }
+    fprintf(stderr, "Pondered %d moves...\n", tree->total_count - count);
+    return tree;
+}
+
+void engine_go(char *filename, char *weightfile, int mcts_iters, float secs, float temp, float cpuct, int anon, int resign)
+{
+    mcts_tree *root = 0;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+    float *board = calloc(19*19*3, sizeof(float));
+    flip_board(board);
+    float *one = calloc(19*19*3, sizeof(float));
+    float *two = calloc(19*19*3, sizeof(float));
+    int ponder_player = 0;
+    int passed = 0;
+    int move_num = 0;
+    int main_time = 0;
+    int byo_yomi_time = 0;
+    int byo_yomi_stones = 0;
+    int black_time_left = 0;
+    int black_stones_left = 0;
+    int white_time_left = 0;
+    int white_stones_left = 0;
+    float orig_time = secs;
+    int old_ponder = 0;
+    while(1){
+        if(ponder_player){
+            root = ponder(root, net, board, two, ponder_player, cpuct);
+        }
+        old_ponder = ponder_player;
+        ponder_player = 0;
+        char buff[256];
+        int id = 0;
+        int has_id = (scanf("%d", &id) == 1);
+        scanf("%s", buff);
+        if (feof(stdin)) break;
+        fprintf(stderr, "%s\n", buff);
+        char ids[256];
+        sprintf(ids, "%d", id);
+        //fprintf(stderr, "%s\n", buff);
+        if (!has_id) ids[0] = 0;
+        if (!strcmp(buff, "protocol_version")){
+            printf("=%s 2\n\n", ids);
+        } else if (!strcmp(buff, "name")){
+            if(anon){
+                printf("=%s The Fool!\n\n", ids);
+            }else{
+                printf("=%s DarkGo\n\n", ids);
+            }
+        } else if (!strcmp(buff, "time_settings")){
+            ponder_player = old_ponder;
+            scanf("%d %d %d", &main_time, &byo_yomi_time, &byo_yomi_stones);
+            printf("=%s \n\n", ids);
+        } else if (!strcmp(buff, "time_left")){
+            ponder_player = old_ponder;
+            char color[256];
+            int time = 0, stones = 0;
+            scanf("%s %d %d", color, &time, &stones);
+            if (color[0] == 'b' || color[0] == 'B'){
+                black_time_left = time;
+                black_stones_left = stones;
+            } else {
+                white_time_left = time;
+                white_stones_left = stones;
+            }
+            printf("=%s \n\n", ids);
+        } else if (!strcmp(buff, "version")){
+            if(anon){
+                printf("=%s :-DDDD\n\n", ids);
+            }else {
+                printf("=%s 1.0. Want more DarkGo? You can find me on OGS, unlimited games, no waiting! https://online-go.com/user/view/434218\n\n", ids);
+            }
+        } else if (!strcmp(buff, "known_command")){
+            char comm[256];
+            scanf("%s", comm);
+            int known = (!strcmp(comm, "protocol_version") || 
+                    !strcmp(comm, "name") || 
+                    !strcmp(comm, "version") || 
+                    !strcmp(comm, "known_command") || 
+                    !strcmp(comm, "list_commands") || 
+                    !strcmp(comm, "quit") || 
+                    !strcmp(comm, "boardsize") || 
+                    !strcmp(comm, "clear_board") || 
+                    !strcmp(comm, "komi") || 
+                    !strcmp(comm, "final_status_list") || 
+                    !strcmp(comm, "play") || 
+                    !strcmp(comm, "genmove_white") || 
+                    !strcmp(comm, "genmove_black") || 
+                    !strcmp(comm, "fixed_handicap") || 
+                    !strcmp(comm, "genmove"));
+            if(known) printf("=%s true\n\n", ids);
+            else printf("=%s false\n\n", ids);
+        } else if (!strcmp(buff, "list_commands")){
+            printf("=%s protocol_version\nshowboard\nname\nversion\nknown_command\nlist_commands\nquit\nboardsize\nclear_board\nkomi\nplay\ngenmove_black\ngenmove_white\ngenmove\nfinal_status_list\nfixed_handicap\n\n", ids);
+        } else if (!strcmp(buff, "quit")){
+            break;
+        } else if (!strcmp(buff, "boardsize")){
+            int boardsize = 0;
+            scanf("%d", &boardsize);
+            //fprintf(stderr, "%d\n", boardsize);
+            if(boardsize != 19){
+                printf("?%s unacceptable size\n\n", ids);
+            } else {
+                root = move_mcts(root, -1);
+                memset(board, 0, 3*19*19*sizeof(float));
+                flip_board(board);
+                move_num = 0;
+                printf("=%s \n\n", ids);
+            }
+        } else if (!strcmp(buff, "fixed_handicap")){
+            int handicap = 0;
+            scanf("%d", &handicap);
+            int indexes[] = {72, 288, 300, 60, 180, 174, 186, 66, 294};
+            int i;
+            for(i = 0; i < handicap; ++i){
+                board[indexes[i]] = 1;   
+                ++move_num;
+            }
+            root = move_mcts(root, -1);
+        } else if (!strcmp(buff, "clear_board")){
+            passed = 0;
+            memset(board, 0, 3*19*19*sizeof(float));
+            flip_board(board);
+            move_num = 0;
+            root = move_mcts(root, -1);
+            printf("=%s \n\n", ids);
+        } else if (!strcmp(buff, "komi")){
+            float komi = 0;
+            scanf("%f", &komi);
+            printf("=%s \n\n", ids);
+        } else if (!strcmp(buff, "showboard")){
+            printf("=%s \n", ids);
+            print_board(stdout, board, 1, 0);
+            printf("\n");
+        } else if (!strcmp(buff, "play") || !strcmp(buff, "black") || !strcmp(buff, "white")){
+            ++move_num;
+            char color[256];
+            if(!strcmp(buff, "play"))
+            {
+                scanf("%s ", color);
+            } else {
+                scanf(" ");
+                color[0] = buff[0];
+            }
+            char c;
+            int r;
+            int count = scanf("%c%d", &c, &r);
+            int player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1;
+            if((c == 'p' || c == 'P') && count < 2) {
+                passed = 1;
+                printf("=%s \n\n", ids);
+                char *line = fgetl(stdin);
+                free(line);
+                fflush(stdout);
+                fflush(stderr);
+                root = move_mcts(root, 19*19);
+                continue;
+            } else {
+                passed = 0;
+            }
+            if(c >= 'A' && c <= 'Z') c = c - 'A';
+            if(c >= 'a' && c <= 'z') c = c - 'a';
+            if(c >= 8) --c;
+            r = 19 - r;
+            fprintf(stderr, "move: %d %d\n", r, c);
+
+            float *swap = two;
+            two = one;
+            one = swap;
+            move_go(board, player, r, c);
+            copy_cpu(19*19*3, board, 1, one, 1);
+            if(root) fprintf(stderr, "Prior: %f\n", root->prior[r*19 + c]);
+            if(root) fprintf(stderr, "Mean: %f\n", root->mean[r*19 + c]);
+            if(root) fprintf(stderr, "Result: %f\n", root->result);
+            root = move_mcts(root, r*19 + c);
+            if(root) fprintf(stderr, "Visited: %d\n", root->total_count);
+            else fprintf(stderr, "NOT VISITED\n");
+
+            printf("=%s \n\n", ids);
+            //print_board(stderr, board, 1, 0);
+        } else if (!strcmp(buff, "genmove") || !strcmp(buff, "genmove_black") || !strcmp(buff, "genmove_white")){
+            ++move_num;
+            int player = 0;
+            if(!strcmp(buff, "genmove")){
+                char color[256];
+                scanf("%s", color);
+                player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1;
+            } else if (!strcmp(buff, "genmove_black")){
+                player = 1;
+            } else {
+                player = -1;
+            }
+            if(player > 0){
+                if(black_time_left <= 30) secs = 2.5;
+                else secs = orig_time;
+            } else {
+                if(white_time_left <= 30) secs = 2.5;
+                else secs = orig_time;
+            }
+            ponder_player = -player;
+
+            //tree = generate_move(net, player, board, multi, .1, two, 1);
+            double t = what_time_is_it_now();
+            root = run_mcts(root, net, board, two, player, mcts_iters, cpuct, secs);
+            fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t);
+            move m = pick_move(root, temp, player);
+            root = move_mcts(root, m.row*19 + m.col);
+
+
+            if(move_num > resign && m.value < .1 && m.mcts < .1){
+                printf("=%s resign\n\n", ids);
+            } else if(m.row == 19){
+                printf("=%s pass\n\n", ids);
+                passed = 0;
+            } else {
+                int row = m.row;
+                int col = m.col;
+
+                float *swap = two;
+                two = one;
+                one = swap;
+
+                move_go(board, player, row, col);
+                copy_cpu(19*19*3, board, 1, one, 1);
+                row = 19 - row;
+                if (col >= 8) ++col;
+                printf("=%s %c%d\n\n", ids, 'A' + col, row);
+            }
+
+        } else if (!strcmp(buff, "p")){
+            //print_board(board, 1, 0);
+        } else if (!strcmp(buff, "final_status_list")){
+            char type[256];
+            scanf("%s", type);
+            fprintf(stderr, "final_status\n");
+            char *line = fgetl(stdin);
+            free(line);
+            if(type[0] == 'd' || type[0] == 'D'){
+                int i;
+                FILE *f = fopen("game.txt", "w");
+                int count = print_game(board, f);
+                fprintf(f, "%s final_status_list dead\n", ids);
+                fclose(f);
+                FILE *p = popen("./gnugo --mode gtp < game.txt", "r");
+                for(i = 0; i < count; ++i){
+                    free(fgetl(p));
+                    free(fgetl(p));
+                }
+                char *l = 0;
+                while((l = fgetl(p))){
+                    printf("%s\n", l);
+                    free(l);
+                }
+            } else {
+                printf("?%s unknown command\n\n", ids);
+            }
+        } else if (!strcmp(buff, "kgs-genmove_cleanup")){
+            char type[256];
+            scanf("%s", type);
+            fprintf(stderr, "kgs-genmove_cleanup\n");
+            char *line = fgetl(stdin);
+            free(line);
+            int i;
+            FILE *f = fopen("game.txt", "w");
+            int count = print_game(board, f);
+            fprintf(f, "%s kgs-genmove_cleanup %s\n", ids, type);
+            fclose(f);
+            FILE *p = popen("./gnugo --mode gtp < game.txt", "r");
+            for(i = 0; i < count; ++i){
+                free(fgetl(p));
+                free(fgetl(p));
+            }
+            char *l = 0;
+            while((l = fgetl(p))){
+                printf("%s\n", l);
+                free(l);
+            }
+        } else {
+            char *line = fgetl(stdin);
+            free(line);
+            printf("?%s unknown command\n\n", ids);
+        }
+        fflush(stdout);
+        fflush(stderr);
+    }
+    printf("%d %d %d\n",passed, black_stones_left, white_stones_left);
+}
+
+void test_go(char *cfg, char *weights, int multi)
+{
+    int i;
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+    float *board = calloc(19*19*3, sizeof(float));
+    flip_board(board);
+    float *move = calloc(19*19+1, sizeof(float));
+    int color = 1;
+    while(1){
+        float result = predict_move2(net, board, move, multi);
+        printf("%.2f%% Win Chance\n", (result+1)/2*100);
+
+        int indexes[nind];
+        int row, col;
+        top_k(move, 19*19+1, nind, indexes);
+        print_board(stderr, board, color, indexes);
+        for(i = 0; i < nind; ++i){
+            int index = indexes[i];
+            row = index / 19;
+            col = index % 19;
+            if(row == 19){
+                printf("%d: Pass, %.2f%%\n", i+1, move[index]*100);
+            } else {
+                printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
+            }
+        }
+        //if(color == 1) printf("\u25EF Enter move: ");
+        //else printf("\u25C9 Enter move: ");
+        if(color == 1) printf("X Enter move: ");
+        else printf("O Enter move: ");
+
+        char c;
+        char *line = fgetl(stdin);
+        int picked = 1;
+        int dnum = sscanf(line, "%d", &picked);
+        int cnum = sscanf(line, "%c", &c);
+        if (strlen(line) == 0 || dnum) {
+            --picked;
+            if (picked < nind){
+                int index = indexes[picked];
+                row = index / 19;
+                col = index % 19;
+                if(row < 19){
+                    move_go(board, 1, row, col);
+                }
+            }
+        } else if (cnum){
+            if (c <= 'T' && c >= 'A'){
+                int num = sscanf(line, "%c %d", &c, &row);
+                row = (inverted)?19 - row : row-1;
+                col = c - 'A';
+                if (col > 7 && noi) col -= 1;
+                if (num == 2) move_go(board, 1, row, col);
+            } else if (c == 'p') {
+                // Pass
+            } else if(c=='b' || c == 'w'){
+                char g;
+                int num = sscanf(line, "%c %c %d", &g, &c, &row);
+                row = (inverted)?19 - row : row-1;
+                col = c - 'A';
+                if (col > 7 && noi) col -= 1;
+                if (num == 3) {
+                    int mc = (g == 'b') ? 1 : -1;
+                    if (mc == color) {
+                        board[row*19 + col] = 1;
+                    } else {
+                        board[19*19 + row*19 + col] = 1;
+                    }
+                }
+            } else if(c == 'c'){
+                char g;
+                int num = sscanf(line, "%c %c %d", &g, &c, &row);
+                row = (inverted)?19 - row : row-1;
+                col = c - 'A';
+                if (col > 7 && noi) col -= 1;
+                if (num == 3) {
+                    board[row*19 + col] = 0;
+                    board[19*19 + row*19 + col] = 0;
+                }
+            }
+        }
+        free(line);
+        flip_board(board);
+        color = -color;
+    }
+}
+
+float score_game(float *board)
+{
+    int i;
+    FILE *f = fopen("game.txt", "w");
+    int count = print_game(board, f);
+    fprintf(f, "final_score\n");
+    fclose(f);
+    FILE *p = popen("./gnugo --mode gtp < game.txt", "r");
+    for(i = 0; i < count; ++i){
+        free(fgetl(p));
+        free(fgetl(p));
+    }
+    char *l = 0;
+    float score = 0;
+    char player = 0;
+    while((l = fgetl(p))){
+        fprintf(stderr, "%s  \t", l);
+        int n = sscanf(l, "= %c+%f", &player, &score);
+        free(l);
+        if (n == 2) break;
+    }
+    if(player == 'W') score = -score;
+    pclose(p);
+    return score;
+}
+
+void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi)
+{
+    mcts_tree *tree1 = 0;
+    mcts_tree *tree2 = 0;
+    network *net = load_network(filename, weightfile, 0);
+    //set_batch_network(net, 1);
+
+    network *net2;
+    if (f2) {
+        net2 = parse_network_cfg(f2);
+        if(w2){
+            load_weights(net2, w2);
+        }
+    } else {
+        net2 = calloc(1, sizeof(network));
+        *net2 = *net;
+    }
+    srand(time(0));
+    char boards[600][93];
+    int count = 0;
+    //set_batch_network(net, 1);
+    //set_batch_network(net2, 1);
+    float *board = calloc(19*19*3, sizeof(float));
+    flip_board(board);
+    float *one = calloc(19*19*3, sizeof(float));
+    float *two = calloc(19*19*3, sizeof(float));
+    int done = 0;
+    int player = 1;
+    int p1 = 0;
+    int p2 = 0;
+    int total = 0;
+    float temp = .1;
+    int mcts_iters = 500;
+    float cpuct = 5;
+    while(1){
+        if (done){
+            tree1 = move_mcts(tree1, -1);
+            tree2 = move_mcts(tree2, -1);
+            float score = score_game(board);
+            if((score > 0) == (total%2==0)) ++p1;
+            else ++p2;
+            ++total;
+            fprintf(stderr, "Total: %d, Player 1: %f, Player 2: %f\n", total, (float)p1/total, (float)p2/total);
+            sleep(1);
+            /*
+               int i = (score > 0)? 0 : 1;
+               int j;
+               for(; i < count; i += 2){
+               for(j = 0; j < 93; ++j){
+               printf("%c", boards[i][j]);
+               }
+               printf("\n");
+               }
+             */
+            memset(board, 0, 3*19*19*sizeof(float));
+            flip_board(board);
+            player = 1;
+            done = 0;
+            count = 0;
+            fflush(stdout);
+            fflush(stderr);
+        }
+        //print_board(stderr, board, 1, 0);
+        //sleep(1);
+
+        if ((total%2==0) == (player==1)){
+            //mcts_iters = 4500;   
+            cpuct = 5;
+        } else {
+            //mcts_iters = 500;
+            cpuct = 1;
+        }
+        network *use = ((total%2==0) == (player==1)) ? net : net2;
+        mcts_tree *t = ((total%2==0) == (player==1)) ? tree1 : tree2;
+        t = run_mcts(t, use, board, two, player, mcts_iters, cpuct, 0);
+        move m = pick_move(t, temp, player);
+        if(((total%2==0) == (player==1))) tree1 = t;
+        else tree2 = t;
+
+        tree1 = move_mcts(tree1, m.row*19 + m.col);
+        tree2 = move_mcts(tree2, m.row*19 + m.col);
+
+        if(m.row == 19){
+            done = 1;
+            continue;
+        }
+        int row = m.row;
+        int col = m.col;
+
+        float *swap = two;
+        two = one;
+        one = swap;
+
+        if(player < 0) flip_board(board);
+        boards[count][0] = row;
+        boards[count][1] = col;
+        board_to_string(boards[count] + 2, board);
+        if(player < 0) flip_board(board);
+        ++count;
+
+        move_go(board, player, row, col);
+        copy_cpu(19*19*3, board, 1, one, 1);
+
+        player = -player;
+    }
+}
+
+void run_go(int argc, char **argv)
+{
+    //boards_go();
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+    int clear = find_arg(argc, argv, "-clear");
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *c2 = (argc > 5) ? argv[5] : 0;
+    char *w2 = (argc > 6) ? argv[6] : 0;
+    int multi = find_arg(argc, argv, "-multi");
+    int anon = find_arg(argc, argv, "-anon");
+    int iters = find_int_arg(argc, argv, "-iters", 500);
+    int resign = find_int_arg(argc, argv, "-resign", 175);
+    float cpuct = find_float_arg(argc, argv, "-cpuct", 5);
+    float temp = find_float_arg(argc, argv, "-temp", .1);
+    float time = find_float_arg(argc, argv, "-time", 0);
+    if(0==strcmp(argv[2], "train")) train_go(cfg, weights, c2, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "valid")) valid_go(cfg, weights, multi, c2);
+    else if(0==strcmp(argv[2], "self")) self_go(cfg, weights, c2, w2, multi);
+    else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi);
+    else if(0==strcmp(argv[2], "engine")) engine_go(cfg, weights, iters, time, temp, cpuct, anon, resign);
+}
+
+
diff --git a/hanzi_detection/examples/instance-segmenter.c b/hanzi_detection/examples/instance-segmenter.c
new file mode 100755
index 0000000..664e714
--- /dev/null
+++ b/hanzi_detection/examples/instance-segmenter.c
@@ -0,0 +1,267 @@
+#include "darknet.h"
+#include <sys/time.h>
+#include <assert.h>
+
+void normalize_image2(image p);
+void train_isegmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+    image pred = get_network_image(net);
+
+    image embed = pred;
+    embed.c = 3;
+    embed.data += embed.w*embed.h*80;
+
+    int div = net->w/pred.w;
+    assert(pred.w * div == net->w);
+    assert(pred.h * div == net->h);
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.scale = div;
+    args.num_boxes = 90;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+    args.classes = 80;
+
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.type = ISEG_DATA;
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        double time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(display){
+            image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]);
+            image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
+            pred.c = 80;
+            image mask = mask_to_rgb(tr);
+            image prmask = mask_to_rgb(pred);
+            image ecopy = copy_image(embed);
+            normalize_image2(ecopy);
+            show_image(ecopy, "embed", 1);
+            free_image(ecopy);
+
+            show_image(im, "input", 1);
+            show_image(prmask, "pred", 1);
+            show_image(mask, "truth", 100);
+            free_image(mask);
+            free_image(prmask);
+        }
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void predict_isegmenter(char *datafile, char *cfg, char *weights, char *filename)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image sized = letterbox_image(im, net->w, net->h);
+
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        printf("Predicted: %f\n", predictions[0]);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        show_image(sized, "orig", 1);
+        show_image(prmask, "pred", 0);
+        free_image(im);
+        free_image(sized);
+        free_image(prmask);
+        if (filename) break;
+    }
+}
+
+
+void demo_isegmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    printf("Classifier Demo\n");
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    void * cap = open_video_stream(filename, cam_index, 0,0,0);
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    float fps = 0;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        image in_s = letterbox_image(in, net->w, net->h);
+
+        network_predict(net, in_s.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        show_image(prmask, "Segmenter", 10);
+
+        free_image(in_s);
+        free_image(in);
+        free_image(prmask);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void run_isegmenter(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    int display = find_arg(argc, argv, "-display");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) predict_isegmenter(data, cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_isegmenter(data, cfg, weights, gpus, ngpus, clear, display);
+    else if(0==strcmp(argv[2], "demo")) demo_isegmenter(data, cfg, weights, cam_index, filename);
+}
+
+
diff --git a/hanzi_detection/examples/lsd.c b/hanzi_detection/examples/lsd.c
new file mode 100755
index 0000000..4ab944c
--- /dev/null
+++ b/hanzi_detection/examples/lsd.c
@@ -0,0 +1,1378 @@
+#include <math.h>
+#include "darknet.h"
+
+/*
+void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg, char *aweight, int clear)
+{
+#ifdef GPU
+    //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+    char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
+    //char *style_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+    char *style_images = "/home/pjreddie/zelda.txt";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    network fnet = load_network(fcfg, fweight, clear);
+    network gnet = load_network(gcfg, gweight, clear);
+    network anet = load_network(acfg, aweight, clear);
+    char *gbase = basecfg(gcfg);
+    char *abase = basecfg(acfg);
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
+    int imgs = gnet->batch*gnet->subdivisions;
+    int i = *gnet->seen/imgs;
+    data train, tbuffer;
+    data style, sbuffer;
+
+
+    list *slist = get_paths(style_images);
+    char **spaths = (char **)list_to_array(slist);
+
+    list *tlist = get_paths(train_images);
+    char **tpaths = (char **)list_to_array(tlist);
+
+    load_args targs= get_base_args(gnet);
+    targs.paths = tpaths;
+    targs.n = imgs;
+    targs.m = tlist->size;
+    targs.d = &tbuffer;
+    targs.type = CLASSIFICATION_DATA;
+    targs.classes = 1;
+    char *ls[1] = {"zelda"};
+    targs.labels = ls;
+
+    load_args sargs = get_base_args(gnet);
+    sargs.paths = spaths;
+    sargs.n = imgs;
+    sargs.m = slist->size;
+    sargs.d = &sbuffer;
+    sargs.type = CLASSIFICATION_DATA;
+    sargs.classes = 1;
+    sargs.labels = ls;
+
+    pthread_t tload_thread = load_data_in_thread(targs);
+    pthread_t sload_thread = load_data_in_thread(sargs);
+    clock_t time;
+
+    float aloss_avg = -1;
+    float floss_avg = -1;
+
+    fnet->train=1;
+    int x_size = fnet->inputs*fnet->batch;
+    int y_size = fnet->truths*fnet->batch;
+    float *X = calloc(x_size, sizeof(float));
+    float *y = calloc(y_size, sizeof(float));
+
+
+    int ax_size = anet->inputs*anet->batch;
+    int ay_size = anet->truths*anet->batch;
+    fill_gpu(ay_size, .9, anet->truth_gpu, 1);
+    anet->delta_gpu = cuda_make_array(0, ax_size);
+    anet->train = 1;
+
+    int gx_size = gnet->inputs*gnet->batch;
+    int gy_size = gnet->truths*gnet->batch;
+    gstate.input = cuda_make_array(0, gx_size);
+    gstate.truth = 0;
+    gstate.delta = 0;
+    gstate.train = 1;
+
+    while (get_current_batch(gnet) < gnet->max_batches) {
+        i += 1;
+        time=clock();
+        pthread_join(tload_thread, 0);
+        pthread_join(sload_thread, 0);
+        train = tbuffer;
+        style = sbuffer;
+        tload_thread = load_data_in_thread(targs);
+        sload_thread = load_data_in_thread(sargs);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        data generated = copy_data(train);
+        time=clock();
+
+        int j, k;
+        float floss = 0;
+        for(j = 0; j < fnet->subdivisions; ++j){
+            layer imlayer = gnet->layers[gnet->n - 1];
+            get_next_batch(train, fnet->batch, j*fnet->batch, X, y);
+
+            cuda_push_array(fstate.input, X, x_size);
+            cuda_push_array(gstate.input, X, gx_size);
+            *gnet->seen += gnet->batch;
+
+            forward_network_gpu(fnet, fstate);
+            float *feats = fnet->layers[fnet->n - 2].output_gpu;
+            copy_gpu(y_size, feats, 1, fstate.truth, 1);
+
+            forward_network_gpu(gnet, gstate);
+            float *gen = gnet->layers[gnet->n-1].output_gpu;
+            copy_gpu(x_size, gen, 1, fstate.input, 1);
+
+            fill_gpu(x_size, 0, fstate.delta, 1);
+            forward_network_gpu(fnet, fstate);
+            backward_network_gpu(fnet, fstate);
+            //HERE
+
+            astate.input = gen;
+            fill_gpu(ax_size, 0, astate.delta, 1);
+            forward_network_gpu(anet, astate);
+            backward_network_gpu(anet, astate);
+
+            float *delta = imlayer.delta_gpu;
+            fill_gpu(x_size, 0, delta, 1);
+            scal_gpu(x_size, 100, astate.delta, 1);
+            scal_gpu(x_size, .001, fstate.delta, 1);
+            axpy_gpu(x_size, 1, fstate.delta, 1, delta, 1);
+            axpy_gpu(x_size, 1, astate.delta, 1, delta, 1);
+
+            //fill_gpu(x_size, 0, delta, 1);
+            //cuda_push_array(delta, X, x_size);
+            //axpy_gpu(x_size, -1, imlayer.output_gpu, 1, delta, 1);
+            //printf("pix error: %f\n", cuda_mag_array(delta, x_size));
+            printf("fea error: %f\n", cuda_mag_array(fstate.delta, x_size));
+            printf("adv error: %f\n", cuda_mag_array(astate.delta, x_size));
+            //axpy_gpu(x_size, 1, astate.delta, 1, delta, 1);
+
+            backward_network_gpu(gnet, gstate);
+
+            floss += get_network_cost(fnet) /(fnet->subdivisions*fnet->batch);
+
+            cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
+            for(k = 0; k < gnet->batch; ++k){
+                int index = j*gnet->batch + k;
+                copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
+                generated.y.vals[index][0] = .1;
+                style.y.vals[index][0] = .9;
+            }
+        }
+
+*/
+/*
+        image sim = float_to_image(anet->w, anet->h, anet->c, style.X.vals[j]);
+        show_image(sim, "style");
+        cvWaitKey(0);
+        */
+        /*
+
+        harmless_update_network_gpu(anet);
+
+        data merge = concat_data(style, generated);
+        randomize_data(merge);
+        float aloss = train_network(anet, merge);
+
+        update_network_gpu(gnet);
+
+        free_data(merge);
+        free_data(train);
+        free_data(generated);
+        free_data(style);
+        if (aloss_avg < 0) aloss_avg = aloss;
+        if (floss_avg < 0) floss_avg = floss;
+        aloss_avg = aloss_avg*.9 + aloss*.1;
+        floss_avg = floss_avg*.9 + floss*.1;
+
+        printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, floss, aloss, floss_avg, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, gbase, i);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+            save_weights(anet, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, gbase);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s.backup", backup_directory, abase);
+            save_weights(anet, buff);
+        }
+    }
+#endif
+}
+*/
+
+/*
+void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear)
+{
+#ifdef GPU
+    //char *train_images = "/home/pjreddie/data/coco/train1.txt";
+    //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+    char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfg);
+    char *abase = basecfg(acfg);
+    printf("%s\n", base);
+    network net = load_network(cfg, weight, clear);
+    network anet = load_network(acfg, aweight, clear);
+
+    int i, j, k;
+    layer imlayer = {0};
+    for (i = 0; i < net->n; ++i) {
+        if (net->layers[i].out_c == 3) {
+            imlayer = net->layers[i];
+            break;
+        }
+    }
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = net->batch*net->subdivisions;
+    i = *net->seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+    args.type = CLASSIFICATION_DATA;
+    args.classes = 1;
+    char *ls[1] = {"coco"};
+    args.labels = ls;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+
+    network_state gstate = {0};
+    gstate.index = 0;
+    gstate.net = net;
+    int x_size = get_network_input_size(net)*net->batch;
+    int y_size = x_size;
+    gstate.input = cuda_make_array(0, x_size);
+    gstate.truth = cuda_make_array(0, y_size);
+    gstate.delta = 0;
+    gstate.train = 1;
+    float *pixs = calloc(x_size, sizeof(float));
+    float *graypixs = calloc(x_size, sizeof(float));
+    float *y = calloc(y_size, sizeof(float));
+
+    network_state astate = {0};
+    astate.index = 0;
+    astate.net = anet;
+    int ay_size = get_network_output_size(anet)*anet->batch;
+    astate.input = 0;
+    astate.truth = 0;
+    astate.delta = 0;
+    astate.train = 1;
+
+    float *imerror = cuda_make_array(0, imlayer.outputs);
+    float *ones_gpu = cuda_make_array(0, ay_size);
+    fill_gpu(ay_size, .9, ones_gpu, 1);
+
+    float aloss_avg = -1;
+    float gloss_avg = -1;
+
+    //data generated = copy_data(train);
+
+    while (get_current_batch(net) < net->max_batches) {
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        data gray = copy_data(train);
+        for(j = 0; j < imgs; ++j){
+            image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
+            grayscale_image_3c(gim);
+            train.y.vals[j][0] = .9;
+
+            image yim = float_to_image(net->w, net->h, net->c, train.X.vals[j]);
+            //rgb_to_yuv(yim);
+        }
+        time=clock();
+        float gloss = 0;
+
+        for(j = 0; j < net->subdivisions; ++j){
+            get_next_batch(train, net->batch, j*net->batch, pixs, y);
+            get_next_batch(gray, net->batch, j*net->batch, graypixs, y);
+            cuda_push_array(gstate.input, graypixs, x_size);
+            cuda_push_array(gstate.truth, pixs, y_size);
+            */
+            /*
+            image origi = float_to_image(net->w, net->h, 3, pixs);
+            image grayi = float_to_image(net->w, net->h, 3, graypixs);
+            show_image(grayi, "gray");
+            show_image(origi, "orig");
+            cvWaitKey(0);
+            */
+            /*
+            *net->seen += net->batch;
+            forward_network_gpu(net, gstate);
+
+            fill_gpu(imlayer.outputs, 0, imerror, 1);
+            astate.input = imlayer.output_gpu;
+            astate.delta = imerror;
+            astate.truth = ones_gpu;
+            forward_network_gpu(anet, astate);
+            backward_network_gpu(anet, astate);
+
+            scal_gpu(imlayer.outputs, .1, net->layers[net->n-1].delta_gpu, 1);
+
+            backward_network_gpu(net, gstate);
+
+            scal_gpu(imlayer.outputs, 1000, imerror, 1);
+
+            printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
+            printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs));
+
+            axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
+
+            gloss += get_network_cost(net) /(net->subdivisions*net->batch);
+
+            cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
+            for(k = 0; k < net->batch; ++k){
+                int index = j*net->batch + k;
+                copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
+                gray.y.vals[index][0] = .1;
+            }
+        }
+        harmless_update_network_gpu(anet);
+
+        data merge = concat_data(train, gray);
+        randomize_data(merge);
+        float aloss = train_network(anet, merge);
+
+        update_network_gpu(net);
+        update_network_gpu(anet);
+        free_data(merge);
+        free_data(train);
+        free_data(gray);
+        if (aloss_avg < 0) aloss_avg = aloss;
+        aloss_avg = aloss_avg*.9 + aloss*.1;
+        gloss_avg = gloss_avg*.9 + gloss*.1;
+
+        printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+            save_weights(anet, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+            sprintf(buff, "%s/%s.backup", backup_directory, abase);
+            save_weights(anet, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+#endif
+}
+*/
+
+void slerp(float *start, float *end, float s, int n, float *out)
+{
+    float omega = acos(dot_cpu(n, start, 1, end, 1));
+    float so = sin(omega);
+    fill_cpu(n, 0, out, 1);
+    axpy_cpu(n, sin((1-s)*omega)/so, start, 1, out, 1);
+    axpy_cpu(n, sin(s*omega)/so, end, 1, out, 1);
+
+    float mag = mag_array(out, n);
+    scale_array(out, n, 1./mag);
+}
+
+image random_unit_vector_image(int w, int h, int c)
+{
+    image im = make_image(w, h, c);
+    int i;
+    for(i = 0; i < im.w*im.h*im.c; ++i){
+        im.data[i] = rand_normal();
+    }
+    float mag = mag_array(im.data, im.w*im.h*im.c);
+    scale_array(im.data, im.w*im.h*im.c, 1./mag);
+    return im;
+}
+
+void inter_dcgan(char *cfgfile, char *weightfile)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    int i, imlayer = 0;
+
+    for (i = 0; i < net->n; ++i) {
+        if (net->layers[i].out_c == 3) {
+            imlayer = i;
+            printf("%d\n", i);
+            break;
+        }
+    }
+    image start = random_unit_vector_image(net->w, net->h, net->c);
+    image end = random_unit_vector_image(net->w, net->h, net->c);
+        image im = make_image(net->w, net->h, net->c);
+        image orig = copy_image(start);
+
+    int c = 0;
+    int count = 0;
+    int max_count = 15;
+    while(1){
+        ++c;
+        
+        if(count == max_count){
+            count = 0;
+            free_image(start);
+            start = end;
+            end = random_unit_vector_image(net->w, net->h, net->c);
+            if(c > 300){
+                end = orig;
+            }
+            if(c>300 + max_count) return;
+        }
+        ++count;
+
+        slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);
+
+        float *X = im.data;
+        time=clock();
+        network_predict(net, X);
+        image out = get_network_image_layer(net, imlayer);
+        //yuv_to_rgb(out);
+        normalize_image(out);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        //char buff[256];
+        sprintf(buff, "out%05d", c);
+        save_image(out, "out");
+        save_image(out, buff);
+        show_image(out, "out", 0);
+    }
+}
+
+void test_dcgan(char *cfgfile, char *weightfile)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    int imlayer = 0;
+
+    imlayer = net->n-1;
+
+    while(1){
+        image im = make_image(net->w, net->h, net->c);
+        int i;
+        for(i = 0; i < im.w*im.h*im.c; ++i){
+            im.data[i] = rand_normal();
+        }
+        //float mag = mag_array(im.data, im.w*im.h*im.c);
+        //scale_array(im.data, im.w*im.h*im.c, 1./mag);
+
+        float *X = im.data;
+        time=clock();
+        network_predict(net, X);
+        image out = get_network_image_layer(net, imlayer);
+        //yuv_to_rgb(out);
+        normalize_image(out);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        save_image(out, "out");
+        show_image(out, "out", 0);
+
+        free_image(im);
+    }
+}
+
+void set_network_alpha_beta(network *net, float alpha, float beta)
+{
+    int i;
+    for(i = 0; i < net->n; ++i){
+        if(net->layers[i].type == SHORTCUT){
+            net->layers[i].alpha = alpha;
+            net->layers[i].beta = beta;
+        }
+    }
+}
+
+void train_prog(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch)
+{
+#ifdef GPU
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfg);
+    char *abase = basecfg(acfg);
+    printf("%s\n", base);
+    network *gnet = load_network(cfg, weight, clear);
+    network *anet = load_network(acfg, aweight, clear);
+
+    int i, j, k;
+    layer imlayer = gnet->layers[gnet->n-1];
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
+    int imgs = gnet->batch*gnet->subdivisions;
+    i = *gnet->seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args= get_base_args(anet);
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+    args.type = CLASSIFICATION_DATA;
+    args.threads=16;
+    args.classes = 1;
+    char *ls[2] = {"imagenet", "zzzzzzzz"};
+    args.labels = ls;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+
+    gnet->train = 1;
+    anet->train = 1;
+
+    int x_size = gnet->inputs*gnet->batch;
+    int y_size = gnet->truths*gnet->batch;
+    float *imerror = cuda_make_array(0, y_size);
+
+    float aloss_avg = -1;
+
+    if (maxbatch == 0) maxbatch = gnet->max_batches;
+    while (get_current_batch(gnet) < maxbatch) {
+        {
+            int cb = get_current_batch(gnet);
+            float alpha = (float) cb / (maxbatch/2);
+            if(alpha > 1) alpha = 1;
+            float beta = 1 - alpha;
+            printf("%f %f\n", alpha, beta);
+            set_network_alpha_beta(gnet, alpha, beta);
+            set_network_alpha_beta(anet, beta, alpha);
+        }
+
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        data gen = copy_data(train);
+        for (j = 0; j < imgs; ++j) {
+            train.y.vals[j][0] = 1;
+            gen.y.vals[j][0] = 0;
+        }
+        time=clock();
+
+        for (j = 0; j < gnet->subdivisions; ++j) {
+            get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0);
+            int z;
+            for(z = 0; z < x_size; ++z){
+                gnet->input[z] = rand_normal();
+            }
+            /*
+               for(z = 0; z < gnet->batch; ++z){
+               float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
+               scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
+               }
+             */
+            *gnet->seen += gnet->batch;
+            forward_network(gnet);
+
+            fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
+            fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
+            copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
+            anet->delta_gpu = imerror;
+            forward_network(anet);
+            backward_network(anet);
+
+            //float genaloss = *anet->cost / anet->batch;
+
+            scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
+            scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
+
+            axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
+
+            backward_network(gnet);
+
+            for(k = 0; k < gnet->batch; ++k){
+                int index = j*gnet->batch + k;
+                copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1);
+            }
+        }
+        harmless_update_network_gpu(anet);
+
+        data merge = concat_data(train, gen);
+        float aloss = train_network(anet, merge);
+
+#ifdef OPENCV
+        if(display){
+            image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
+            image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
+            show_image(im, "gen", 1);
+            show_image(im2, "train", 1);
+            save_image(im, "gen");
+            save_image(im2, "train");
+        }
+#endif
+
+        update_network_gpu(gnet);
+
+        free_data(merge);
+        free_data(train);
+        free_data(gen);
+        if (aloss_avg < 0) aloss_avg = aloss;
+        aloss_avg = aloss_avg*.9 + aloss*.1;
+
+        printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
+        if(i%10000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+            save_weights(anet, buff);
+        }
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s.backup", backup_directory, abase);
+            save_weights(anet, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(gnet, buff);
+#endif
+}
+
+void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch)
+{
+#ifdef GPU
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfg);
+    char *abase = basecfg(acfg);
+    printf("%s\n", base);
+    network *gnet = load_network(cfg, weight, clear);
+    network *anet = load_network(acfg, aweight, clear);
+    //float orig_rate = anet->learning_rate;
+
+    int i, j, k;
+    layer imlayer = {0};
+    for (i = 0; i < gnet->n; ++i) {
+        if (gnet->layers[i].out_c == 3) {
+            imlayer = gnet->layers[i];
+            break;
+        }
+    }
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
+    int imgs = gnet->batch*gnet->subdivisions;
+    i = *gnet->seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args= get_base_args(anet);
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+    args.type = CLASSIFICATION_DATA;
+    args.threads=16;
+    args.classes = 1;
+    char *ls[2] = {"imagenet", "zzzzzzzz"};
+    args.labels = ls;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+
+    gnet->train = 1;
+    anet->train = 1;
+
+    int x_size = gnet->inputs*gnet->batch;
+    int y_size = gnet->truths*gnet->batch;
+    float *imerror = cuda_make_array(0, y_size);
+
+    //int ay_size = anet->truths*anet->batch;
+
+    float aloss_avg = -1;
+
+    //data generated = copy_data(train);
+
+    if (maxbatch == 0) maxbatch = gnet->max_batches;
+    while (get_current_batch(gnet) < maxbatch) {
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+
+        //translate_data_rows(train, -.5);
+        //scale_data_rows(train, 2);
+
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        data gen = copy_data(train);
+        for (j = 0; j < imgs; ++j) {
+            train.y.vals[j][0] = 1;
+            gen.y.vals[j][0] = 0;
+        }
+        time=clock();
+
+        for(j = 0; j < gnet->subdivisions; ++j){
+            get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0);
+            int z;
+            for(z = 0; z < x_size; ++z){
+                gnet->input[z] = rand_normal();
+            }
+            for(z = 0; z < gnet->batch; ++z){
+                float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
+                scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
+            }
+            /*
+               for(z = 0; z < 100; ++z){
+               printf("%f, ", gnet->input[z]);
+               }
+               printf("\n");
+               printf("input: %f %f\n", mean_array(gnet->input, x_size), variance_array(gnet->input, x_size));
+             */
+
+            //cuda_push_array(gnet->input_gpu, gnet->input, x_size);
+            //cuda_push_array(gnet->truth_gpu, gnet->truth, y_size);
+            *gnet->seen += gnet->batch;
+            forward_network(gnet);
+
+            fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
+            fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
+            copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
+            anet->delta_gpu = imerror;
+            forward_network(anet);
+            backward_network(anet);
+
+            //float genaloss = *anet->cost / anet->batch;
+            //printf("%f\n", genaloss);
+
+            scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
+            scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
+
+            //printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
+            //printf("features %f\n", cuda_mag_array(gnet->layers[gnet->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
+
+            axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
+
+            backward_network(gnet);
+
+            /*
+               for(k = 0; k < gnet->n; ++k){
+               layer l = gnet->layers[k];
+               cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
+               printf("%d: %f %f\n", k, mean_array(l.output, l.outputs*l.batch), variance_array(l.output, l.outputs*l.batch));
+               }
+             */
+
+            for(k = 0; k < gnet->batch; ++k){
+                int index = j*gnet->batch + k;
+                copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1);
+            }
+        }
+        harmless_update_network_gpu(anet);
+
+        data merge = concat_data(train, gen);
+        //randomize_data(merge);
+        float aloss = train_network(anet, merge);
+
+        //translate_image(im, 1);
+        //scale_image(im, .5);
+        //translate_image(im2, 1);
+        //scale_image(im2, .5);
+#ifdef OPENCV
+        if(display){
+            image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
+            image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
+            show_image(im, "gen", 1);
+            show_image(im2, "train", 1);
+            save_image(im, "gen");
+            save_image(im2, "train");
+        }
+#endif
+
+        /*
+           if(aloss < .1){
+           anet->learning_rate = 0;
+           } else if (aloss > .3){
+           anet->learning_rate = orig_rate;
+           }
+         */
+
+        update_network_gpu(gnet);
+
+        free_data(merge);
+        free_data(train);
+        free_data(gen);
+        if (aloss_avg < 0) aloss_avg = aloss;
+        aloss_avg = aloss_avg*.9 + aloss*.1;
+
+        printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
+        if(i%10000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+            save_weights(anet, buff);
+        }
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(gnet, buff);
+            sprintf(buff, "%s/%s.backup", backup_directory, abase);
+            save_weights(anet, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(gnet, buff);
+#endif
+}
+
+void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display)
+{
+#ifdef GPU
+    //char *train_images = "/home/pjreddie/data/coco/train1.txt";
+    //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+    char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfg);
+    char *abase = basecfg(acfg);
+    printf("%s\n", base);
+    network *net = load_network(cfg, weight, clear);
+    network *anet = load_network(acfg, aweight, clear);
+
+    int i, j, k;
+    layer imlayer = {0};
+    for (i = 0; i < net->n; ++i) {
+        if (net->layers[i].out_c == 3) {
+            imlayer = net->layers[i];
+            break;
+        }
+    }
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = net->batch*net->subdivisions;
+    i = *net->seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args= get_base_args(net);
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+
+    args.type = CLASSIFICATION_DATA;
+    args.classes = 1;
+    char *ls[2] = {"imagenet"};
+    args.labels = ls;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+
+    int x_size = net->inputs*net->batch;
+    //int y_size = x_size;
+    net->delta = 0;
+    net->train = 1;
+    float *pixs = calloc(x_size, sizeof(float));
+    float *graypixs = calloc(x_size, sizeof(float));
+    //float *y = calloc(y_size, sizeof(float));
+
+    //int ay_size = anet->outputs*anet->batch;
+    anet->delta = 0;
+    anet->train = 1;
+
+    float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch);
+
+    float aloss_avg = -1;
+    float gloss_avg = -1;
+
+    //data generated = copy_data(train);
+
+    while (get_current_batch(net) < net->max_batches) {
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        data gray = copy_data(train);
+        for(j = 0; j < imgs; ++j){
+            image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
+            grayscale_image_3c(gim);
+            train.y.vals[j][0] = .95;
+            gray.y.vals[j][0] = .05;
+        }
+        time=clock();
+        float gloss = 0;
+
+        for(j = 0; j < net->subdivisions; ++j){
+            get_next_batch(train, net->batch, j*net->batch, pixs, 0);
+            get_next_batch(gray, net->batch, j*net->batch, graypixs, 0);
+            cuda_push_array(net->input_gpu, graypixs, net->inputs*net->batch);
+            cuda_push_array(net->truth_gpu, pixs, net->truths*net->batch);
+            /*
+               image origi = float_to_image(net->w, net->h, 3, pixs);
+               image grayi = float_to_image(net->w, net->h, 3, graypixs);
+               show_image(grayi, "gray");
+               show_image(origi, "orig");
+               cvWaitKey(0);
+             */
+            *net->seen += net->batch;
+            forward_network_gpu(net);
+
+            fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
+            copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1);
+            fill_gpu(anet->inputs*anet->batch, .95, anet->truth_gpu, 1);
+            anet->delta_gpu = imerror;
+            forward_network_gpu(anet);
+            backward_network_gpu(anet);
+
+            scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net->layers[net->n-1].delta_gpu, 1);
+
+            scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
+
+            printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
+            printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
+
+            axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net->layers[net->n-1].delta_gpu, 1);
+
+            backward_network_gpu(net);
+
+
+            gloss += *net->cost /(net->subdivisions*net->batch);
+
+            for(k = 0; k < net->batch; ++k){
+                int index = j*net->batch + k;
+                copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
+            }
+        }
+        harmless_update_network_gpu(anet);
+
+        data merge = concat_data(train, gray);
+        //randomize_data(merge);
+        float aloss = train_network(anet, merge);
+
+        update_network_gpu(net);
+
+#ifdef OPENCV
+        if(display){
+            image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
+            image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
+            show_image(im, "gen", 1);
+            show_image(im2, "train", 1);
+        }
+#endif
+        free_data(merge);
+        free_data(train);
+        free_data(gray);
+        if (aloss_avg < 0) aloss_avg = aloss;
+        aloss_avg = aloss_avg*.9 + aloss*.1;
+        gloss_avg = gloss_avg*.9 + gloss*.1;
+
+        printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+            save_weights(anet, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+            sprintf(buff, "%s/%s.backup", backup_directory, abase);
+            save_weights(anet, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+#endif
+}
+
+/*
+   void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
+   {
+#ifdef GPU
+char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+char *backup_directory = "/home/pjreddie/backup/";
+srand(time(0));
+char *base = basecfg(cfgfile);
+printf("%s\n", base);
+network net = parse_network_cfg(cfgfile);
+if(weightfile){
+load_weights(&net, weightfile);
+}
+if(clear) *net->seen = 0;
+
+char *abase = basecfg(acfgfile);
+network anet = parse_network_cfg(acfgfile);
+if(aweightfile){
+load_weights(&anet, aweightfile);
+}
+if(clear) *anet->seen = 0;
+
+int i, j, k;
+layer imlayer = {0};
+for (i = 0; i < net->n; ++i) {
+if (net->layers[i].out_c == 3) {
+imlayer = net->layers[i];
+break;
+}
+}
+
+printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+int imgs = net->batch*net->subdivisions;
+i = *net->seen/imgs;
+data train, buffer;
+
+
+list *plist = get_paths(train_images);
+//int N = plist->size;
+char **paths = (char **)list_to_array(plist);
+
+load_args args = {0};
+args.w = net->w;
+args.h = net->h;
+args.paths = paths;
+args.n = imgs;
+args.m = plist->size;
+args.d = &buffer;
+
+args.min = net->min_crop;
+args.max = net->max_crop;
+args.angle = net->angle;
+args.aspect = net->aspect;
+args.exposure = net->exposure;
+args.saturation = net->saturation;
+args.hue = net->hue;
+args.size = net->w;
+args.type = CLASSIFICATION_DATA;
+args.classes = 1;
+char *ls[1] = {"coco"};
+args.labels = ls;
+
+pthread_t load_thread = load_data_in_thread(args);
+clock_t time;
+
+network_state gstate = {0};
+gstate.index = 0;
+gstate.net = net;
+int x_size = get_network_input_size(net)*net->batch;
+int y_size = 1*net->batch;
+gstate.input = cuda_make_array(0, x_size);
+gstate.truth = 0;
+gstate.delta = 0;
+gstate.train = 1;
+float *X = calloc(x_size, sizeof(float));
+float *y = calloc(y_size, sizeof(float));
+
+network_state astate = {0};
+astate.index = 0;
+astate.net = anet;
+int ay_size = get_network_output_size(anet)*anet->batch;
+astate.input = 0;
+astate.truth = 0;
+astate.delta = 0;
+astate.train = 1;
+
+float *imerror = cuda_make_array(0, imlayer.outputs);
+float *ones_gpu = cuda_make_array(0, ay_size);
+fill_gpu(ay_size, 1, ones_gpu, 1);
+
+float aloss_avg = -1;
+float gloss_avg = -1;
+
+//data generated = copy_data(train);
+
+while (get_current_batch(net) < net->max_batches) {
+    i += 1;
+    time=clock();
+    pthread_join(load_thread, 0);
+    train = buffer;
+    load_thread = load_data_in_thread(args);
+
+    printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+    data generated = copy_data(train);
+    time=clock();
+    float gloss = 0;
+
+    for(j = 0; j < net->subdivisions; ++j){
+        get_next_batch(train, net->batch, j*net->batch, X, y);
+        cuda_push_array(gstate.input, X, x_size);
+        *net->seen += net->batch;
+        forward_network_gpu(net, gstate);
+
+        fill_gpu(imlayer.outputs, 0, imerror, 1);
+        astate.input = imlayer.output_gpu;
+        astate.delta = imerror;
+        astate.truth = ones_gpu;
+        forward_network_gpu(anet, astate);
+        backward_network_gpu(anet, astate);
+
+        scal_gpu(imlayer.outputs, 1, imerror, 1);
+        axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
+
+        backward_network_gpu(net, gstate);
+
+        printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
+        printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
+
+        gloss += get_network_cost(net) /(net->subdivisions*net->batch);
+
+        cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
+        for(k = 0; k < net->batch; ++k){
+            int index = j*net->batch + k;
+            copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
+            generated.y.vals[index][0] = 0;
+        }
+    }
+    harmless_update_network_gpu(anet);
+
+    data merge = concat_data(train, generated);
+    randomize_data(merge);
+    float aloss = train_network(anet, merge);
+
+    update_network_gpu(net);
+    update_network_gpu(anet);
+    free_data(merge);
+    free_data(train);
+    free_data(generated);
+    if (aloss_avg < 0) aloss_avg = aloss;
+    aloss_avg = aloss_avg*.9 + aloss*.1;
+    gloss_avg = gloss_avg*.9 + gloss*.1;
+
+    printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
+    if(i%1000==0){
+        char buff[256];
+        sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+        save_weights(net, buff);
+        sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
+        save_weights(anet, buff);
+    }
+    if(i%100==0){
+        char buff[256];
+        sprintf(buff, "%s/%s.backup", backup_directory, base);
+        save_weights(net, buff);
+        sprintf(buff, "%s/%s.backup", backup_directory, abase);
+        save_weights(anet, buff);
+    }
+}
+char buff[256];
+sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+save_weights(net, buff);
+#endif
+}
+*/
+
+/*
+   void train_lsd(char *cfgfile, char *weightfile, int clear)
+   {
+   char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
+   char *backup_directory = "/home/pjreddie/backup/";
+   srand(time(0));
+   char *base = basecfg(cfgfile);
+   printf("%s\n", base);
+   float avg_loss = -1;
+   network net = parse_network_cfg(cfgfile);
+   if(weightfile){
+   load_weights(&net, weightfile);
+   }
+   if(clear) *net->seen = 0;
+   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+   int imgs = net->batch*net->subdivisions;
+   int i = *net->seen/imgs;
+   data train, buffer;
+
+
+   list *plist = get_paths(train_images);
+//int N = plist->size;
+char **paths = (char **)list_to_array(plist);
+
+load_args args = {0};
+args.w = net->w;
+args.h = net->h;
+args.paths = paths;
+args.n = imgs;
+args.m = plist->size;
+args.d = &buffer;
+
+args.min = net->min_crop;
+args.max = net->max_crop;
+args.angle = net->angle;
+args.aspect = net->aspect;
+args.exposure = net->exposure;
+args.saturation = net->saturation;
+args.hue = net->hue;
+args.size = net->w;
+args.type = CLASSIFICATION_DATA;
+args.classes = 1;
+char *ls[1] = {"coco"};
+args.labels = ls;
+
+pthread_t load_thread = load_data_in_thread(args);
+clock_t time;
+//while(i*imgs < N*120){
+while(get_current_batch(net) < net->max_batches){
+i += 1;
+time=clock();
+pthread_join(load_thread, 0);
+train = buffer;
+load_thread = load_data_in_thread(args);
+
+printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+time=clock();
+float loss = train_network(net, train);
+if (avg_loss < 0) avg_loss = loss;
+avg_loss = avg_loss*.9 + loss*.1;
+
+printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+if(i%1000==0){
+char buff[256];
+sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+save_weights(net, buff);
+}
+if(i%100==0){
+char buff[256];
+sprintf(buff, "%s/%s.backup", backup_directory, base);
+save_weights(net, buff);
+}
+free_data(train);
+}
+char buff[256];
+sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+save_weights(net, buff);
+}
+*/
+
+void test_lsd(char *cfg, char *weights, char *filename, int gray)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    int i, imlayer = 0;
+
+    for (i = 0; i < net->n; ++i) {
+        if (net->layers[i].out_c == 3) {
+            imlayer = i;
+            printf("%d\n", i);
+            break;
+        }
+    }
+
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image resized = resize_min(im, net->w);
+        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
+        if(gray) grayscale_image_3c(crop);
+
+        float *X = crop.data;
+        time=clock();
+        network_predict(net, X);
+        image out = get_network_image_layer(net, imlayer);
+        //yuv_to_rgb(out);
+        constrain_image(out);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        save_image(out, "out");
+        show_image(out, "out", 1);
+        show_image(crop, "crop", 0);
+
+        free_image(im);
+        free_image(resized);
+        free_image(crop);
+        if (filename) break;
+    }
+}
+
+
+void run_lsd(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    int clear = find_arg(argc, argv, "-clear");
+    int display = find_arg(argc, argv, "-display");
+    int batches = find_int_arg(argc, argv, "-b", 0);
+    char *file = find_char_arg(argc, argv, "-file", "/home/pjreddie/data/imagenet/imagenet1k.train.list");
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5] : 0;
+    char *acfg = argv[5];
+    char *aweights = (argc > 6) ? argv[6] : 0;
+    //if(0==strcmp(argv[2], "train")) train_lsd(cfg, weights, clear);
+    //else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear);
+    //else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
+    //else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
+    if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file, batches);
+    else if(0==strcmp(argv[2], "trainprog")) train_prog(cfg, weights, acfg, aweights, clear, display, file, batches);
+    else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display);
+    else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
+    else if(0==strcmp(argv[2], "inter")) inter_dcgan(cfg, weights);
+    else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0);
+    else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1);
+    /*
+       else if(0==strcmp(argv[2], "valid")) validate_lsd(cfg, weights);
+     */
+}
diff --git a/hanzi_detection/examples/nightmare.c b/hanzi_detection/examples/nightmare.c
new file mode 100755
index 0000000..2978eb6
--- /dev/null
+++ b/hanzi_detection/examples/nightmare.c
@@ -0,0 +1,414 @@
+#include "darknet.h"
+
+#include <math.h>
+
+// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
+
+float abs_mean(float *x, int n)
+{
+    int i;
+    float sum = 0;
+    for (i = 0; i < n; ++i){
+        sum += fabs(x[i]);
+    }
+    return sum/n;
+}
+
+void calculate_loss(float *output, float *delta, int n, float thresh)
+{
+    int i;
+    float mean = mean_array(output, n); 
+    float var = variance_array(output, n);
+    for(i = 0; i < n; ++i){
+        if(delta[i] > mean + thresh*sqrt(var)) delta[i] = output[i];
+        else delta[i] = 0;
+    }
+}
+
+void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
+{
+    //scale_image(orig, 2);
+    //translate_image(orig, -1);
+    net->n = max_layer + 1;
+
+    int dx = rand()%16 - 8;
+    int dy = rand()%16 - 8;
+    int flip = rand()%2;
+
+    image crop = crop_image(orig, dx, dy, orig.w, orig.h);
+    image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale));
+    if(flip) flip_image(im);
+
+    resize_network(net, im.w, im.h);
+    layer last = net->layers[net->n-1];
+    //net->layers[net->n - 1].activation = LINEAR;
+
+    image delta = make_image(im.w, im.h, im.c);
+
+#ifdef GPU
+    net->delta_gpu = cuda_make_array(delta.data, im.w*im.h*im.c);
+    copy_cpu(net->inputs, im.data, 1, net->input, 1);
+
+    forward_network_gpu(net);
+    copy_gpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1);
+
+    cuda_pull_array(last.delta_gpu, last.delta, last.outputs);
+    calculate_loss(last.delta, last.delta, last.outputs, thresh);
+    cuda_push_array(last.delta_gpu, last.delta, last.outputs);
+
+    backward_network_gpu(net);
+
+    cuda_pull_array(net->delta_gpu, delta.data, im.w*im.h*im.c);
+    cuda_free(net->delta_gpu);
+    net->delta_gpu = 0;
+#else
+    printf("\nnet: %d %d %d im: %d %d %d\n", net->w, net->h, net->inputs, im.w, im.h, im.c);
+    copy_cpu(net->inputs, im.data, 1, net->input, 1);
+    net->delta = delta.data;
+    forward_network(net);
+    copy_cpu(last.outputs, last.output, 1, last.delta, 1);
+    calculate_loss(last.output, last.delta, last.outputs, thresh);
+    backward_network(net);
+#endif
+
+    if(flip) flip_image(delta);
+    //normalize_array(delta.data, delta.w*delta.h*delta.c);
+    image resized = resize_image(delta, orig.w, orig.h);
+    image out = crop_image(resized, -dx, -dy, orig.w, orig.h);
+
+    /*
+       image g = grayscale_image(out);
+       free_image(out);
+       out = g;
+     */
+
+    //rate = rate / abs_mean(out.data, out.w*out.h*out.c);
+    image gray = make_image(out.w, out.h, out.c);
+    fill_image(gray, .5);
+    axpy_cpu(orig.w*orig.h*orig.c, -1, orig.data, 1, gray.data, 1);
+    axpy_cpu(orig.w*orig.h*orig.c, .1, gray.data, 1, out.data, 1);
+
+    if(norm) normalize_array(out.data, out.w*out.h*out.c);
+    axpy_cpu(orig.w*orig.h*orig.c, rate, out.data, 1, orig.data, 1);
+
+    /*
+       normalize_array(orig.data, orig.w*orig.h*orig.c);
+       scale_image(orig, sqrt(var));
+       translate_image(orig, mean);
+     */
+
+    //translate_image(orig, 1);
+    //scale_image(orig, .5);
+    //normalize_image(orig);
+
+    constrain_image(orig);
+
+    free_image(crop);
+    free_image(im);
+    free_image(delta);
+    free_image(resized);
+    free_image(out);
+
+}
+
+void smooth(image recon, image update, float lambda, int num)
+{
+    int i, j, k;
+    int ii, jj;
+    for(k = 0; k < recon.c; ++k){
+        for(j = 0; j < recon.h; ++j){
+            for(i = 0; i < recon.w; ++i){
+                int out_index = i + recon.w*(j + recon.h*k);
+                for(jj = j-num; jj <= j + num && jj < recon.h; ++jj){
+                    if (jj < 0) continue;
+                    for(ii = i-num; ii <= i + num && ii < recon.w; ++ii){
+                        if (ii < 0) continue;
+                        int in_index = ii + recon.w*(jj + recon.h*k);
+                        update.data[out_index] += lambda * (recon.data[in_index] - recon.data[out_index]);
+                    }
+                }
+            }
+        }
+    }
+}
+
+void reconstruct_picture(network *net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
+{
+    int iter = 0;
+    for (iter = 0; iter < iters; ++iter) {
+        image delta = make_image(recon.w, recon.h, recon.c);
+
+#ifdef GPU
+        layer l = get_network_output_layer(net);
+        cuda_push_array(net->input_gpu, recon.data, recon.w*recon.h*recon.c);
+        //cuda_push_array(net->truth_gpu, features, net->truths);
+        net->delta_gpu = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
+
+        forward_network_gpu(net);
+        cuda_push_array(l.delta_gpu, features, l.outputs);
+        axpy_gpu(l.outputs, -1, l.output_gpu, 1, l.delta_gpu, 1);
+        backward_network_gpu(net);
+
+        cuda_pull_array(net->delta_gpu, delta.data, delta.w*delta.h*delta.c);
+
+        cuda_free(net->delta_gpu);
+#else
+        net->input = recon.data;
+        net->delta = delta.data;
+        net->truth = features;
+
+        forward_network(net);
+        backward_network(net);
+#endif
+
+        //normalize_array(delta.data, delta.w*delta.h*delta.c);
+        axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
+        //smooth(recon, update, lambda, smooth_size);
+
+        axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1);
+        scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1);
+
+        float mag = mag_array(delta.data, recon.w*recon.h*recon.c);
+        printf("mag: %f\n", mag);
+        //scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
+
+        constrain_image(recon);
+        free_image(delta);
+    }
+}
+
+/*
+void run_lsd(int argc, char **argv)
+{
+    srand(0);
+    if(argc < 3){
+        fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [options! (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[2];
+    char *weights = argv[3];
+    char *input = argv[4];
+
+    int norm = find_int_arg(argc, argv, "-norm", 1);
+    int rounds = find_int_arg(argc, argv, "-rounds", 1);
+    int iters = find_int_arg(argc, argv, "-iters", 10);
+    float rate = find_float_arg(argc, argv, "-rate", .04);
+    float momentum = find_float_arg(argc, argv, "-momentum", .9);
+    float lambda = find_float_arg(argc, argv, "-lambda", .01);
+    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+    int reconstruct = find_arg(argc, argv, "-reconstruct");
+    int smooth_size = find_int_arg(argc, argv, "-smooth", 1);
+
+    network net = parse_network_cfg(cfg);
+    load_weights(&net, weights);
+    char *cfgbase = basecfg(cfg);
+    char *imbase = basecfg(input);
+
+    set_batch_network(&net, 1);
+    image im = load_image_color(input, 0, 0);
+
+    float *features = 0;
+    image update;
+    if (reconstruct){
+        im = letterbox_image(im, net->w, net->h);
+
+        int zz = 0;
+        network_predict(net, im.data);
+        image out_im = get_network_image(net);
+        image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
+        //flip_image(crop);
+        image f_im = resize_image(crop, out_im.w, out_im.h);
+        free_image(crop);
+        printf("%d features\n", out_im.w*out_im.h*out_im.c);
+
+
+        im = resize_image(im, im.w, im.h);
+        f_im = resize_image(f_im, f_im.w, f_im.h);
+        features = f_im.data;
+
+        int i;
+        for(i = 0; i < 14*14*512; ++i){
+            features[i] += rand_uniform(-.19, .19);
+        }
+
+        free_image(im);
+        im = make_random_image(im.w, im.h, im.c);
+        update = make_image(im.w, im.h, im.c);
+
+    }
+
+    int e;
+    int n;
+    for(e = 0; e < rounds; ++e){
+        fprintf(stderr, "Iteration: ");
+        fflush(stderr);
+        for(n = 0; n < iters; ++n){  
+            fprintf(stderr, "%d, ", n);
+            fflush(stderr);
+            if(reconstruct){
+                reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
+                //if ((n+1)%30 == 0) rate *= .5;
+                show_image(im, "reconstruction");
+#ifdef OPENCV
+                cvWaitKey(10);
+#endif
+            }else{
+                int layer = max_layer + rand()%range - range/2;
+                int octave = rand()%octaves;
+                optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
+            }
+        }
+        fprintf(stderr, "done\n");
+        char buff[256];
+        if (prefix){
+            sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
+        }else{
+            sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
+        }
+        printf("%d %s\n", e, buff);
+        save_image(im, buff);
+        //show_image(im, buff);
+        //cvWaitKey(0);
+
+        if(rotate){
+            image rot = rotate_image(im, rotate);
+            free_image(im);
+            im = rot;
+        }
+        image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
+        image resized = resize_image(crop, im.w, im.h);
+        free_image(im);
+        free_image(crop);
+        im = resized;
+    }
+}
+*/
+
+void run_nightmare(int argc, char **argv)
+{
+    srand(0);
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[2];
+    char *weights = argv[3];
+    char *input = argv[4];
+    int max_layer = atoi(argv[5]);
+
+    int range = find_int_arg(argc, argv, "-range", 1);
+    int norm = find_int_arg(argc, argv, "-norm", 1);
+    int rounds = find_int_arg(argc, argv, "-rounds", 1);
+    int iters = find_int_arg(argc, argv, "-iters", 10);
+    int octaves = find_int_arg(argc, argv, "-octaves", 4);
+    float zoom = find_float_arg(argc, argv, "-zoom", 1.);
+    float rate = find_float_arg(argc, argv, "-rate", .04);
+    float thresh = find_float_arg(argc, argv, "-thresh", 1.);
+    float rotate = find_float_arg(argc, argv, "-rotate", 0);
+    float momentum = find_float_arg(argc, argv, "-momentum", .9);
+    float lambda = find_float_arg(argc, argv, "-lambda", .01);
+    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+    int reconstruct = find_arg(argc, argv, "-reconstruct");
+    int smooth_size = find_int_arg(argc, argv, "-smooth", 1);
+
+    network *net = load_network(cfg, weights, 0);
+    char *cfgbase = basecfg(cfg);
+    char *imbase = basecfg(input);
+
+    set_batch_network(net, 1);
+    image im = load_image_color(input, 0, 0);
+    if(0){
+        float scale = 1;
+        if(im.w > 512 || im.h > 512){
+            if(im.w > im.h) scale = 512.0/im.w;
+            else scale = 512.0/im.h;
+        }
+        image resized = resize_image(im, scale*im.w, scale*im.h);
+        free_image(im);
+        im = resized;
+    }
+    //im = letterbox_image(im, net->w, net->h);
+
+    float *features = 0;
+    image update;
+    if (reconstruct){
+        net->n = max_layer;
+        im = letterbox_image(im, net->w, net->h);
+        //resize_network(&net, im.w, im.h);
+
+        network_predict(net, im.data);
+        if(net->layers[net->n-1].type == REGION){
+            printf("region!\n");
+            zero_objectness(net->layers[net->n-1]);
+        }
+        image out_im = copy_image(get_network_image(net));
+        /*
+           image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
+        //flip_image(crop);
+        image f_im = resize_image(crop, out_im.w, out_im.h);
+        free_image(crop);
+         */
+        printf("%d features\n", out_im.w*out_im.h*out_im.c);
+
+        features = out_im.data;
+
+        /*
+        int i;
+           for(i = 0; i < 14*14*512; ++i){
+        //features[i] += rand_uniform(-.19, .19);
+        }
+        free_image(im);
+        im = make_random_image(im.w, im.h, im.c);
+         */
+        update = make_image(im.w, im.h, im.c);
+    }
+
+    int e;
+    int n;
+    for(e = 0; e < rounds; ++e){
+        fprintf(stderr, "Iteration: ");
+        fflush(stderr);
+        for(n = 0; n < iters; ++n){  
+            fprintf(stderr, "%d, ", n);
+            fflush(stderr);
+            if(reconstruct){
+                reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
+                //if ((n+1)%30 == 0) rate *= .5;
+                show_image(im, "reconstruction", 10);
+            }else{
+                int layer = max_layer + rand()%range - range/2;
+                int octave = rand()%octaves;
+                optimize_picture(net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
+            }
+        }
+        fprintf(stderr, "done\n");
+        if(0){
+            image g = grayscale_image(im);
+            free_image(im);
+            im = g;
+        }
+        char buff[256];
+        if (prefix){
+            sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
+        }else{
+            sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
+        }
+        printf("%d %s\n", e, buff);
+        save_image(im, buff);
+        //show_image(im, buff, 0);
+
+        if(rotate){
+            image rot = rotate_image(im, rotate);
+            free_image(im);
+            im = rot;
+        }
+        image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
+        image resized = resize_image(crop, im.w, im.h);
+        free_image(im);
+        free_image(crop);
+        im = resized;
+    }
+}
+
diff --git a/hanzi_detection/examples/regressor.c b/hanzi_detection/examples/regressor.c
new file mode 100755
index 0000000..20cec0f
--- /dev/null
+++ b/hanzi_detection/examples/regressor.c
@@ -0,0 +1,240 @@
+#include "darknet.h"
+#include <sys/time.h>
+#include <assert.h>
+
+void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+    int classes = option_find_int(options, "classes", 1);
+
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+    clock_t time;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.classes = classes;
+
+    args.min = net->min_ratio*net->w;
+    args.max = net->max_ratio*net->w;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.type = REGRESSION_DATA;
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void predict_regressor(char *cfgfile, char *weightfile, char *filename)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image sized = letterbox_image(im, net->w, net->h);
+
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        printf("Predicted: %f\n", predictions[0]);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        free_image(im);
+        free_image(sized);
+        if (filename) break;
+    }
+}
+
+
+void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    printf("Regressor Demo\n");
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    list *options = read_data_cfg(datacfg);
+    int classes = option_find_int(options, "classes", 1);
+    char *name_list = option_find_str(options, "names", 0);
+    char **names = get_labels(name_list);
+
+    void * cap = open_video_stream(filename, cam_index, 0,0,0);
+    if(!cap) error("Couldn't connect to webcam.\n");
+    float fps = 0;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        image crop = center_crop_image(in, net->w, net->h);
+        grayscale_image_3c(crop);
+
+        float *predictions = network_predict(net, crop.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        int i;
+        for(i = 0; i < classes; ++i){
+            printf("%s: %f\n", names[i], predictions[i]);
+        }
+
+        show_image(crop, "Regressor", 10);
+        free_image(in);
+        free_image(crop);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void run_regressor(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) predict_regressor(data, cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_regressor(data, cfg, weights, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "demo")) demo_regressor(data, cfg, weights, cam_index, filename);
+}
+
+
diff --git a/hanzi_detection/examples/rnn.c b/hanzi_detection/examples/rnn.c
new file mode 100755
index 0000000..5d49eaa
--- /dev/null
+++ b/hanzi_detection/examples/rnn.c
@@ -0,0 +1,542 @@
+#include "darknet.h"
+
+#include <math.h>
+
+typedef struct {
+    float *x;
+    float *y;
+} float_pair;
+
+unsigned char **load_files(char *filename, int *n)
+{
+    list *paths = get_paths(filename);
+    *n = paths->size;
+    unsigned char **contents = calloc(*n, sizeof(char *));
+    int i;
+    node *x = paths->front;
+    for(i = 0; i < *n; ++i){
+        contents[i] = read_file((char *)x->val);
+        x = x->next;
+    }
+    return contents;
+}
+
+int *read_tokenized_data(char *filename, size_t *read)
+{
+    size_t size = 512;
+    size_t count = 0;
+    FILE *fp = fopen(filename, "r");
+    int *d = calloc(size, sizeof(int));
+    int n, one;
+    one = fscanf(fp, "%d", &n);
+    while(one == 1){
+        ++count;
+        if(count > size){
+            size = size*2;
+            d = realloc(d, size*sizeof(int));
+        }
+        d[count-1] = n;
+        one = fscanf(fp, "%d", &n);
+    }
+    fclose(fp);
+    d = realloc(d, count*sizeof(int));
+    *read = count;
+    return d;
+}
+
+char **read_tokens(char *filename, size_t *read)
+{
+    size_t size = 512;
+    size_t count = 0;
+    FILE *fp = fopen(filename, "r");
+    char **d = calloc(size, sizeof(char *));
+    char *line;
+    while((line=fgetl(fp)) != 0){
+        ++count;
+        if(count > size){
+            size = size*2;
+            d = realloc(d, size*sizeof(char *));
+        }
+        if(0==strcmp(line, "<NEWLINE>")) line = "\n";
+        d[count-1] = line;
+    }
+    fclose(fp);
+    d = realloc(d, count*sizeof(char *));
+    *read = count;
+    return d;
+}
+
+
+float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps)
+{
+    float *x = calloc(batch * steps * characters, sizeof(float));
+    float *y = calloc(batch * steps * characters, sizeof(float));
+    int i,j;
+    for(i = 0; i < batch; ++i){
+        for(j = 0; j < steps; ++j){
+            int curr = tokens[(offsets[i])%len];
+            int next = tokens[(offsets[i] + 1)%len];
+
+            x[(j*batch + i)*characters + curr] = 1;
+            y[(j*batch + i)*characters + next] = 1;
+
+            offsets[i] = (offsets[i] + 1) % len;
+
+            if(curr >= characters || curr < 0 || next >= characters || next < 0){
+                error("Bad char");
+            }
+        }
+    }
+    float_pair p;
+    p.x = x;
+    p.y = y;
+    return p;
+}
+
+float_pair get_seq2seq_data(char **source, char **dest, int n, int characters, size_t len, int batch, int steps)
+{
+    int i,j;
+    float *x = calloc(batch * steps * characters, sizeof(float));
+    float *y = calloc(batch * steps * characters, sizeof(float));
+    for(i = 0; i < batch; ++i){
+        int index = rand()%n;
+        //int slen = strlen(source[index]);
+        //int dlen = strlen(dest[index]);
+        for(j = 0; j < steps; ++j){
+            unsigned char curr = source[index][j];
+            unsigned char next = dest[index][j];
+
+            x[(j*batch + i)*characters + curr] = 1;
+            y[(j*batch + i)*characters + next] = 1;
+
+            if(curr > 255 || curr <= 0 || next > 255 || next <= 0){
+                /*text[(index+j+2)%len] = 0;
+                printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]);
+                printf("%s", text+index);
+                */
+                error("Bad char");
+            }
+        }
+    }
+    float_pair p;
+    p.x = x;
+    p.y = y;
+    return p;
+}
+
+float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps)
+{
+    float *x = calloc(batch * steps * characters, sizeof(float));
+    float *y = calloc(batch * steps * characters, sizeof(float));
+    int i,j;
+    for(i = 0; i < batch; ++i){
+        for(j = 0; j < steps; ++j){
+            unsigned char curr = text[(offsets[i])%len];
+            unsigned char next = text[(offsets[i] + 1)%len];
+
+            x[(j*batch + i)*characters + curr] = 1;
+            y[(j*batch + i)*characters + next] = 1;
+
+            offsets[i] = (offsets[i] + 1) % len;
+
+            if(curr > 255 || curr <= 0 || next > 255 || next <= 0){
+                /*text[(index+j+2)%len] = 0;
+                printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]);
+                printf("%s", text+index);
+                */
+                error("Bad char");
+            }
+        }
+    }
+    float_pair p;
+    p.x = x;
+    p.y = y;
+    return p;
+}
+
+void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized)
+{
+    srand(time(0));
+    unsigned char *text = 0;
+    int *tokens = 0;
+    size_t size;
+    if(tokenized){
+        tokens = read_tokenized_data(filename, &size);
+    } else {
+        text = read_file(filename);
+        size = strlen((const char*)text);
+    }
+
+    char *backup_directory = "/home/pjreddie/backup/";
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+    float avg_loss = -1;
+    network *net = load_network(cfgfile, weightfile, clear);
+
+    int inputs = net->inputs;
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g, Inputs: %d %d %d\n", net->learning_rate, net->momentum, net->decay, inputs, net->batch, net->time_steps);
+    int batch = net->batch;
+    int steps = net->time_steps;
+    if(clear) *net->seen = 0;
+    int i = (*net->seen)/net->batch;
+
+    int streams = batch/steps;
+    size_t *offsets = calloc(streams, sizeof(size_t));
+    int j;
+    for(j = 0; j < streams; ++j){
+        offsets[j] = rand_size_t()%size;
+    }
+
+    clock_t time;
+    while(get_current_batch(net) < net->max_batches){
+        i += 1;
+        time=clock();
+        float_pair p;
+        if(tokenized){
+            p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps);
+        }else{
+            p = get_rnn_data(text, offsets, inputs, size, streams, steps);
+        }
+
+        copy_cpu(net->inputs*net->batch, p.x, 1, net->input, 1);
+        copy_cpu(net->truths*net->batch, p.y, 1, net->truth, 1);
+        float loss = train_network_datum(net) / (batch);
+        free(p.x);
+        free(p.y);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        size_t chars = get_current_batch(net)*batch;
+        fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size);
+
+        for(j = 0; j < streams; ++j){
+            //printf("%d\n", j);
+            if(rand()%64 == 0){
+                //fprintf(stderr, "Reset\n");
+                offsets[j] = rand_size_t()%size;
+                reset_network_state(net, j);
+            }
+        }
+
+        if(i%10000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void print_symbol(int n, char **tokens){
+    if(tokens){
+        printf("%s ", tokens[n]);
+    } else {
+        printf("%c", n);
+    }
+}
+
+void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
+{
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
+    srand(rseed);
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int i, j;
+    for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp;
+    int c = 0;
+    int len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+
+    /*
+       fill_cpu(inputs, 0, input, 1);
+       for(i = 0; i < 10; ++i){
+       network_predict(net, input);
+       }
+       fill_cpu(inputs, 0, input, 1);
+     */
+
+    for(i = 0; i < len-1; ++i){
+        c = seed[i];
+        input[c] = 1;
+        network_predict(net, input);
+        input[c] = 0;
+        print_symbol(c, tokens);
+    }
+    if(len) c = seed[len-1];
+    print_symbol(c, tokens);
+    for(i = 0; i < num; ++i){
+        input[c] = 1;
+        float *out = network_predict(net, input);
+        input[c] = 0;
+        for(j = 32; j < 127; ++j){
+            //printf("%d %c %f\n",j, j, out[j]);
+        }
+        for(j = 0; j < inputs; ++j){
+            if (out[j] < .0001) out[j] = 0;
+        }
+        c = sample_array(out, inputs);
+        print_symbol(c, tokens);
+    }
+    printf("\n");
+}
+
+void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
+{
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
+    srand(rseed);
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int i, j;
+    for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp;
+    int c = 0;
+    float *input = calloc(inputs, sizeof(float));
+    float *out = 0;
+
+    while(1){
+        reset_network_state(net, 0);
+        while((c = getc(stdin)) != EOF && c != 0){
+            input[c] = 1;
+            out = network_predict(net, input);
+            input[c] = 0;
+        }
+        for(i = 0; i < num; ++i){
+            for(j = 0; j < inputs; ++j){
+                if (out[j] < .0001) out[j] = 0;
+            }
+            int next = sample_array(out, inputs);
+            if(c == '.' && next == '\n') break;
+            c = next;
+            print_symbol(c, tokens);
+
+            input[c] = 1;
+            out = network_predict(net, input);
+            input[c] = 0;
+        }
+        printf("\n");
+    }
+}
+
+void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
+{
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
+    srand(rseed);
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int i, j;
+    for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp;
+    int c = 0;
+    float *input = calloc(inputs, sizeof(float));
+    float *out = 0;
+
+    while((c = getc(stdin)) != EOF){
+        input[c] = 1;
+        out = network_predict(net, input);
+        input[c] = 0;
+    }
+    for(i = 0; i < num; ++i){
+        for(j = 0; j < inputs; ++j){
+            if (out[j] < .0001) out[j] = 0;
+        }
+        int next = sample_array(out, inputs);
+        if(c == '.' && next == '\n') break;
+        c = next;
+        print_symbol(c, tokens);
+
+        input[c] = 1;
+        out = network_predict(net, input);
+        input[c] = 0;
+    }
+    printf("\n");
+}
+
+void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int count = 0;
+    int words = 1;
+    int c;
+    int len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+    int i;
+    for(i = 0; i < len; ++i){
+        c = seed[i];
+        input[(int)c] = 1;
+        network_predict(net, input);
+        input[(int)c] = 0;
+    }
+    float sum = 0;
+    c = getc(stdin);
+    float log2 = log(2);
+    int in = 0;
+    while(c != EOF){
+        int next = getc(stdin);
+        if(next == EOF) break;
+        if(next < 0 || next >= 255) error("Out of range character");
+
+        input[c] = 1;
+        float *out = network_predict(net, input);
+        input[c] = 0;
+
+        if(c == '.' && next == '\n') in = 0;
+        if(!in) {
+            if(c == '>' && next == '>'){
+                in = 1;
+                ++words;
+            }
+            c = next;
+            continue;
+        }
+        ++count;
+        sum += log(out[next])/log2;
+        c = next;
+        printf("%d %d Perplexity: %4.4f    Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
+    }
+}
+
+void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int count = 0;
+    int words = 1;
+    int c;
+    int len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+    int i;
+    for(i = 0; i < len; ++i){
+        c = seed[i];
+        input[(int)c] = 1;
+        network_predict(net, input);
+        input[(int)c] = 0;
+    }
+    float sum = 0;
+    c = getc(stdin);
+    float log2 = log(2);
+    while(c != EOF){
+        int next = getc(stdin);
+        if(next == EOF) break;
+        if(next < 0 || next >= 255) error("Out of range character");
+        ++count;
+        if(next == ' ' || next == '\n' || next == '\t') ++words;
+        input[c] = 1;
+        float *out = network_predict(net, input);
+        input[c] = 0;
+        sum += log(out[next])/log2;
+        c = next;
+        printf("%d BPC: %4.4f   Perplexity: %4.4f    Word Perplexity: %4.4f\n", count, -sum/count, pow(2, -sum/count), pow(2, -sum/words));
+    }
+}
+
+void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network *net = load_network(cfgfile, weightfile, 0);
+    int inputs = net->inputs;
+
+    int c;
+    int seed_len = strlen(seed);
+    float *input = calloc(inputs, sizeof(float));
+    int i;
+    char *line;
+    while((line=fgetl(stdin)) != 0){
+        reset_network_state(net, 0);
+        for(i = 0; i < seed_len; ++i){
+            c = seed[i];
+            input[(int)c] = 1;
+            network_predict(net, input);
+            input[(int)c] = 0;
+        }
+        strip(line);
+        int str_len = strlen(line);
+        for(i = 0; i < str_len; ++i){
+            c = line[i];
+            input[(int)c] = 1;
+            network_predict(net, input);
+            input[(int)c] = 0;
+        }
+        c = ' ';
+        input[(int)c] = 1;
+        network_predict(net, input);
+        input[(int)c] = 0;
+
+        layer l = net->layers[0];
+        #ifdef GPU
+        cuda_pull_array(l.output_gpu, l.output, l.outputs);
+        #endif
+        printf("%s", line);
+        for(i = 0; i < l.outputs; ++i){
+            printf(",%g", l.output[i]);
+        }
+        printf("\n");
+    }
+}
+
+void run_char_rnn(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+    char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
+    char *seed = find_char_arg(argc, argv, "-seed", "\n\n");
+    int len = find_int_arg(argc, argv, "-len", 1000);
+    float temp = find_float_arg(argc, argv, "-temp", .7);
+    int rseed = find_int_arg(argc, argv, "-srand", time(0));
+    int clear = find_arg(argc, argv, "-clear");
+    int tokenized = find_arg(argc, argv, "-tokenized");
+    char *tokens = find_char_arg(argc, argv, "-tokens", 0);
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
+    else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
+    else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
+    else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
+    else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
+    else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, temp, rseed, tokens);
+}
diff --git a/hanzi_detection/examples/rnn_vid.c b/hanzi_detection/examples/rnn_vid.c
new file mode 100755
index 0000000..e887923
--- /dev/null
+++ b/hanzi_detection/examples/rnn_vid.c
@@ -0,0 +1,208 @@
+#include "darknet.h"
+
+#ifdef OPENCV
+image get_image_from_stream(CvCapture *cap);
+image ipl_to_image(IplImage* src);
+
+void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
+
+
+typedef struct {
+    float *x;
+    float *y;
+} float_pair;
+
+float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps)
+{
+    int b;
+    assert(net.batch == steps + 1);
+    image out_im = get_network_image(net);
+    int output_size = out_im.w*out_im.h*out_im.c;
+    printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
+    float *feats = calloc(net.batch*batch*output_size, sizeof(float));
+    for(b = 0; b < batch; ++b){
+        int input_size = net.w*net.h*net.c;
+        float *input = calloc(input_size*net.batch, sizeof(float));
+        char *filename = files[rand()%n];
+        CvCapture *cap = cvCaptureFromFile(filename);
+        int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT);
+        int index = rand() % (frames - steps - 2);
+        if (frames < (steps + 4)){
+            --b;
+            free(input);
+            continue;
+        }
+
+        printf("frames: %d, index: %d\n", frames, index);
+        cvSetCaptureProperty(cap, CV_CAP_PROP_POS_FRAMES, index);
+
+        int i;
+        for(i = 0; i < net.batch; ++i){
+            IplImage* src = cvQueryFrame(cap);
+            image im = ipl_to_image(src);
+            rgbgr_image(im);
+            image re = resize_image(im, net.w, net.h);
+            //show_image(re, "loaded");
+            //cvWaitKey(10);
+            memcpy(input + i*input_size, re.data, input_size*sizeof(float));
+            free_image(im);
+            free_image(re);
+        }
+        float *output = network_predict(net, input);
+
+        free(input);
+
+        for(i = 0; i < net.batch; ++i){
+            memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float));
+        }
+
+        cvReleaseCapture(&cap);
+    }
+
+    //printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
+    float_pair p = {0};
+    p.x = feats;
+    p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c;
+
+    return p;
+}
+
+
+void train_vid_rnn(char *cfgfile, char *weightfile)
+{
+    char *train_videos = "data/vid/train.txt";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = net.batch*net.subdivisions;
+    int i = *net.seen/imgs;
+
+    list *plist = get_paths(train_videos);
+    int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+    clock_t time;
+    int steps = net.time_steps;
+    int batch = net.batch / net.time_steps;
+
+    network extractor = parse_network_cfg("cfg/extractor.cfg");
+    load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
+
+    while(get_current_batch(net) < net.max_batches){
+        i += 1;
+        time=clock();
+        float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps);
+
+        copy_cpu(net.inputs*net.batch, p.x, 1, net.input, 1);
+        copy_cpu(net.truths*net.batch, p.y, 1, net.truth, 1);
+        float loss = train_network_datum(net) / (net.batch);
+
+
+        free(p.x);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        if(i%10==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+
+image save_reconstruction(network net, image *init, float *feat, char *name, int i)
+{
+    image recon;
+    if (init) {
+        recon = copy_image(*init);
+    } else {
+        recon = make_random_image(net.w, net.h, 3);
+    }
+
+    image update = make_image(net.w, net.h, 3);
+    reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50);
+    char buff[256];
+    sprintf(buff, "%s%d", name, i);
+    save_image(recon, buff);
+    free_image(update);
+    return recon;
+}
+
+void generate_vid_rnn(char *cfgfile, char *weightfile)
+{
+    network extractor = parse_network_cfg("cfg/extractor.recon.cfg");
+    load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
+
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&extractor, 1);
+    set_batch_network(&net, 1);
+
+    int i;
+    CvCapture *cap = cvCaptureFromFile("/extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
+    float *feat;
+    float *next;
+    image last;
+    for(i = 0; i < 25; ++i){
+        image im = get_image_from_stream(cap);
+        image re = resize_image(im, extractor.w, extractor.h);
+        feat = network_predict(extractor, re.data);
+        if(i > 0){
+            printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512));
+            printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512));
+            printf("%f\n", mse_array(feat, 14*14*512));
+            axpy_cpu(14*14*512, -1, feat, 1, next, 1);
+            printf("%f\n", mse_array(next, 14*14*512));
+        }
+        next = network_predict(net, feat);
+
+        free_image(im);
+
+        free_image(save_reconstruction(extractor, 0, feat, "feat", i));
+        free_image(save_reconstruction(extractor, 0, next, "next", i));
+        if (i==24) last = copy_image(re);
+        free_image(re);
+    }
+    for(i = 0; i < 30; ++i){
+        next = network_predict(net, next);
+        image new = save_reconstruction(extractor, &last, next, "new", i);
+        free_image(last);
+        last = new;
+    }
+}
+
+void run_vid_rnn(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    //char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights);
+    else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights);
+}
+#else
+void run_vid_rnn(int argc, char **argv){}
+#endif
+
diff --git a/hanzi_detection/examples/segmenter.c b/hanzi_detection/examples/segmenter.c
new file mode 100755
index 0000000..2e7cea0
--- /dev/null
+++ b/hanzi_detection/examples/segmenter.c
@@ -0,0 +1,255 @@
+#include "darknet.h"
+#include <sys/time.h>
+#include <assert.h>
+
+void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+    image pred = get_network_image(net);
+
+    int div = net->w/pred.w;
+    assert(pred.w * div == net->w);
+    assert(pred.h * div == net->h);
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.scale = div;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+    args.classes = 80;
+
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.type = SEGMENTATION_DATA;
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        double time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(display){
+            image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]);
+            image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
+            image mask = mask_to_rgb(tr);
+            image prmask = mask_to_rgb(pred);
+            show_image(im, "input", 1);
+            show_image(prmask, "pred", 1);
+            show_image(mask, "truth", 100);
+            free_image(mask);
+            free_image(prmask);
+        }
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void predict_segmenter(char *datafile, char *cfg, char *weights, char *filename)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image sized = letterbox_image(im, net->w, net->h);
+
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        printf("Predicted: %f\n", predictions[0]);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        show_image(sized, "orig", 1);
+        show_image(prmask, "pred", 0);
+        free_image(im);
+        free_image(sized);
+        free_image(prmask);
+        if (filename) break;
+    }
+}
+
+
+void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    printf("Classifier Demo\n");
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    void * cap = open_video_stream(filename, cam_index, 0,0,0);
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    float fps = 0;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        image in_s = letterbox_image(in, net->w, net->h);
+
+        network_predict(net, in_s.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        show_image(prmask, "Segmenter", 10);
+        
+        free_image(in_s);
+        free_image(in);
+        free_image(prmask);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void run_segmenter(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    int display = find_arg(argc, argv, "-display");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) predict_segmenter(data, cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_segmenter(data, cfg, weights, gpus, ngpus, clear, display);
+    else if(0==strcmp(argv[2], "demo")) demo_segmenter(data, cfg, weights, cam_index, filename);
+}
+
+
diff --git a/hanzi_detection/examples/super.c b/hanzi_detection/examples/super.c
new file mode 100755
index 0000000..d34406b
--- /dev/null
+++ b/hanzi_detection/examples/super.c
@@ -0,0 +1,120 @@
+#include "darknet.h"
+
+void train_super(char *cfgfile, char *weightfile, int clear)
+{
+    char *train_images = "/data/imagenet/imagenet1k.train.list";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network *net = load_network(cfgfile, weightfile, clear);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = net->batch*net->subdivisions;
+    int i = *net->seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.scale = 4;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+    args.type = SUPER_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net->max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void test_super(char *cfgfile, char *weightfile, char *filename)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        resize_network(net, im.w, im.h);
+        printf("%d %d\n", im.w, im.h);
+
+        float *X = im.data;
+        time=clock();
+        network_predict(net, X);
+        image out = get_network_image(net);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        save_image(out, "out");
+        show_image(out, "out", 0);
+
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void run_super(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5] : 0;
+    int clear = find_arg(argc, argv, "-clear");
+    if(0==strcmp(argv[2], "train")) train_super(cfg, weights, clear);
+    else if(0==strcmp(argv[2], "test")) test_super(cfg, weights, filename);
+    /*
+    else if(0==strcmp(argv[2], "valid")) validate_super(cfg, weights);
+    */
+}
diff --git a/hanzi_detection/examples/swag.c b/hanzi_detection/examples/swag.c
new file mode 100755
index 0000000..c22d785
--- /dev/null
+++ b/hanzi_detection/examples/swag.c
@@ -0,0 +1,83 @@
+#include "darknet.h"
+#include <sys/time.h>
+
+void train_swag(char *cfgfile, char *weightfile)
+{
+    char *train_images = "data/voc.0712.trainval";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = net.batch*net.subdivisions;
+    int i = *net.seen/imgs;
+    data train, buffer;
+
+    layer l = net.layers[net.n - 1];
+
+    int side = l.side;
+    int classes = l.classes;
+    float jitter = l.jitter;
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.classes = classes;
+    args.jitter = jitter;
+    args.num_boxes = side;
+    args.d = &buffer;
+    args.type = REGION_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net.max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0 || i == 600){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void run_swag(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    if(0==strcmp(argv[2], "train")) train_swag(cfg, weights);
+}
diff --git a/hanzi_detection/examples/tag.c b/hanzi_detection/examples/tag.c
new file mode 100755
index 0000000..4caf8cb
--- /dev/null
+++ b/hanzi_detection/examples/tag.c
@@ -0,0 +1,140 @@
+#include "darknet.h"
+
+void train_tag(char *cfgfile, char *weightfile, int clear)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    char *backup_directory = "/home/pjreddie/backup/";
+    printf("%s\n", base);
+    network *net = load_network(cfgfile, weightfile, clear);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = 1024;
+    list *plist = get_paths("/home/pjreddie/tag/train.list");
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+    clock_t time;
+    pthread_t load_thread;
+    data train;
+    data buffer;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+
+    args.min = net->w;
+    args.max = net->max_crop;
+    args.size = net->w;
+
+    args.paths = paths;
+    args.classes = net->outputs;
+    args.n = imgs;
+    args.m = N;
+    args.d = &buffer;
+    args.type = TAG_DATA;
+
+    args.angle = net->angle;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+
+    fprintf(stderr, "%d classes\n", net->outputs);
+
+    load_thread = load_data_in_thread(args);
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+
+        load_thread = load_data_in_thread(args);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    pthread_join(load_thread, 0);
+    free_data(buffer);
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void test_tag(char *cfgfile, char *weightfile, char *filename)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+    int i = 0;
+    char **names = get_labels("data/tags.txt");
+    clock_t time;
+    int indexes[10];
+    char buff[256];
+    char *input = buff;
+    int size = net->w;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image r = resize_min(im, size);
+        resize_network(net, r.w, r.h);
+        printf("%d %d\n", r.w, r.h);
+
+        float *X = r.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 10, indexes);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        for(i = 0; i < 10; ++i){
+            int index = indexes[i];
+            printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+        }
+        if(r.data != im.data) free_image(r);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void run_tag(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    int clear = find_arg(argc, argv, "-clear");
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5] : 0;
+    if(0==strcmp(argv[2], "train")) train_tag(cfg, weights, clear);
+    else if(0==strcmp(argv[2], "test")) test_tag(cfg, weights, filename);
+}
+
diff --git a/hanzi_detection/examples/voxel.c b/hanzi_detection/examples/voxel.c
new file mode 100755
index 0000000..01ea9bb
--- /dev/null
+++ b/hanzi_detection/examples/voxel.c
@@ -0,0 +1,161 @@
+#include "darknet.h"
+
+void extract_voxel(char *lfile, char *rfile, char *prefix)
+{
+#ifdef OPENCV
+    int w = 1920;
+    int h = 1080;
+    int shift = 0;
+    int count = 0;
+    CvCapture *lcap = cvCaptureFromFile(lfile);
+    CvCapture *rcap = cvCaptureFromFile(rfile);
+    while(1){
+        image l = get_image_from_stream(lcap);
+        image r = get_image_from_stream(rcap);
+        if(!l.w || !r.w) break;
+        if(count%100 == 0) {
+            shift = best_3d_shift_r(l, r, -l.h/100, l.h/100);
+            printf("%d\n", shift);
+        }
+        image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h);
+        image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h);
+        char buff[256];
+        sprintf(buff, "%s_%05d_l", prefix, count);
+        save_image(ls, buff);
+        sprintf(buff, "%s_%05d_r", prefix, count);
+        save_image(rs, buff);
+        free_image(l);
+        free_image(r);
+        free_image(ls);
+        free_image(rs);
+        ++count;
+    }
+
+#else
+    printf("need OpenCV for extraction\n");
+#endif
+}
+
+void train_voxel(char *cfgfile, char *weightfile)
+{
+    char *train_images = "/data/imagenet/imagenet1k.train.list";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = net.batch*net.subdivisions;
+    int i = *net.seen/imgs;
+    data train, buffer;
+
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.scale = 4;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.d = &buffer;
+    args.type = SUPER_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net.max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup", backup_directory, base);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void test_voxel(char *cfgfile, char *weightfile, char *filename)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        resize_network(&net, im.w, im.h);
+        printf("%d %d\n", im.w, im.h);
+
+        float *X = im.data;
+        time=clock();
+        network_predict(net, X);
+        image out = get_network_image(net);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        save_image(out, "out");
+
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void run_voxel(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5] : 0;
+    if(0==strcmp(argv[2], "train")) train_voxel(cfg, weights);
+    else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename);
+    else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]);
+    /*
+       else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights);
+     */
+}
diff --git a/hanzi_detection/examples/writing.c b/hanzi_detection/examples/writing.c
new file mode 100755
index 0000000..1b6ff83
--- /dev/null
+++ b/hanzi_detection/examples/writing.c
@@ -0,0 +1,144 @@
+#include "darknet.h"
+
+void train_writing(char *cfgfile, char *weightfile)
+{
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = net.batch*net.subdivisions;
+    list *plist = get_paths("figures.list");
+    char **paths = (char **)list_to_array(plist);
+    clock_t time;
+    int N = plist->size;
+    printf("N: %d\n", N);
+    image out = get_network_image(net);
+
+    data train, buffer;
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.out_w = out.w;
+    args.out_h = out.h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.d = &buffer;
+    args.type = WRITING_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    int epoch = (*net.seen)/N;
+    while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+        printf("Loaded %lf seconds\n",sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+
+        /*
+           image pred = float_to_image(64, 64, 1, out);
+           print_image(pred);
+         */
+
+        /*
+           image im = float_to_image(256, 256, 3, train.X.vals[0]);
+           image lab = float_to_image(64, 64, 1, train.y.vals[0]);
+           image pred = float_to_image(64, 64, 1, out);
+           show_image(im, "image");
+           show_image(lab, "label");
+           print_image(lab);
+           show_image(pred, "pred");
+           cvWaitKey(0);
+         */
+
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+        free_data(train);
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s_batch_%ld.weights", backup_directory, base, get_current_batch(net));
+            save_weights(net, buff);
+        }
+        if(*net.seen/N > epoch){
+            epoch = *net.seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+    }
+}
+
+void test_writing(char *cfgfile, char *weightfile, char *filename)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    srand(2222222);
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+
+        image im = load_image_color(input, 0, 0);
+        resize_network(&net, im.w, im.h);
+        printf("%d %d %d\n", im.h, im.w, im.c);
+        float *X = im.data;
+        time=clock();
+        network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        image pred = get_network_image(net);
+
+        image upsampled = resize_image(pred, im.w, im.h);
+        image thresh = threshold_image(upsampled, .5);
+        pred = thresh;
+
+        show_image(pred, "prediction");
+        show_image(im, "orig");
+#ifdef OPENCV
+        cvWaitKey(0);
+        cvDestroyAllWindows();
+#endif
+
+        free_image(upsampled);
+        free_image(thresh);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+void run_writing(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5] : 0;
+    if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
+    else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, filename);
+}
+
diff --git a/hanzi_detection/examples/yolo.c b/hanzi_detection/examples/yolo.c
new file mode 100755
index 0000000..4ddb69a
--- /dev/null
+++ b/hanzi_detection/examples/yolo.c
@@ -0,0 +1,327 @@
+#include "darknet.h"
+
+char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+
+void train_yolo(char *cfgfile, char *weightfile)
+{
+    char *train_images = "/data/voc/train.txt";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network *net = load_network(cfgfile, weightfile, 0);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    int imgs = net->batch*net->subdivisions;
+    int i = *net->seen/imgs;
+    data train, buffer;
+
+
+    layer l = net->layers[net->n - 1];
+
+    int side = l.side;
+    int classes = l.classes;
+    float jitter = l.jitter;
+
+    list *plist = get_paths(train_images);
+    //int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.classes = classes;
+    args.jitter = jitter;
+    args.num_boxes = side;
+    args.d = &buffer;
+    args.type = REGION_DATA;
+
+    args.angle = net->angle;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    //while(i*imgs < N*120){
+    while(get_current_batch(net) < net->max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+        if(i%1000==0 || (i < 1000 && i%100 == 0)){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void print_yolo_detections(FILE **fps, char *id, int total, int classes, int w, int h, detection *dets)
+{
+    int i, j;
+    for(i = 0; i < total; ++i){
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
+
+        if (xmin < 0) xmin = 0;
+        if (ymin < 0) ymin = 0;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        for(j = 0; j < classes; ++j){
+            if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
+                    xmin, ymin, xmax, ymax);
+        }
+    }
+}
+
+void validate_yolo(char *cfg, char *weights)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    char *base = "results/comp4_det_test_";
+    //list *plist = get_paths("data/voc.2007.test");
+    list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt");
+    //list *plist = get_paths("data/voc.2012.test");
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+
+    int j;
+    FILE **fps = calloc(classes, sizeof(FILE *));
+    for(j = 0; j < classes; ++j){
+        char buff[1024];
+        snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
+        fps[j] = fopen(buff, "w");
+    }
+
+    int m = plist->size;
+    int i=0;
+    int t;
+
+    float thresh = .001;
+    int nms = 1;
+    float iou_thresh = .5;
+
+    int nthreads = 8;
+    image *val = calloc(nthreads, sizeof(image));
+    image *val_resized = calloc(nthreads, sizeof(image));
+    image *buf = calloc(nthreads, sizeof(image));
+    image *buf_resized = calloc(nthreads, sizeof(image));
+    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.type = IMAGE_DATA;
+
+    for(t = 0; t < nthreads; ++t){
+        args.path = paths[i+t];
+        args.im = &buf[t];
+        args.resized = &buf_resized[t];
+        thr[t] = load_data_in_thread(args);
+    }
+    time_t start = time(0);
+    for(i = nthreads; i < m+nthreads; i += nthreads){
+        fprintf(stderr, "%d\n", i);
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            pthread_join(thr[t], 0);
+            val[t] = buf[t];
+            val_resized[t] = buf_resized[t];
+        }
+        for(t = 0; t < nthreads && i+t < m; ++t){
+            args.path = paths[i+t];
+            args.im = &buf[t];
+            args.resized = &buf_resized[t];
+            thr[t] = load_data_in_thread(args);
+        }
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            char *path = paths[i+t-nthreads];
+            char *id = basecfg(path);
+            float *X = val_resized[t].data;
+            network_predict(net, X);
+            int w = val[t].w;
+            int h = val[t].h;
+            int nboxes = 0;
+            detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes);
+            if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh);
+            print_yolo_detections(fps, id, l.side*l.side*l.n, classes, w, h, dets);
+            free_detections(dets, nboxes);
+            free(id);
+            free_image(val[t]);
+            free_image(val_resized[t]);
+        }
+    }
+    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+}
+
+void validate_yolo_recall(char *cfg, char *weights)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    srand(time(0));
+
+    char *base = "results/comp4_det_test_";
+    list *plist = get_paths("data/voc.2007.test");
+    char **paths = (char **)list_to_array(plist);
+
+    layer l = net->layers[net->n-1];
+    int classes = l.classes;
+    int side = l.side;
+
+    int j, k;
+    FILE **fps = calloc(classes, sizeof(FILE *));
+    for(j = 0; j < classes; ++j){
+        char buff[1024];
+        snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
+        fps[j] = fopen(buff, "w");
+    }
+
+    int m = plist->size;
+    int i=0;
+
+    float thresh = .001;
+    float iou_thresh = .5;
+    float nms = 0;
+
+    int total = 0;
+    int correct = 0;
+    int proposals = 0;
+    float avg_iou = 0;
+
+    for(i = 0; i < m; ++i){
+        char *path = paths[i];
+        image orig = load_image_color(path, 0, 0);
+        image sized = resize_image(orig, net->w, net->h);
+        char *id = basecfg(path);
+        network_predict(net, sized.data);
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
+        if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
+
+        char labelpath[4096];
+        find_replace(path, "images", "labels", labelpath);
+        find_replace(labelpath, "JPEGImages", "labels", labelpath);
+        find_replace(labelpath, ".jpg", ".txt", labelpath);
+        find_replace(labelpath, ".JPEG", ".txt", labelpath);
+
+        int num_labels = 0;
+        box_label *truth = read_boxes(labelpath, &num_labels);
+        for(k = 0; k < side*side*l.n; ++k){
+            if(dets[k].objectness > thresh){
+                ++proposals;
+            }
+        }
+        for (j = 0; j < num_labels; ++j) {
+            ++total;
+            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+            float best_iou = 0;
+            for(k = 0; k < side*side*l.n; ++k){
+                float iou = box_iou(dets[k].bbox, t);
+                if(dets[k].objectness > thresh && iou > best_iou){
+                    best_iou = iou;
+                }
+            }
+            avg_iou += best_iou;
+            if(best_iou > iou_thresh){
+                ++correct;
+            }
+        }
+
+        fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
+        free_detections(dets, nboxes);
+        free(id);
+        free_image(orig);
+        free_image(sized);
+    }
+}
+
+void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
+{
+    image **alphabet = load_alphabet();
+    network *net = load_network(cfgfile, weightfile, 0);
+    layer l = net->layers[net->n-1];
+    set_batch_network(net, 1);
+    srand(2222222);
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    float nms=.4;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        } else {
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input,0,0);
+        image sized = resize_image(im, net->w, net->h);
+        float *X = sized.data;
+        time=clock();
+        network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
+        if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
+
+        draw_detections(im, dets, l.side*l.side*l.n, thresh, voc_names, alphabet, 20);
+        save_image(im, "predictions");
+        show_image(im, "predictions", 0);
+        free_detections(dets, nboxes);
+        free_image(im);
+        free_image(sized);
+        if (filename) break;
+    }
+}
+
+void run_yolo(int argc, char **argv)
+{
+    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+    float thresh = find_float_arg(argc, argv, "-thresh", .2);
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int frame_skip = find_int_arg(argc, argv, "-s", 0);
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    int avg = find_int_arg(argc, argv, "-avg", 1);
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
+    else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
+    else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
+    else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix, avg, .5, 0,0,0,0);
+}
diff --git a/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0015673f726b2a1ddd43c1758553093b.txt b/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0015673f726b2a1ddd43c1758553093b.txt
new file mode 100644
index 0000000..ce22e09
--- /dev/null
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@@ -0,0 +1,4 @@
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diff --git a/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0030fbc156e567a2c34afe4495318e16.txt b/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0030fbc156e567a2c34afe4495318e16.txt
new file mode 100644
index 0000000..58bc5ad
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diff --git a/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0042d7d2797b26fcfc090e27678beedd.txt b/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0042d7d2797b26fcfc090e27678beedd.txt
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index 0000000..f47ff26
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diff --git a/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0048a76678a29202cf03731f46af9c64.txt b/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/0048a76678a29202cf03731f46af9c64.txt
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diff --git a/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/00ce931e1afbaf47c62f6078e72014ce.txt b/hanzi_detection/files/train/jiyan_train_imgs_txt_1K/00ce931e1afbaf47c62f6078e72014ce.txt
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new file mode 100644
index 0000000..75384f8
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index 0000000..efe807f
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index 0000000..a5a0c7e
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new file mode 100644
index 0000000..934648f
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diff --git a/hanzi_detection/include/darknet.h b/hanzi_detection/include/darknet.h
new file mode 100755
index 0000000..4390c61
--- /dev/null
+++ b/hanzi_detection/include/darknet.h
@@ -0,0 +1,805 @@
+#ifndef DARKNET_API
+#define DARKNET_API
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include <pthread.h>
+
+#ifdef GPU
+    #define BLOCK 512
+
+    #include "cuda_runtime.h"
+    #include "curand.h"
+    #include "cublas_v2.h"
+
+    #ifdef CUDNN
+    #include "cudnn.h"
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define SECRET_NUM -1234
+extern int gpu_index;
+
+typedef struct{
+    int classes;
+    char **names;
+} metadata;
+
+metadata get_metadata(char *file);
+
+typedef struct{
+    int *leaf;
+    int n;
+    int *parent;
+    int *child;
+    int *group;
+    char **name;
+
+    int groups;
+    int *group_size;
+    int *group_offset;
+} tree;
+tree *read_tree(char *filename);
+
+typedef enum{
+    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU
+} ACTIVATION;
+
+typedef enum{
+    PNG, BMP, TGA, JPG
+} IMTYPE;
+
+typedef enum{
+    MULT, ADD, SUB, DIV
+} BINARY_ACTIVATION;
+
+typedef enum {
+    CONVOLUTIONAL,
+    DECONVOLUTIONAL,
+    CONNECTED,
+    MAXPOOL,
+    SOFTMAX,
+    DETECTION,
+    DROPOUT,
+    CROP,
+    ROUTE,
+    COST,
+    NORMALIZATION,
+    AVGPOOL,
+    LOCAL,
+    SHORTCUT,
+    ACTIVE,
+    RNN,
+    GRU,
+    LSTM,
+    CRNN,
+    BATCHNORM,
+    NETWORK,
+    XNOR,
+    REGION,
+    YOLO,
+    ISEG,
+    REORG,
+    UPSAMPLE,
+    LOGXENT,
+    L2NORM,
+    BLANK
+} LAYER_TYPE;
+
+typedef enum{
+    SSE, MASKED, L1, SEG, SMOOTH,WGAN
+} COST_TYPE;
+
+typedef struct{
+    int batch;
+    float learning_rate;
+    float momentum;
+    float decay;
+    int adam;
+    float B1;
+    float B2;
+    float eps;
+    int t;
+} update_args;
+
+struct network;
+typedef struct network network;
+
+struct layer;
+typedef struct layer layer;
+
+struct layer{
+    LAYER_TYPE type;
+    ACTIVATION activation;
+    COST_TYPE cost_type;
+    void (*forward)   (struct layer, struct network);
+    void (*backward)  (struct layer, struct network);
+    void (*update)    (struct layer, update_args);
+    void (*forward_gpu)   (struct layer, struct network);
+    void (*backward_gpu)  (struct layer, struct network);
+    void (*update_gpu)    (struct layer, update_args);
+    int batch_normalize;
+    int shortcut;
+    int batch;
+    int forced;
+    int flipped;
+    int inputs;
+    int outputs;
+    int nweights;
+    int nbiases;
+    int extra;
+    int truths;
+    int h,w,c;
+    int out_h, out_w, out_c;
+    int n;
+    int max_boxes;
+    int groups;
+    int size;
+    int side;
+    int stride;
+    int reverse;
+    int flatten;
+    int spatial;
+    int pad;
+    int sqrt;
+    int flip;
+    int index;
+    int binary;
+    int xnor;
+    int steps;
+    int hidden;
+    int truth;
+    float smooth;
+    float dot;
+    float angle;
+    float jitter;
+    float saturation;
+    float exposure;
+    float shift;
+    float ratio;
+    float learning_rate_scale;
+    float clip;
+    int noloss;
+    int softmax;
+    int classes;
+    int coords;
+    int background;
+    int rescore;
+    int objectness;
+    int joint;
+    int noadjust;
+    int reorg;
+    int log;
+    int tanh;
+    int *mask;
+    int total;
+
+    float alpha;
+    float beta;
+    float kappa;
+
+    float coord_scale;
+    float object_scale;
+    float noobject_scale;
+    float mask_scale;
+    float class_scale;
+    int bias_match;
+    int random;
+    float ignore_thresh;
+    float truth_thresh;
+    float thresh;
+    float focus;
+    int classfix;
+    int absolute;
+
+    int onlyforward;
+    int stopbackward;
+    int dontload;
+    int dontsave;
+    int dontloadscales;
+    int numload;
+
+    float temperature;
+    float probability;
+    float scale;
+
+    char  * cweights;
+    int   * indexes;
+    int   * input_layers;
+    int   * input_sizes;
+    int   * map;
+    int   * counts;
+    float ** sums;
+    float * rand;
+    float * cost;
+    float * state;
+    float * prev_state;
+    float * forgot_state;
+    float * forgot_delta;
+    float * state_delta;
+    float * combine_cpu;
+    float * combine_delta_cpu;
+
+    float * concat;
+    float * concat_delta;
+
+    float * binary_weights;
+
+    float * biases;
+    float * bias_updates;
+
+    float * scales;
+    float * scale_updates;
+
+    float * weights;
+    float * weight_updates;
+
+    float * delta;
+    float * output;
+    float * loss;
+    float * squared;
+    float * norms;
+
+    float * spatial_mean;
+    float * mean;
+    float * variance;
+
+    float * mean_delta;
+    float * variance_delta;
+
+    float * rolling_mean;
+    float * rolling_variance;
+
+    float * x;
+    float * x_norm;
+
+    float * m;
+    float * v;
+    
+    float * bias_m;
+    float * bias_v;
+    float * scale_m;
+    float * scale_v;
+
+
+    float *z_cpu;
+    float *r_cpu;
+    float *h_cpu;
+    float * prev_state_cpu;
+
+    float *temp_cpu;
+    float *temp2_cpu;
+    float *temp3_cpu;
+
+    float *dh_cpu;
+    float *hh_cpu;
+    float *prev_cell_cpu;
+    float *cell_cpu;
+    float *f_cpu;
+    float *i_cpu;
+    float *g_cpu;
+    float *o_cpu;
+    float *c_cpu;
+    float *dc_cpu; 
+
+    float * binary_input;
+
+    struct layer *input_layer;
+    struct layer *self_layer;
+    struct layer *output_layer;
+
+    struct layer *reset_layer;
+    struct layer *update_layer;
+    struct layer *state_layer;
+
+    struct layer *input_gate_layer;
+    struct layer *state_gate_layer;
+    struct layer *input_save_layer;
+    struct layer *state_save_layer;
+    struct layer *input_state_layer;
+    struct layer *state_state_layer;
+
+    struct layer *input_z_layer;
+    struct layer *state_z_layer;
+
+    struct layer *input_r_layer;
+    struct layer *state_r_layer;
+
+    struct layer *input_h_layer;
+    struct layer *state_h_layer;
+	
+    struct layer *wz;
+    struct layer *uz;
+    struct layer *wr;
+    struct layer *ur;
+    struct layer *wh;
+    struct layer *uh;
+    struct layer *uo;
+    struct layer *wo;
+    struct layer *uf;
+    struct layer *wf;
+    struct layer *ui;
+    struct layer *wi;
+    struct layer *ug;
+    struct layer *wg;
+
+    tree *softmax_tree;
+
+    size_t workspace_size;
+
+#ifdef GPU
+    int *indexes_gpu;
+
+    float *z_gpu;
+    float *r_gpu;
+    float *h_gpu;
+
+    float *temp_gpu;
+    float *temp2_gpu;
+    float *temp3_gpu;
+
+    float *dh_gpu;
+    float *hh_gpu;
+    float *prev_cell_gpu;
+    float *cell_gpu;
+    float *f_gpu;
+    float *i_gpu;
+    float *g_gpu;
+    float *o_gpu;
+    float *c_gpu;
+    float *dc_gpu; 
+
+    float *m_gpu;
+    float *v_gpu;
+    float *bias_m_gpu;
+    float *scale_m_gpu;
+    float *bias_v_gpu;
+    float *scale_v_gpu;
+
+    float * combine_gpu;
+    float * combine_delta_gpu;
+
+    float * prev_state_gpu;
+    float * forgot_state_gpu;
+    float * forgot_delta_gpu;
+    float * state_gpu;
+    float * state_delta_gpu;
+    float * gate_gpu;
+    float * gate_delta_gpu;
+    float * save_gpu;
+    float * save_delta_gpu;
+    float * concat_gpu;
+    float * concat_delta_gpu;
+
+    float * binary_input_gpu;
+    float * binary_weights_gpu;
+
+    float * mean_gpu;
+    float * variance_gpu;
+
+    float * rolling_mean_gpu;
+    float * rolling_variance_gpu;
+
+    float * variance_delta_gpu;
+    float * mean_delta_gpu;
+
+    float * x_gpu;
+    float * x_norm_gpu;
+    float * weights_gpu;
+    float * weight_updates_gpu;
+    float * weight_change_gpu;
+
+    float * biases_gpu;
+    float * bias_updates_gpu;
+    float * bias_change_gpu;
+
+    float * scales_gpu;
+    float * scale_updates_gpu;
+    float * scale_change_gpu;
+
+    float * output_gpu;
+    float * loss_gpu;
+    float * delta_gpu;
+    float * rand_gpu;
+    float * squared_gpu;
+    float * norms_gpu;
+#ifdef CUDNN
+    cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
+    cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
+    cudnnTensorDescriptor_t normTensorDesc;
+    cudnnFilterDescriptor_t weightDesc;
+    cudnnFilterDescriptor_t dweightDesc;
+    cudnnConvolutionDescriptor_t convDesc;
+    cudnnConvolutionFwdAlgo_t fw_algo;
+    cudnnConvolutionBwdDataAlgo_t bd_algo;
+    cudnnConvolutionBwdFilterAlgo_t bf_algo;
+#endif
+#endif
+};
+
+void free_layer(layer);
+
+typedef enum {
+    CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
+} learning_rate_policy;
+
+typedef struct network{
+    int n;
+    int batch;
+    size_t *seen;
+    int *t;
+    float epoch;
+    int subdivisions;
+    layer *layers;
+    float *output;
+    learning_rate_policy policy;
+
+    float learning_rate;
+    float momentum;
+    float decay;
+    float gamma;
+    float scale;
+    float power;
+    int time_steps;
+    int step;
+    int max_batches;
+    float *scales;
+    int   *steps;
+    int num_steps;
+    int burn_in;
+
+    int adam;
+    float B1;
+    float B2;
+    float eps;
+
+    int inputs;
+    int outputs;
+    int truths;
+    int notruth;
+    int h, w, c;
+    int max_crop;
+    int min_crop;
+    float max_ratio;
+    float min_ratio;
+    int center;
+    float angle;
+    float aspect;
+    float exposure;
+    float saturation;
+    float hue;
+    int random;
+
+    int gpu_index;
+    tree *hierarchy;
+
+    float *input;
+    float *truth;
+    float *delta;
+    float *workspace;
+    int train;
+    int index;
+    float *cost;
+    float clip;
+
+#ifdef GPU
+    float *input_gpu;
+    float *truth_gpu;
+    float *delta_gpu;
+    float *output_gpu;
+#endif
+
+} network;
+
+typedef struct {
+    int w;
+    int h;
+    float scale;
+    float rad;
+    float dx;
+    float dy;
+    float aspect;
+} augment_args;
+
+typedef struct {
+    int w;
+    int h;
+    int c;
+    float *data;
+} image;
+
+typedef struct{
+    float x, y, w, h;
+} box;
+
+typedef struct detection{
+    box bbox;
+    int classes;
+    float *prob;
+    float *mask;
+    float objectness;
+    int sort_class;
+} detection;
+
+typedef struct matrix{
+    int rows, cols;
+    float **vals;
+} matrix;
+
+
+typedef struct{
+    int w, h;
+    matrix X;
+    matrix y;
+    int shallow;
+    int *num_boxes;
+    box **boxes;
+} data;
+
+typedef enum {
+    CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA
+} data_type;
+
+typedef struct load_args{
+    int threads;
+    char **paths;
+    char *path;
+    int n;
+    int m;
+    char **labels;
+    int h;
+    int w;
+    int out_w;
+    int out_h;
+    int nh;
+    int nw;
+    int num_boxes;
+    int min, max, size;
+    int classes;
+    int background;
+    int scale;
+    int center;
+    int coords;
+    float jitter;
+    float angle;
+    float aspect;
+    float saturation;
+    float exposure;
+    float hue;
+    data *d;
+    image *im;
+    image *resized;
+    data_type type;
+    tree *hierarchy;
+} load_args;
+
+typedef struct{
+    int id;
+    float x,y,w,h;
+    float left, right, top, bottom;
+} box_label;
+
+
+network *load_network(char *cfg, char *weights, int clear);
+load_args get_base_args(network *net);
+
+void free_data(data d);
+
+typedef struct node{
+    void *val;
+    struct node *next;
+    struct node *prev;
+} node;
+
+typedef struct list{
+    int size;
+    node *front;
+    node *back;
+} list;
+
+pthread_t load_data(load_args args);
+list *read_data_cfg(char *filename);
+list *read_cfg(char *filename);
+unsigned char *read_file(char *filename);
+data resize_data(data orig, int w, int h);
+data *tile_data(data orig, int divs, int size);
+data select_data(data *orig, int *inds);
+
+void forward_network(network *net);
+void backward_network(network *net);
+void update_network(network *net);
+
+
+float dot_cpu(int N, float *X, int INCX, float *Y, int INCY);
+void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
+void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
+void scal_cpu(int N, float ALPHA, float *X, int INCX);
+void fill_cpu(int N, float ALPHA, float * X, int INCX);
+void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial);
+void softmax(float *input, int n, float temp, int stride, float *output);
+
+int best_3d_shift_r(image a, image b, int min, int max);
+#ifdef GPU
+void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY);
+void fill_gpu(int N, float ALPHA, float * X, int INCX);
+void scal_gpu(int N, float ALPHA, float * X, int INCX);
+void copy_gpu(int N, float * X, int INCX, float * Y, int INCY);
+
+void cuda_set_device(int n);
+void cuda_free(float *x_gpu);
+float *cuda_make_array(float *x, size_t n);
+void cuda_pull_array(float *x_gpu, float *x, size_t n);
+float cuda_mag_array(float *x_gpu, size_t n);
+void cuda_push_array(float *x_gpu, float *x, size_t n);
+
+void forward_network_gpu(network *net);
+void backward_network_gpu(network *net);
+void update_network_gpu(network *net);
+
+float train_networks(network **nets, int n, data d, int interval);
+void sync_nets(network **nets, int n, int interval);
+void harmless_update_network_gpu(network *net);
+#endif
+image get_label(image **characters, char *string, int size);
+void draw_label(image a, int r, int c, image label, const float *rgb);
+void save_image(image im, const char *name);
+void save_image_options(image im, const char *name, IMTYPE f, int quality);
+void get_next_batch(data d, int n, int offset, float *X, float *y);
+void grayscale_image_3c(image im);
+void normalize_image(image p);
+void matrix_to_csv(matrix m);
+float train_network_sgd(network *net, data d, int n);
+void rgbgr_image(image im);
+data copy_data(data d);
+data concat_data(data d1, data d2);
+data load_cifar10_data(char *filename);
+float matrix_topk_accuracy(matrix truth, matrix guess, int k);
+void matrix_add_matrix(matrix from, matrix to);
+void scale_matrix(matrix m, float scale);
+matrix csv_to_matrix(char *filename);
+float *network_accuracies(network *net, data d, int n);
+float train_network_datum(network *net);
+image make_random_image(int w, int h, int c);
+
+void denormalize_connected_layer(layer l);
+void denormalize_convolutional_layer(layer l);
+void statistics_connected_layer(layer l);
+void rescale_weights(layer l, float scale, float trans);
+void rgbgr_weights(layer l);
+image *get_weights(layer l);
+
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, int avg, float hier_thresh, int w, int h, int fps, int fullscreen);
+void get_detection_detections(layer l, int w, int h, float thresh, detection *dets);
+
+char *option_find_str(list *l, char *key, char *def);
+int option_find_int(list *l, char *key, int def);
+int option_find_int_quiet(list *l, char *key, int def);
+
+network *parse_network_cfg(char *filename);
+void save_weights(network *net, char *filename);
+void load_weights(network *net, char *filename);
+void save_weights_upto(network *net, char *filename, int cutoff);
+void load_weights_upto(network *net, char *filename, int start, int cutoff);
+
+void zero_objectness(layer l);
+void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets);
+int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets);
+void free_network(network *net);
+void set_batch_network(network *net, int b);
+void set_temp_network(network *net, float t);
+image load_image(char *filename, int w, int h, int c);
+image load_image_color(char *filename, int w, int h);
+image make_image(int w, int h, int c);
+image resize_image(image im, int w, int h);
+void censor_image(image im, int dx, int dy, int w, int h);
+image letterbox_image(image im, int w, int h);
+image crop_image(image im, int dx, int dy, int w, int h);
+image center_crop_image(image im, int w, int h);
+image resize_min(image im, int min);
+image resize_max(image im, int max);
+image threshold_image(image im, float thresh);
+image mask_to_rgb(image mask);
+int resize_network(network *net, int w, int h);
+void free_matrix(matrix m);
+void test_resize(char *filename);
+int show_image(image p, const char *name, int ms);
+image copy_image(image p);
+void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b);
+float get_current_rate(network *net);
+void composite_3d(char *f1, char *f2, char *out, int delta);
+data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h);
+size_t get_current_batch(network *net);
+void constrain_image(image im);
+image get_network_image_layer(network *net, int i);
+layer get_network_output_layer(network *net);
+void top_predictions(network *net, int n, int *index);
+void flip_image(image a);
+image float_to_image(int w, int h, int c, float *data);
+void ghost_image(image source, image dest, int dx, int dy);
+float network_accuracy(network *net, data d);
+void random_distort_image(image im, float hue, float saturation, float exposure);
+void fill_image(image m, float s);
+image grayscale_image(image im);
+void rotate_image_cw(image im, int times);
+double what_time_is_it_now();
+image rotate_image(image m, float rad);
+void visualize_network(network *net);
+float box_iou(box a, box b);
+data load_all_cifar10();
+box_label *read_boxes(char *filename, int *n);
+box float_to_box(float *f, int stride);
+void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes);
+
+matrix network_predict_data(network *net, data test);
+image **load_alphabet();
+image get_network_image(network *net);
+float *network_predict(network *net, float *input);
+
+int network_width(network *net);
+int network_height(network *net);
+float *network_predict_image(network *net, image im);
+void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets);
+detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num);
+void free_detections(detection *dets, int n);
+
+void reset_network_state(network *net, int b);
+
+char **get_labels(char *filename);
+void do_nms_obj(detection *dets, int total, int classes, float thresh);
+void do_nms_sort(detection *dets, int total, int classes, float thresh);
+
+matrix make_matrix(int rows, int cols);
+
+#ifdef OPENCV
+void *open_video_stream(const char *f, int c, int w, int h, int fps);
+image get_image_from_stream(void *p);
+void make_window(char *name, int w, int h, int fullscreen);
+#endif
+
+void free_image(image m);
+float train_network(network *net, data d);
+pthread_t load_data_in_thread(load_args args);
+void load_data_blocking(load_args args);
+list *get_paths(char *filename);
+void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride);
+void change_leaves(tree *t, char *leaf_list);
+
+int find_int_arg(int argc, char **argv, char *arg, int def);
+float find_float_arg(int argc, char **argv, char *arg, float def);
+int find_arg(int argc, char* argv[], char *arg);
+char *find_char_arg(int argc, char **argv, char *arg, char *def);
+char *basecfg(char *cfgfile);
+void find_replace(char *str, char *orig, char *rep, char *output);
+void free_ptrs(void **ptrs, int n);
+char *fgetl(FILE *fp);
+void strip(char *s);
+float sec(clock_t clocks);
+void **list_to_array(list *l);
+void top_k(float *a, int n, int k, int *index);
+int *read_map(char *filename);
+void error(const char *s);
+int max_index(float *a, int n);
+int max_int_index(int *a, int n);
+int sample_array(float *a, int n);
+int *random_index_order(int min, int max);
+void free_list(list *l);
+float mse_array(float *a, int n);
+float variance_array(float *a, int n);
+float mag_array(float *a, int n);
+void scale_array(float *a, int n, float s);
+float mean_array(float *a, int n);
+float sum_array(float *a, int n);
+void normalize_array(float *a, int n);
+int *read_intlist(char *s, int *n, int d);
+size_t rand_size_t();
+float rand_normal();
+float rand_uniform(float min, float max);
+
+#ifdef __cplusplus
+}
+#endif
+#endif
diff --git a/hanzi_detection/libdarknet.so b/hanzi_detection/libdarknet.so
new file mode 100755
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diff --git a/hanzi_detection/model/jiyan_classify.cfg b/hanzi_detection/model/jiyan_classify.cfg
new file mode 100755
index 0000000..05c8d09
--- /dev/null
+++ b/hanzi_detection/model/jiyan_classify.cfg
@@ -0,0 +1,83 @@
+[net]
+#batch=128
+batch=64
+subdivisions=1
+height=64
+width=64
+channels=3
+max_crop=64
+min_crop=64
+angle=7
+hue=.1
+saturation=.75
+exposure=.75
+
+learning_rate=0.1
+policy=poly
+power=4
+max_batches = 85000
+momentum=0.9
+decay=0.0005
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+[convolutional]
+filters=1238
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[avgpool]
+
+[softmax]
+groups=1
+
+[cost]
+type=sse
+
+
diff --git a/hanzi_detection/model/jiyan_yolov3.cfg b/hanzi_detection/model/jiyan_yolov3.cfg
new file mode 100755
index 0000000..c0e0e90
--- /dev/null
+++ b/hanzi_detection/model/jiyan_yolov3.cfg
@@ -0,0 +1,789 @@
+[net]
+# Testing
+# batch=1
+# subdivisions=1
+# Training
+batch=64
+subdivisions=64
+width=608
+height=608
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 1500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
diff --git a/hanzi_detection/python/darknet.py b/hanzi_detection/python/darknet.py
new file mode 100755
index 0000000..d5f8ad9
--- /dev/null
+++ b/hanzi_detection/python/darknet.py
@@ -0,0 +1,156 @@
+from ctypes import *
+import math
+import random
+
+def sample(probs):
+    s = sum(probs)
+    probs = [a/s for a in probs]
+    r = random.uniform(0, 1)
+    for i in range(len(probs)):
+        r = r - probs[i]
+        if r <= 0:
+            return i
+    return len(probs)-1
+
+def c_array(ctype, values):
+    arr = (ctype*len(values))()
+    arr[:] = values
+    return arr
+
+class BOX(Structure):
+    _fields_ = [("x", c_float),
+                ("y", c_float),
+                ("w", c_float),
+                ("h", c_float)]
+
+class DETECTION(Structure):
+    _fields_ = [("bbox", BOX),
+                ("classes", c_int),
+                ("prob", POINTER(c_float)),
+                ("mask", POINTER(c_float)),
+                ("objectness", c_float),
+                ("sort_class", c_int)]
+
+
+class IMAGE(Structure):
+    _fields_ = [("w", c_int),
+                ("h", c_int),
+                ("c", c_int),
+                ("data", POINTER(c_float))]
+
+class METADATA(Structure):
+    _fields_ = [("classes", c_int),
+                ("names", POINTER(c_char_p))]
+
+    
+
+#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
+lib = CDLL("libdarknet.so", RTLD_GLOBAL)
+lib.network_width.argtypes = [c_void_p]
+lib.network_width.restype = c_int
+lib.network_height.argtypes = [c_void_p]
+lib.network_height.restype = c_int
+
+predict = lib.network_predict
+predict.argtypes = [c_void_p, POINTER(c_float)]
+predict.restype = POINTER(c_float)
+
+set_gpu = lib.cuda_set_device
+set_gpu.argtypes = [c_int]
+
+make_image = lib.make_image
+make_image.argtypes = [c_int, c_int, c_int]
+make_image.restype = IMAGE
+
+get_network_boxes = lib.get_network_boxes
+get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
+get_network_boxes.restype = POINTER(DETECTION)
+
+make_network_boxes = lib.make_network_boxes
+make_network_boxes.argtypes = [c_void_p]
+make_network_boxes.restype = POINTER(DETECTION)
+
+free_detections = lib.free_detections
+free_detections.argtypes = [POINTER(DETECTION), c_int]
+
+free_ptrs = lib.free_ptrs
+free_ptrs.argtypes = [POINTER(c_void_p), c_int]
+
+network_predict = lib.network_predict
+network_predict.argtypes = [c_void_p, POINTER(c_float)]
+
+reset_rnn = lib.reset_rnn
+reset_rnn.argtypes = [c_void_p]
+
+load_net = lib.load_network
+load_net.argtypes = [c_char_p, c_char_p, c_int]
+load_net.restype = c_void_p
+
+do_nms_obj = lib.do_nms_obj
+do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
+do_nms_sort = lib.do_nms_sort
+do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
+free_image = lib.free_image
+free_image.argtypes = [IMAGE]
+
+letterbox_image = lib.letterbox_image
+letterbox_image.argtypes = [IMAGE, c_int, c_int]
+letterbox_image.restype = IMAGE
+
+load_meta = lib.get_metadata
+lib.get_metadata.argtypes = [c_char_p]
+lib.get_metadata.restype = METADATA
+
+load_image = lib.load_image_color
+load_image.argtypes = [c_char_p, c_int, c_int]
+load_image.restype = IMAGE
+
+rgbgr_image = lib.rgbgr_image
+rgbgr_image.argtypes = [IMAGE]
+
+predict_image = lib.network_predict_image
+predict_image.argtypes = [c_void_p, IMAGE]
+predict_image.restype = POINTER(c_float)
+
+def classify(net, meta, im):
+    out = predict_image(net, im)
+    res = []
+    for i in range(meta.classes):
+        res.append((meta.names[i], out[i]))
+    res = sorted(res, key=lambda x: -x[1])
+    return res
+
+def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
+    im = load_image(image, 0, 0)
+    num = c_int(0)
+    pnum = pointer(num)
+    predict_image(net, im)
+    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
+    num = pnum[0]
+    if (nms): do_nms_obj(dets, num, meta.classes, nms);
+
+    res = []
+    for j in range(num):
+        for i in range(meta.classes):
+            if dets[j].prob[i] > 0:
+                b = dets[j].bbox
+                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
+    res = sorted(res, key=lambda x: -x[1])
+    free_image(im)
+    free_detections(dets, num)
+    return res
+
+def run():
+    net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
+    meta = load_meta("cfg/coco.data")
+    r = detect(net, meta, "data/dog.jpg")
+    print(r)
+    
+if __name__ == "__main__":
+    run()
+    
+
+    
+
diff --git a/hanzi_detection/python/proverbot.py b/hanzi_detection/python/proverbot.py
new file mode 100755
index 0000000..3a977d0
--- /dev/null
+++ b/hanzi_detection/python/proverbot.py
@@ -0,0 +1,37 @@
+from nine import *
+
+def predict_tactic(net, s):
+    prob = 0
+    d = c_array(c_float, [0.0]*256)
+    tac = ''
+    if not len(s):
+        s = '\n'
+    for c in s[:-1]:
+        d[ord(c)] = 1
+        pred = predict(net, d)
+        d[ord(c)] = 0
+    c = s[-1]
+    while 1:
+        d[ord(c)] = 1
+        pred = predict(net, d)
+        d[ord(c)] = 0
+        pred = [pred[i] for i in range(256)]
+        ind = sample(pred)
+        c = chr(ind)
+        prob += math.log(pred[ind])
+        if len(tac) and tac[-1] == '.':
+            break
+        tac = tac + c
+    return (tac, prob)
+
+def predict_tactics(net, s, n):
+    tacs = []
+    for i in range(n):
+        reset_rnn(net)
+        tacs.append(predict_tactic(net, s))
+    tacs = sorted(tacs, key=lambda x: -x[1])
+    return tacs
+
+net = load_net("cfg/coq.test.cfg", "/home/pjreddie/backup/coq.backup", 0)
+t = predict_tactics(net, "+++++\n", 10)
+print t
diff --git a/hanzi_detection/readme_classify.md b/hanzi_detection/readme_classify.md
new file mode 100644
index 0000000..36a54ca
--- /dev/null
+++ b/hanzi_detection/readme_classify.md
@@ -0,0 +1,20 @@
+## 汉字分类识别模型文档
+
+和检测模型一样使用yolo算法，同样在检测模型项目下进行。
+
+提示：backup/ 文件夹下缺少权重文件，不是我不想传，是大于100M的文件Git传不了。如果需要，微信跟我要吧。
+
+### 使用方法
+
+预测：`./darknet classify predict data/jiyan_classify.data model/jiyan_classify.cfg backup/jiyan_classify_final.weights`
+
+
+
+![classify_test](./classify_test.jpg)
+
+
+
+```shell
+[('池', 0.8325800895690918), ('施', 0.027770591899752617)]
+```
+
diff --git a/hanzi_detection/scripts/dice_label.sh b/hanzi_detection/scripts/dice_label.sh
new file mode 100755
index 0000000..f19f8a4
--- /dev/null
+++ b/hanzi_detection/scripts/dice_label.sh
@@ -0,0 +1,20 @@
+mkdir -p images
+mkdir -p images/orig
+mkdir -p images/train
+mkdir -p images/val
+
+ffmpeg -i Face1.mp4 images/orig/face1_%6d.jpg
+ffmpeg -i Face2.mp4 images/orig/face2_%6d.jpg
+ffmpeg -i Face3.mp4 images/orig/face3_%6d.jpg
+ffmpeg -i Face4.mp4 images/orig/face4_%6d.jpg
+ffmpeg -i Face5.mp4 images/orig/face5_%6d.jpg
+ffmpeg -i Face6.mp4 images/orig/face6_%6d.jpg
+
+mogrify -resize 100x100^ -gravity center -crop 100x100+0+0 +repage images/orig/*
+
+ls images/orig/* | shuf | head -n 1000 | xargs mv -t images/val
+mv images/orig/* images/train
+
+find `pwd`/images/train > dice.train.list -name \*.jpg
+find `pwd`/images/val > dice.val.list -name \*.jpg
+
diff --git a/hanzi_detection/scripts/gen_tactic.sh b/hanzi_detection/scripts/gen_tactic.sh
new file mode 100755
index 0000000..ffa30d2
--- /dev/null
+++ b/hanzi_detection/scripts/gen_tactic.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+# Usage:
+# wget http://pjreddie.com/media/files/peek.weights
+# scripts/gen_tactic.sh < data/goal.txt
+./darknet rnn generatetactic cfg/gru.cfg peek.weights 2>/dev/null
diff --git a/hanzi_detection/scripts/get_coco_dataset.sh b/hanzi_detection/scripts/get_coco_dataset.sh
new file mode 100755
index 0000000..2846301
--- /dev/null
+++ b/hanzi_detection/scripts/get_coco_dataset.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# Clone COCO API
+git clone https://github.com/pdollar/coco
+cd coco
+
+mkdir images
+cd images
+
+# Download Images
+wget -c https://pjreddie.com/media/files/train2014.zip
+wget -c https://pjreddie.com/media/files/val2014.zip
+
+# Unzip
+unzip -q train2014.zip
+unzip -q val2014.zip
+
+cd ..
+
+# Download COCO Metadata
+wget -c https://pjreddie.com/media/files/instances_train-val2014.zip
+wget -c https://pjreddie.com/media/files/coco/5k.part
+wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part
+wget -c https://pjreddie.com/media/files/coco/labels.tgz
+tar xzf labels.tgz
+unzip -q instances_train-val2014.zip
+
+# Set Up Image Lists
+paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt
+paste <(awk "{print \"$PWD\"}" <trainvalno5k.part) trainvalno5k.part | tr -d '\t' > trainvalno5k.txt
+
diff --git a/hanzi_detection/scripts/imagenet_label.sh b/hanzi_detection/scripts/imagenet_label.sh
new file mode 100755
index 0000000..01e4306
--- /dev/null
+++ b/hanzi_detection/scripts/imagenet_label.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+mkdir -p labelled
+wd=`pwd`
+
+for f in val/*.xml;
+do
+label=`grep -m1 "<name>" $f | grep -oP '<name>\K[^<]*'`
+im=`echo $f | sed 's/val/imgs/; s/xml/JPEG/'`
+out=`echo $im | sed 's/JPEG/'${label}'.JPEG/; s/imgs/labelled/'`
+ln -s ${wd}/$im ${wd}/$out
+done
+
+find ${wd}/labelled -name \*.JPEG > inet.val.list
+
diff --git a/hanzi_detection/scripts/voc_label.py b/hanzi_detection/scripts/voc_label.py
new file mode 100755
index 0000000..679fc36
--- /dev/null
+++ b/hanzi_detection/scripts/voc_label.py
@@ -0,0 +1,59 @@
+import xml.etree.ElementTree as ET
+import pickle
+import os
+from os import listdir, getcwd
+from os.path import join
+
+sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
+
+classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+
+
+def convert(size, box):
+    dw = 1./(size[0])
+    dh = 1./(size[1])
+    x = (box[0] + box[1])/2.0 - 1
+    y = (box[2] + box[3])/2.0 - 1
+    w = box[1] - box[0]
+    h = box[3] - box[2]
+    x = x*dw
+    w = w*dw
+    y = y*dh
+    h = h*dh
+    return (x,y,w,h)
+
+def convert_annotation(year, image_id):
+    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
+    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
+    tree=ET.parse(in_file)
+    root = tree.getroot()
+    size = root.find('size')
+    w = int(size.find('width').text)
+    h = int(size.find('height').text)
+
+    for obj in root.iter('object'):
+        difficult = obj.find('difficult').text
+        cls = obj.find('name').text
+        if cls not in classes or int(difficult)==1:
+            continue
+        cls_id = classes.index(cls)
+        xmlbox = obj.find('bndbox')
+        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
+        bb = convert((w,h), b)
+        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
+
+wd = getcwd()
+
+for year, image_set in sets:
+    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
+        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
+    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
+    list_file = open('%s_%s.txt'%(year, image_set), 'w')
+    for image_id in image_ids:
+        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
+        convert_annotation(year, image_id)
+    list_file.close()
+
+os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
+os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")
+
diff --git a/hanzi_detection/src/activation_kernels.cu b/hanzi_detection/src/activation_kernels.cu
new file mode 100755
index 0000000..4dc5804
--- /dev/null
+++ b/hanzi_detection/src/activation_kernels.cu
@@ -0,0 +1,206 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "activations.h"
+#include "cuda.h"
+}
+
+
+__device__ float lhtan_activate_kernel(float x)
+{
+    if(x < 0) return .001f*x;
+    if(x > 1) return .001f*(x-1.f) + 1.f;
+    return x;
+}
+__device__ float lhtan_gradient_kernel(float x)
+{
+    if(x > 0 && x < 1) return 1;
+    return .001;
+}
+
+__device__ float hardtan_activate_kernel(float x)
+{
+    if (x < -1) return -1;
+    if (x > 1) return 1;
+    return x;
+}
+__device__ float linear_activate_kernel(float x){return x;}
+__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));}
+__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;}
+__device__ float relu_activate_kernel(float x){return x*(x>0);}
+__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);}
+__device__ float selu_activate_kernel(float x){return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x)-1);}
+__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;}
+__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;}
+__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;}
+__device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);}
+__device__ float plse_activate_kernel(float x)
+{
+    if(x < -4) return .01f * (x + 4);
+    if(x > 4)  return .01f * (x - 4) + 1;
+    return .125f*x + .5f;
+}
+__device__ float stair_activate_kernel(float x)
+{
+    int n = floorf(x);
+    if (n%2 == 0) return floorf(x/2);
+    else return (x - n) + floorf(x/2);
+}
+ 
+
+__device__ float hardtan_gradient_kernel(float x)
+{
+    if (x > -1 && x < 1) return 1;
+    return 0;
+}
+__device__ float linear_gradient_kernel(float x){return 1;}
+__device__ float logistic_gradient_kernel(float x){return (1-x)*x;}
+__device__ float loggy_gradient_kernel(float x)
+{
+    float y = (x+1)/2;
+    return 2*(1-y)*y;
+}
+__device__ float relu_gradient_kernel(float x){return (x>0);}
+__device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);}
+__device__ float selu_gradient_kernel(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);}
+__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;}
+__device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;}
+__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;}
+__device__ float tanh_gradient_kernel(float x){return 1-x*x;}
+__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;}
+__device__ float stair_gradient_kernel(float x)
+{
+    if (floorf(x) == x) return 0;
+    return 1;
+}
+
+__device__ float activate_kernel(float x, ACTIVATION a)
+{
+    switch(a){
+        case LINEAR:
+            return linear_activate_kernel(x);
+        case LOGISTIC:
+            return logistic_activate_kernel(x);
+        case LOGGY:
+            return loggy_activate_kernel(x);
+        case RELU:
+            return relu_activate_kernel(x);
+        case ELU:
+            return elu_activate_kernel(x);
+        case SELU:
+            return selu_activate_kernel(x);
+        case RELIE:
+            return relie_activate_kernel(x);
+        case RAMP:
+            return ramp_activate_kernel(x);
+        case LEAKY:
+            return leaky_activate_kernel(x);
+        case TANH:
+            return tanh_activate_kernel(x);
+        case PLSE:
+            return plse_activate_kernel(x);
+        case STAIR:
+            return stair_activate_kernel(x);
+        case HARDTAN:
+            return hardtan_activate_kernel(x);
+        case LHTAN:
+            return lhtan_activate_kernel(x);
+    }
+    return 0;
+}
+
+__device__ float gradient_kernel(float x, ACTIVATION a)
+{
+    switch(a){
+        case LINEAR:
+            return linear_gradient_kernel(x);
+        case LOGISTIC:
+            return logistic_gradient_kernel(x);
+        case LOGGY:
+            return loggy_gradient_kernel(x);
+        case RELU:
+            return relu_gradient_kernel(x);
+        case ELU:
+            return elu_gradient_kernel(x);
+        case SELU:
+            return selu_gradient_kernel(x);
+        case RELIE:
+            return relie_gradient_kernel(x);
+        case RAMP:
+            return ramp_gradient_kernel(x);
+        case LEAKY:
+            return leaky_gradient_kernel(x);
+        case TANH:
+            return tanh_gradient_kernel(x);
+        case PLSE:
+            return plse_gradient_kernel(x);
+        case STAIR:
+            return stair_gradient_kernel(x);
+        case HARDTAN:
+            return hardtan_gradient_kernel(x);
+        case LHTAN:
+            return lhtan_gradient_kernel(x);
+    }
+    return 0;
+}
+
+__global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, BINARY_ACTIVATION a, float *dx)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    int i = id % s;
+    int b = id / s;
+    float x1 = x[b*s + i];
+    float x2 = x[b*s + s/2 + i];
+    if(id < n) {
+        float de = dy[id];
+        dx[b*s + i] = x2*de;
+        dx[b*s + s/2 + i] = x1*de; 
+    }
+}
+
+extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) 
+{
+    binary_gradient_array_kernel<<<cuda_gridsize(n/2), BLOCK>>>(x, dx, n/2, size, a, y);
+    check_error(cudaPeekAtLastError());
+}
+__global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    int i = id % s;
+    int b = id / s;
+    float x1 = x[b*s + i];
+    float x2 = x[b*s + s/2 + i];
+    if(id < n) y[id] = x1*x2;
+}
+
+extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) 
+{
+    binary_activate_array_kernel<<<cuda_gridsize(n/2), BLOCK>>>(x, n/2, size, a, y);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void activate_array_kernel(float *x, int n, ACTIVATION a)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n) x[i] = activate_kernel(x[i], a);
+}
+
+__global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n) delta[i] *= gradient_kernel(x[i], a);
+}
+
+extern "C" void activate_array_gpu(float *x, int n, ACTIVATION a) 
+{
+    activate_array_kernel<<<cuda_gridsize(n), BLOCK>>>(x, n, a);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta) 
+{
+    gradient_array_kernel<<<cuda_gridsize(n), BLOCK>>>(x, n, a, delta);
+    check_error(cudaPeekAtLastError());
+}
diff --git a/hanzi_detection/src/activation_layer.c b/hanzi_detection/src/activation_layer.c
new file mode 100755
index 0000000..b4ba953
--- /dev/null
+++ b/hanzi_detection/src/activation_layer.c
@@ -0,0 +1,63 @@
+#include "activation_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+layer make_activation_layer(int batch, int inputs, ACTIVATION activation)
+{
+    layer l = {0};
+    l.type = ACTIVE;
+
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.batch=batch;
+
+    l.output = calloc(batch*inputs, sizeof(float*));
+    l.delta = calloc(batch*inputs, sizeof(float*));
+
+    l.forward = forward_activation_layer;
+    l.backward = backward_activation_layer;
+#ifdef GPU
+    l.forward_gpu = forward_activation_layer_gpu;
+    l.backward_gpu = backward_activation_layer_gpu;
+
+    l.output_gpu = cuda_make_array(l.output, inputs*batch);
+    l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
+#endif
+    l.activation = activation;
+    fprintf(stderr, "Activation Layer: %d inputs\n", inputs);
+    return l;
+}
+
+void forward_activation_layer(layer l, network net)
+{
+    copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_activation_layer(layer l, network net)
+{
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+    copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void forward_activation_layer_gpu(layer l, network net)
+{
+    copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_activation_layer_gpu(layer l, network net)
+{
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+    copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
diff --git a/hanzi_detection/src/activation_layer.h b/hanzi_detection/src/activation_layer.h
new file mode 100755
index 0000000..42118a8
--- /dev/null
+++ b/hanzi_detection/src/activation_layer.h
@@ -0,0 +1,19 @@
+#ifndef ACTIVATION_LAYER_H
+#define ACTIVATION_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_activation_layer(int batch, int inputs, ACTIVATION activation);
+
+void forward_activation_layer(layer l, network net);
+void backward_activation_layer(layer l, network net);
+
+#ifdef GPU
+void forward_activation_layer_gpu(layer l, network net);
+void backward_activation_layer_gpu(layer l, network net);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/activations.c b/hanzi_detection/src/activations.c
new file mode 100755
index 0000000..da1a17a
--- /dev/null
+++ b/hanzi_detection/src/activations.c
@@ -0,0 +1,150 @@
+#include "activations.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+char *get_activation_string(ACTIVATION a)
+{
+    switch(a){
+        case LOGISTIC:
+            return "logistic";
+        case LOGGY:
+            return "loggy";
+        case RELU:
+            return "relu";
+        case ELU:
+            return "elu";
+        case SELU:
+            return "selu";
+        case RELIE:
+            return "relie";
+        case RAMP:
+            return "ramp";
+        case LINEAR:
+            return "linear";
+        case TANH:
+            return "tanh";
+        case PLSE:
+            return "plse";
+        case LEAKY:
+            return "leaky";
+        case STAIR:
+            return "stair";
+        case HARDTAN:
+            return "hardtan";
+        case LHTAN:
+            return "lhtan";
+        default:
+            break;
+    }
+    return "relu";
+}
+
+ACTIVATION get_activation(char *s)
+{
+    if (strcmp(s, "logistic")==0) return LOGISTIC;
+    if (strcmp(s, "loggy")==0) return LOGGY;
+    if (strcmp(s, "relu")==0) return RELU;
+    if (strcmp(s, "elu")==0) return ELU;
+    if (strcmp(s, "selu")==0) return SELU;
+    if (strcmp(s, "relie")==0) return RELIE;
+    if (strcmp(s, "plse")==0) return PLSE;
+    if (strcmp(s, "hardtan")==0) return HARDTAN;
+    if (strcmp(s, "lhtan")==0) return LHTAN;
+    if (strcmp(s, "linear")==0) return LINEAR;
+    if (strcmp(s, "ramp")==0) return RAMP;
+    if (strcmp(s, "leaky")==0) return LEAKY;
+    if (strcmp(s, "tanh")==0) return TANH;
+    if (strcmp(s, "stair")==0) return STAIR;
+    fprintf(stderr, "Couldn't find activation function %s, going with ReLU\n", s);
+    return RELU;
+}
+
+float activate(float x, ACTIVATION a)
+{
+    switch(a){
+        case LINEAR:
+            return linear_activate(x);
+        case LOGISTIC:
+            return logistic_activate(x);
+        case LOGGY:
+            return loggy_activate(x);
+        case RELU:
+            return relu_activate(x);
+        case ELU:
+            return elu_activate(x);
+        case SELU:
+            return selu_activate(x);
+        case RELIE:
+            return relie_activate(x);
+        case RAMP:
+            return ramp_activate(x);
+        case LEAKY:
+            return leaky_activate(x);
+        case TANH:
+            return tanh_activate(x);
+        case PLSE:
+            return plse_activate(x);
+        case STAIR:
+            return stair_activate(x);
+        case HARDTAN:
+            return hardtan_activate(x);
+        case LHTAN:
+            return lhtan_activate(x);
+    }
+    return 0;
+}
+
+void activate_array(float *x, const int n, const ACTIVATION a)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        x[i] = activate(x[i], a);
+    }
+}
+
+float gradient(float x, ACTIVATION a)
+{
+    switch(a){
+        case LINEAR:
+            return linear_gradient(x);
+        case LOGISTIC:
+            return logistic_gradient(x);
+        case LOGGY:
+            return loggy_gradient(x);
+        case RELU:
+            return relu_gradient(x);
+        case ELU:
+            return elu_gradient(x);
+        case SELU:
+            return selu_gradient(x);
+        case RELIE:
+            return relie_gradient(x);
+        case RAMP:
+            return ramp_gradient(x);
+        case LEAKY:
+            return leaky_gradient(x);
+        case TANH:
+            return tanh_gradient(x);
+        case PLSE:
+            return plse_gradient(x);
+        case STAIR:
+            return stair_gradient(x);
+        case HARDTAN:
+            return hardtan_gradient(x);
+        case LHTAN:
+            return lhtan_gradient(x);
+    }
+    return 0;
+}
+
+void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        delta[i] *= gradient(x[i], a);
+    }
+} 
+
diff --git a/hanzi_detection/src/activations.h b/hanzi_detection/src/activations.h
new file mode 100755
index 0000000..9780d2c
--- /dev/null
+++ b/hanzi_detection/src/activations.h
@@ -0,0 +1,87 @@
+#ifndef ACTIVATIONS_H
+#define ACTIVATIONS_H
+#include "darknet.h"
+#include "cuda.h"
+#include "math.h"
+
+ACTIVATION get_activation(char *s);
+
+char *get_activation_string(ACTIVATION a);
+float activate(float x, ACTIVATION a);
+float gradient(float x, ACTIVATION a);
+void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta);
+void activate_array(float *x, const int n, const ACTIVATION a);
+#ifdef GPU
+void activate_array_gpu(float *x, int n, ACTIVATION a);
+void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta);
+#endif
+
+static inline float stair_activate(float x)
+{
+    int n = floor(x);
+    if (n%2 == 0) return floor(x/2.);
+    else return (x - n) + floor(x/2.);
+}
+static inline float hardtan_activate(float x)
+{
+    if (x < -1) return -1;
+    if (x > 1) return 1;
+    return x;
+}
+static inline float linear_activate(float x){return x;}
+static inline float logistic_activate(float x){return 1./(1. + exp(-x));}
+static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;}
+static inline float relu_activate(float x){return x*(x>0);}
+static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);}
+static inline float selu_activate(float x){return (x >= 0)*1.0507*x + (x < 0)*1.0507*1.6732*(exp(x)-1);}
+static inline float relie_activate(float x){return (x>0) ? x : .01*x;}
+static inline float ramp_activate(float x){return x*(x>0)+.1*x;}
+static inline float leaky_activate(float x){return (x>0) ? x : .1*x;}
+static inline float tanh_activate(float x){return (exp(2*x)-1)/(exp(2*x)+1);}
+static inline float plse_activate(float x)
+{
+    if(x < -4) return .01 * (x + 4);
+    if(x > 4)  return .01 * (x - 4) + 1;
+    return .125*x + .5;
+}
+
+static inline float lhtan_activate(float x)
+{
+    if(x < 0) return .001*x;
+    if(x > 1) return .001*(x-1) + 1;
+    return x;
+}
+static inline float lhtan_gradient(float x)
+{
+    if(x > 0 && x < 1) return 1;
+    return .001;
+}
+
+static inline float hardtan_gradient(float x)
+{
+    if (x > -1 && x < 1) return 1;
+    return 0;
+}
+static inline float linear_gradient(float x){return 1;}
+static inline float logistic_gradient(float x){return (1-x)*x;}
+static inline float loggy_gradient(float x)
+{
+    float y = (x+1.)/2.;
+    return 2*(1-y)*y;
+}
+static inline float stair_gradient(float x)
+{
+    if (floor(x) == x) return 0;
+    return 1;
+}
+static inline float relu_gradient(float x){return (x>0);}
+static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);}
+static inline float selu_gradient(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);}
+static inline float relie_gradient(float x){return (x>0) ? 1 : .01;}
+static inline float ramp_gradient(float x){return (x>0)+.1;}
+static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;}
+static inline float tanh_gradient(float x){return 1-x*x;}
+static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01 : .125;}
+
+#endif
+
diff --git a/hanzi_detection/src/avgpool_layer.c b/hanzi_detection/src/avgpool_layer.c
new file mode 100755
index 0000000..83034db
--- /dev/null
+++ b/hanzi_detection/src/avgpool_layer.c
@@ -0,0 +1,71 @@
+#include "avgpool_layer.h"
+#include "cuda.h"
+#include <stdio.h>
+
+avgpool_layer make_avgpool_layer(int batch, int w, int h, int c)
+{
+    fprintf(stderr, "avg                     %4d x%4d x%4d   ->  %4d\n",  w, h, c, c);
+    avgpool_layer l = {0};
+    l.type = AVGPOOL;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.out_w = 1;
+    l.out_h = 1;
+    l.out_c = c;
+    l.outputs = l.out_c;
+    l.inputs = h*w*c;
+    int output_size = l.outputs * batch;
+    l.output =  calloc(output_size, sizeof(float));
+    l.delta =   calloc(output_size, sizeof(float));
+    l.forward = forward_avgpool_layer;
+    l.backward = backward_avgpool_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_avgpool_layer_gpu;
+    l.backward_gpu = backward_avgpool_layer_gpu;
+    l.output_gpu  = cuda_make_array(l.output, output_size);
+    l.delta_gpu   = cuda_make_array(l.delta, output_size);
+    #endif
+    return l;
+}
+
+void resize_avgpool_layer(avgpool_layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+    l->inputs = h*w*l->c;
+}
+
+void forward_avgpool_layer(const avgpool_layer l, network net)
+{
+    int b,i,k;
+
+    for(b = 0; b < l.batch; ++b){
+        for(k = 0; k < l.c; ++k){
+            int out_index = k + b*l.c;
+            l.output[out_index] = 0;
+            for(i = 0; i < l.h*l.w; ++i){
+                int in_index = i + l.h*l.w*(k + b*l.c);
+                l.output[out_index] += net.input[in_index];
+            }
+            l.output[out_index] /= l.h*l.w;
+        }
+    }
+}
+
+void backward_avgpool_layer(const avgpool_layer l, network net)
+{
+    int b,i,k;
+
+    for(b = 0; b < l.batch; ++b){
+        for(k = 0; k < l.c; ++k){
+            int out_index = k + b*l.c;
+            for(i = 0; i < l.h*l.w; ++i){
+                int in_index = i + l.h*l.w*(k + b*l.c);
+                net.delta[in_index] += l.delta[out_index] / (l.h*l.w);
+            }
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/avgpool_layer.h b/hanzi_detection/src/avgpool_layer.h
new file mode 100755
index 0000000..3bd356c
--- /dev/null
+++ b/hanzi_detection/src/avgpool_layer.h
@@ -0,0 +1,23 @@
+#ifndef AVGPOOL_LAYER_H
+#define AVGPOOL_LAYER_H
+
+#include "image.h"
+#include "cuda.h"
+#include "layer.h"
+#include "network.h"
+
+typedef layer avgpool_layer;
+
+image get_avgpool_image(avgpool_layer l);
+avgpool_layer make_avgpool_layer(int batch, int w, int h, int c);
+void resize_avgpool_layer(avgpool_layer *l, int w, int h);
+void forward_avgpool_layer(const avgpool_layer l, network net);
+void backward_avgpool_layer(const avgpool_layer l, network net);
+
+#ifdef GPU
+void forward_avgpool_layer_gpu(avgpool_layer l, network net);
+void backward_avgpool_layer_gpu(avgpool_layer l, network net);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/avgpool_layer_kernels.cu b/hanzi_detection/src/avgpool_layer_kernels.cu
new file mode 100755
index 0000000..a7eca3a
--- /dev/null
+++ b/hanzi_detection/src/avgpool_layer_kernels.cu
@@ -0,0 +1,61 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "avgpool_layer.h"
+#include "cuda.h"
+}
+
+__global__ void forward_avgpool_layer_kernel(int n, int w, int h, int c, float *input, float *output)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int i;
+    int out_index = (k + c*b);
+    output[out_index] = 0;
+    for(i = 0; i < w*h; ++i){
+        int in_index = i + h*w*(k + b*c);
+        output[out_index] += input[in_index];
+    }
+    output[out_index] /= w*h;
+}
+
+__global__ void backward_avgpool_layer_kernel(int n, int w, int h, int c, float *in_delta, float *out_delta)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int i;
+    int out_index = (k + c*b);
+    for(i = 0; i < w*h; ++i){
+        int in_index = i + h*w*(k + b*c);
+        in_delta[in_index] += out_delta[out_index] / (w*h);
+    }
+}
+
+extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network net)
+{
+    size_t n = layer.c*layer.batch;
+
+    forward_avgpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.w, layer.h, layer.c, net.input_gpu, layer.output_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network net)
+{
+    size_t n = layer.c*layer.batch;
+
+    backward_avgpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.w, layer.h, layer.c, net.delta_gpu, layer.delta_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
diff --git a/hanzi_detection/src/batchnorm_layer.c b/hanzi_detection/src/batchnorm_layer.c
new file mode 100755
index 0000000..ebff387
--- /dev/null
+++ b/hanzi_detection/src/batchnorm_layer.c
@@ -0,0 +1,279 @@
+#include "convolutional_layer.h"
+#include "batchnorm_layer.h"
+#include "blas.h"
+#include <stdio.h>
+
+layer make_batchnorm_layer(int batch, int w, int h, int c)
+{
+    fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c);
+    layer l = {0};
+    l.type = BATCHNORM;
+    l.batch = batch;
+    l.h = l.out_h = h;
+    l.w = l.out_w = w;
+    l.c = l.out_c = c;
+    l.output = calloc(h * w * c * batch, sizeof(float));
+    l.delta  = calloc(h * w * c * batch, sizeof(float));
+    l.inputs = w*h*c;
+    l.outputs = l.inputs;
+
+    l.scales = calloc(c, sizeof(float));
+    l.scale_updates = calloc(c, sizeof(float));
+    l.biases = calloc(c, sizeof(float));
+    l.bias_updates = calloc(c, sizeof(float));
+    int i;
+    for(i = 0; i < c; ++i){
+        l.scales[i] = 1;
+    }
+
+    l.mean = calloc(c, sizeof(float));
+    l.variance = calloc(c, sizeof(float));
+
+    l.rolling_mean = calloc(c, sizeof(float));
+    l.rolling_variance = calloc(c, sizeof(float));
+
+    l.forward = forward_batchnorm_layer;
+    l.backward = backward_batchnorm_layer;
+#ifdef GPU
+    l.forward_gpu = forward_batchnorm_layer_gpu;
+    l.backward_gpu = backward_batchnorm_layer_gpu;
+
+    l.output_gpu =  cuda_make_array(l.output, h * w * c * batch);
+    l.delta_gpu =   cuda_make_array(l.delta, h * w * c * batch);
+
+    l.biases_gpu = cuda_make_array(l.biases, c);
+    l.bias_updates_gpu = cuda_make_array(l.bias_updates, c);
+
+    l.scales_gpu = cuda_make_array(l.scales, c);
+    l.scale_updates_gpu = cuda_make_array(l.scale_updates, c);
+
+    l.mean_gpu = cuda_make_array(l.mean, c);
+    l.variance_gpu = cuda_make_array(l.variance, c);
+
+    l.rolling_mean_gpu = cuda_make_array(l.mean, c);
+    l.rolling_variance_gpu = cuda_make_array(l.variance, c);
+
+    l.mean_delta_gpu = cuda_make_array(l.mean, c);
+    l.variance_delta_gpu = cuda_make_array(l.variance, c);
+
+    l.x_gpu = cuda_make_array(l.output, l.batch*l.outputs);
+    l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.outputs);
+    #ifdef CUDNN
+    cudnnCreateTensorDescriptor(&l.normTensorDesc);
+    cudnnCreateTensorDescriptor(&l.dstTensorDesc);
+    cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
+    cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); 
+
+    #endif
+#endif
+    return l;
+}
+
+void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
+{
+    int i,b,f;
+    for(f = 0; f < n; ++f){
+        float sum = 0;
+        for(b = 0; b < batch; ++b){
+            for(i = 0; i < size; ++i){
+                int index = i + size*(f + n*b);
+                sum += delta[index] * x_norm[index];
+            }
+        }
+        scale_updates[f] += sum;
+    }
+}
+
+void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+
+    int i,j,k;
+    for(i = 0; i < filters; ++i){
+        mean_delta[i] = 0;
+        for (j = 0; j < batch; ++j) {
+            for (k = 0; k < spatial; ++k) {
+                int index = j*filters*spatial + i*spatial + k;
+                mean_delta[i] += delta[index];
+            }
+        }
+        mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
+    }
+}
+void  variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+
+    int i,j,k;
+    for(i = 0; i < filters; ++i){
+        variance_delta[i] = 0;
+        for(j = 0; j < batch; ++j){
+            for(k = 0; k < spatial; ++k){
+                int index = j*filters*spatial + i*spatial + k;
+                variance_delta[i] += delta[index]*(x[index] - mean[i]);
+            }
+        }
+        variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
+    }
+}
+void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
+{
+    int f, j, k;
+    for(j = 0; j < batch; ++j){
+        for(f = 0; f < filters; ++f){
+            for(k = 0; k < spatial; ++k){
+                int index = j*filters*spatial + f*spatial + k;
+                delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
+            }
+        }
+    }
+}
+
+void resize_batchnorm_layer(layer *layer, int w, int h)
+{
+    fprintf(stderr, "Not implemented\n");
+}
+
+void forward_batchnorm_layer(layer l, network net)
+{
+    if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
+    copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1);
+    if(net.train){
+        mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean);
+        variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance);
+
+        scal_cpu(l.out_c, .99, l.rolling_mean, 1);
+        axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1);
+        scal_cpu(l.out_c, .99, l.rolling_variance, 1);
+        axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1);
+
+        normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w);   
+        copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1);
+    } else {
+        normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w);
+    }
+    scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
+    add_bias(l.output, l.biases, l.batch, l.out_c, l.out_h*l.out_w);
+}
+
+void backward_batchnorm_layer(layer l, network net)
+{
+    if(!net.train){
+        l.mean = l.rolling_mean;
+        l.variance = l.rolling_variance;
+    }
+    backward_bias(l.bias_updates, l.delta, l.batch, l.out_c, l.out_w*l.out_h);
+    backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates);
+
+    scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
+
+    mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta);
+    variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta);
+    normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta);
+    if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void pull_batchnorm_layer(layer l)
+{
+    cuda_pull_array(l.scales_gpu, l.scales, l.c);
+    cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
+    cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
+}
+void push_batchnorm_layer(layer l)
+{
+    cuda_push_array(l.scales_gpu, l.scales, l.c);
+    cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
+    cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
+}
+
+void forward_batchnorm_layer_gpu(layer l, network net)
+{
+    if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
+    copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
+    if (net.train) {
+#ifdef CUDNN
+        float one = 1;
+        float zero = 0;
+        cudnnBatchNormalizationForwardTraining(cudnn_handle(),
+                CUDNN_BATCHNORM_SPATIAL,
+                &one,
+                &zero,
+                l.dstTensorDesc,
+                l.x_gpu,
+                l.dstTensorDesc,
+                l.output_gpu,
+                l.normTensorDesc,
+                l.scales_gpu,
+                l.biases_gpu,
+                .01,
+                l.rolling_mean_gpu,
+                l.rolling_variance_gpu,
+                .00001,
+                l.mean_gpu,
+                l.variance_gpu);
+#else
+        fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu);
+        fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu);
+
+        scal_gpu(l.out_c, .99, l.rolling_mean_gpu, 1);
+        axpy_gpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
+        scal_gpu(l.out_c, .99, l.rolling_variance_gpu, 1);
+        axpy_gpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
+
+        copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
+        normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+        copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
+
+        scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+        add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+#endif
+    } else {
+        normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+        scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+        add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+    }
+
+}
+
+void backward_batchnorm_layer_gpu(layer l, network net)
+{
+    if(!net.train){
+        l.mean_gpu = l.rolling_mean_gpu;
+        l.variance_gpu = l.rolling_variance_gpu;
+    }
+#ifdef CUDNN
+    float one = 1;
+    float zero = 0;
+    cudnnBatchNormalizationBackward(cudnn_handle(),
+            CUDNN_BATCHNORM_SPATIAL,
+            &one,
+            &zero,
+            &one,
+            &one,
+            l.dstTensorDesc,
+            l.x_gpu,
+            l.dstTensorDesc,
+            l.delta_gpu,
+            l.dstTensorDesc,
+            l.x_norm_gpu,
+            l.normTensorDesc,
+            l.scales_gpu,
+            l.scale_updates_gpu,
+            l.bias_updates_gpu,
+            .00001,
+            l.mean_gpu,
+            l.variance_gpu);
+    copy_gpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1);
+#else
+    backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+    backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu);
+
+    scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+
+    fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu);
+    fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu);
+    normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu);
+#endif
+    if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
diff --git a/hanzi_detection/src/batchnorm_layer.h b/hanzi_detection/src/batchnorm_layer.h
new file mode 100755
index 0000000..25a18a3
--- /dev/null
+++ b/hanzi_detection/src/batchnorm_layer.h
@@ -0,0 +1,19 @@
+#ifndef BATCHNORM_LAYER_H
+#define BATCHNORM_LAYER_H
+
+#include "image.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_batchnorm_layer(int batch, int w, int h, int c);
+void forward_batchnorm_layer(layer l, network net);
+void backward_batchnorm_layer(layer l, network net);
+
+#ifdef GPU
+void forward_batchnorm_layer_gpu(layer l, network net);
+void backward_batchnorm_layer_gpu(layer l, network net);
+void pull_batchnorm_layer(layer l);
+void push_batchnorm_layer(layer l);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/blas.c b/hanzi_detection/src/blas.c
new file mode 100755
index 0000000..9e16044
--- /dev/null
+++ b/hanzi_detection/src/blas.c
@@ -0,0 +1,351 @@
+#include "blas.h"
+
+#include <math.h>
+#include <assert.h>
+#include <float.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
+{
+    int b,i,j,k;
+    int out_c = c/(stride*stride);
+
+    for(b = 0; b < batch; ++b){
+        for(k = 0; k < c; ++k){
+            for(j = 0; j < h; ++j){
+                for(i = 0; i < w; ++i){
+                    int in_index  = i + w*(j + h*(k + c*b));
+                    int c2 = k % out_c;
+                    int offset = k / out_c;
+                    int w2 = i*stride + offset % stride;
+                    int h2 = j*stride + offset / stride;
+                    int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
+                    if(forward) out[out_index] = x[in_index];
+                    else out[in_index] = x[out_index];
+                }
+            }
+        }
+    }
+}
+
+void flatten(float *x, int size, int layers, int batch, int forward)
+{
+    float *swap = calloc(size*layers*batch, sizeof(float));
+    int i,c,b;
+    for(b = 0; b < batch; ++b){
+        for(c = 0; c < layers; ++c){
+            for(i = 0; i < size; ++i){
+                int i1 = b*layers*size + c*size + i;
+                int i2 = b*layers*size + i*layers + c;
+                if (forward) swap[i2] = x[i1];
+                else swap[i1] = x[i2];
+            }
+        }
+    }
+    memcpy(x, swap, size*layers*batch*sizeof(float));
+    free(swap);
+}
+
+void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
+    }
+}
+
+void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        if(da) da[i] += dc[i] * s[i];
+        if(db) db[i] += dc[i] * (1-s[i]);
+        ds[i] += dc[i] * (a[i] - b[i]);
+    }
+}
+
+void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out)
+{
+    int stride = w1/w2;
+    int sample = w2/w1;
+    assert(stride == h1/h2);
+    assert(sample == h2/h1);
+    if(stride < 1) stride = 1;
+    if(sample < 1) sample = 1;
+    int minw = (w1 < w2) ? w1 : w2;
+    int minh = (h1 < h2) ? h1 : h2;
+    int minc = (c1 < c2) ? c1 : c2;
+
+    int i,j,k,b;
+    for(b = 0; b < batch; ++b){
+        for(k = 0; k < minc; ++k){
+            for(j = 0; j < minh; ++j){
+                for(i = 0; i < minw; ++i){
+                    int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
+                    int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
+                    out[out_index] = s1*out[out_index] + s2*add[add_index];
+                }
+            }
+        }
+    }
+}
+
+void mean_cpu(float *x, int batch, int filters, int spatial, float *mean)
+{
+    float scale = 1./(batch * spatial);
+    int i,j,k;
+    for(i = 0; i < filters; ++i){
+        mean[i] = 0;
+        for(j = 0; j < batch; ++j){
+            for(k = 0; k < spatial; ++k){
+                int index = j*filters*spatial + i*spatial + k;
+                mean[i] += x[index];
+            }
+        }
+        mean[i] *= scale;
+    }
+}
+
+void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+    float scale = 1./(batch * spatial - 1);
+    int i,j,k;
+    for(i = 0; i < filters; ++i){
+        variance[i] = 0;
+        for(j = 0; j < batch; ++j){
+            for(k = 0; k < spatial; ++k){
+                int index = j*filters*spatial + i*spatial + k;
+                variance[i] += pow((x[index] - mean[i]), 2);
+            }
+        }
+        variance[i] *= scale;
+    }
+}
+
+void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial)
+{
+    int b,f,i;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < spatial; ++i){
+            float sum = 0;
+            for(f = 0; f < filters; ++f){
+                int index = b*filters*spatial + f*spatial + i;
+                sum += powf(x[index], 2);
+            }
+            sum = sqrtf(sum);
+            for(f = 0; f < filters; ++f){
+                int index = b*filters*spatial + f*spatial + i;
+                x[index] /= sum;
+                dx[index] = (1 - x[index]) / sum;
+            }
+        }
+    }
+}
+
+
+void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+    int b, f, i;
+    for(b = 0; b < batch; ++b){
+        for(f = 0; f < filters; ++f){
+            for(i = 0; i < spatial; ++i){
+                int index = b*filters*spatial + f*spatial + i;
+                x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f);
+            }
+        }
+    }
+}
+
+void const_cpu(int N, float ALPHA, float *X, int INCX)
+{
+    int i;
+    for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
+}
+
+void mul_cpu(int N, float *X, int INCX, float *Y, int INCY)
+{
+    int i;
+    for(i = 0; i < N; ++i) Y[i*INCY] *= X[i*INCX];
+}
+
+void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
+{
+    int i;
+    for(i = 0; i < N; ++i) Y[i*INCY] = pow(X[i*INCX], ALPHA);
+}
+
+void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
+{
+    int i;
+    for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX];
+}
+
+void scal_cpu(int N, float ALPHA, float *X, int INCX)
+{
+    int i;
+    for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
+}
+
+void fill_cpu(int N, float ALPHA, float *X, int INCX)
+{
+    int i;
+    for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
+}
+
+void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    int i, j;
+    int index = 0;
+    for(j = 0; j < B; ++j) {
+        for(i = 0; i < NX; ++i){
+            if(X) X[j*NX + i] += OUT[index];
+            ++index;
+        }
+        for(i = 0; i < NY; ++i){
+            if(Y) Y[j*NY + i] += OUT[index];
+            ++index;
+        }
+    }
+}
+
+void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    int i, j;
+    int index = 0;
+    for(j = 0; j < B; ++j) {
+        for(i = 0; i < NX; ++i){
+            OUT[index++] = X[j*NX + i];
+        }
+        for(i = 0; i < NY; ++i){
+            OUT[index++] = Y[j*NY + i];
+        }
+    }
+}
+
+void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
+{
+    int i;
+    for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
+}
+
+void mult_add_into_cpu(int N, float *X, float *Y, float *Z)
+{
+    int i;
+    for(i = 0; i < N; ++i) Z[i] += X[i]*Y[i];
+}
+
+void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        float diff = truth[i] - pred[i];
+        float abs_val = fabs(diff);
+        if(abs_val < 1) {
+            error[i] = diff * diff;
+            delta[i] = diff;
+        }
+        else {
+            error[i] = 2*abs_val - 1;
+            delta[i] = (diff < 0) ? 1 : -1;
+        }
+    }
+}
+
+void l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        float diff = truth[i] - pred[i];
+        error[i] = fabs(diff);
+        delta[i] = diff > 0 ? 1 : -1;
+    }
+}
+
+void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        float t = truth[i];
+        float p = pred[i];
+        error[i] = (t) ? -log(p) : 0;
+        delta[i] = t-p;
+    }
+}
+
+void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        float t = truth[i];
+        float p = pred[i];
+        error[i] = -t*log(p) - (1-t)*log(1-p);
+        delta[i] = t-p;
+    }
+}
+
+void l2_cpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        float diff = truth[i] - pred[i];
+        error[i] = diff * diff;
+        delta[i] = diff;
+    }
+}
+
+float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
+{
+    int i;
+    float dot = 0;
+    for(i = 0; i < N; ++i) dot += X[i*INCX] * Y[i*INCY];
+    return dot;
+}
+
+void softmax(float *input, int n, float temp, int stride, float *output)
+{
+    int i;
+    float sum = 0;
+    float largest = -FLT_MAX;
+    for(i = 0; i < n; ++i){
+        if(input[i*stride] > largest) largest = input[i*stride];
+    }
+    for(i = 0; i < n; ++i){
+        float e = exp(input[i*stride]/temp - largest/temp);
+        sum += e;
+        output[i*stride] = e;
+    }
+    for(i = 0; i < n; ++i){
+        output[i*stride] /= sum;
+    }
+}
+
+
+void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
+{
+    int g, b;
+    for(b = 0; b < batch; ++b){
+        for(g = 0; g < groups; ++g){
+            softmax(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset);
+        }
+    }
+}
+
+void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
+{
+    int i, j, k, b;
+    for(b = 0; b < batch; ++b){
+        for(k = 0; k < c; ++k){
+            for(j = 0; j < h*stride; ++j){
+                for(i = 0; i < w*stride; ++i){
+                    int in_index = b*w*h*c + k*w*h + (j/stride)*w + i/stride;
+                    int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i;
+                    if(forward) out[out_index] = scale*in[in_index];
+                    else in[in_index] += scale*out[out_index];
+                }
+            }
+        }
+    }
+}
+
+
diff --git a/hanzi_detection/src/blas.h b/hanzi_detection/src/blas.h
new file mode 100755
index 0000000..707291d
--- /dev/null
+++ b/hanzi_detection/src/blas.h
@@ -0,0 +1,105 @@
+#ifndef BLAS_H
+#define BLAS_H
+#include "darknet.h"
+
+void flatten(float *x, int size, int layers, int batch, int forward);
+void pm(int M, int N, float *A);
+float *random_matrix(int rows, int cols);
+void time_random_matrix(int TA, int TB, int m, int k, int n);
+void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out);
+
+void test_blas();
+
+void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT);
+void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT);
+void mult_add_into_cpu(int N, float *X, float *Y, float *Z);
+
+void const_cpu(int N, float ALPHA, float *X, int INCX);
+void constrain_gpu(int N, float ALPHA, float * X, int INCX);
+void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
+void mul_cpu(int N, float *X, int INCX, float *Y, int INCY);
+
+int test_gpu_blas();
+void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out);
+
+void mean_cpu(float *x, int batch, int filters, int spatial, float *mean);
+void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
+
+void scale_bias(float *output, float *scales, int batch, int n, int size);
+void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates);
+void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta);
+void  variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta);
+void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta);
+void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial);
+
+void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error);
+void l2_cpu(int n, float *pred, float *truth, float *delta, float *error);
+void l1_cpu(int n, float *pred, float *truth, float *delta, float *error);
+void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error);
+void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error);
+void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c);
+void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc);
+
+void softmax(float *input, int n, float temp, int stride, float *output);
+void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output);
+void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out);
+
+#ifdef GPU
+#include "cuda.h"
+#include "tree.h"
+
+void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY);
+void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY);
+void copy_gpu(int N, float * X, int INCX, float * Y, int INCY);
+void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY);
+void add_gpu(int N, float ALPHA, float * X, int INCX);
+void supp_gpu(int N, float ALPHA, float * X, int INCX);
+void mask_gpu(int N, float * X, float mask_num, float * mask, float val);
+void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale);
+void const_gpu(int N, float ALPHA, float *X, int INCX);
+void pow_gpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
+void mul_gpu(int N, float *X, int INCX, float *Y, int INCY);
+
+void mean_gpu(float *x, int batch, int filters, int spatial, float *mean);
+void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
+void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial);
+void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial);
+
+void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta);
+
+void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta);
+void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta);
+
+void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
+void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean);
+void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out);
+void scale_bias_gpu(float *output, float *biases, int batch, int n, int size);
+void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates);
+void scale_bias_gpu(float *output, float *biases, int batch, int n, int size);
+void add_bias_gpu(float *output, float *biases, int batch, int n, int size);
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size);
+
+void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void l2_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void l1_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error);
+void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc);
+void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c);
+void mult_add_into_gpu(int num, float *a, float *b, float *c);
+void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT);
+void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT);
+
+void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out);
+
+void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output);
+void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t);
+void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t);
+
+void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out);
+void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier);
+void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out);
+
+#endif
+#endif
diff --git a/hanzi_detection/src/blas_kernels.cu b/hanzi_detection/src/blas_kernels.cu
new file mode 100755
index 0000000..47e8217
--- /dev/null
+++ b/hanzi_detection/src/blas_kernels.cu
@@ -0,0 +1,1035 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+#include <assert.h>
+
+extern "C" {
+#include "blas.h"
+#include "cuda.h"
+#include "utils.h"
+}
+
+__global__ void scale_bias_kernel(float *output, float *biases, int n, int size)
+{
+    int offset = blockIdx.x * blockDim.x + threadIdx.x;
+    int filter = blockIdx.y;
+    int batch = blockIdx.z;
+
+    if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter];
+}
+
+void scale_bias_gpu(float *output, float *biases, int batch, int n, int size)
+{
+    dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
+    dim3 dimBlock(BLOCK, 1, 1);
+
+    scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
+{
+    __shared__ float part[BLOCK];
+    int i,b;
+    int filter = blockIdx.x;
+    int p = threadIdx.x;
+    float sum = 0;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < size; i += BLOCK){
+            int index = p + i + size*(filter + n*b);
+            sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
+        }
+    }
+    part[p] = sum;
+    __syncthreads();
+    if (p == 0) {
+        for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
+    }
+}
+
+void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
+{
+    backward_scale_kernel<<<n, BLOCK>>>(x_norm, delta, batch, n, size, scale_updates);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void add_bias_kernel(float *output, float *biases, int batch, int n, int size)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= n*size*batch) return;
+    int i = index % size;
+    index /= size;
+    int j = index % n;
+    index /= n;
+    int k = index;
+
+    output[(k*n+j)*size + i] += biases[j];
+}
+
+void add_bias_gpu(float *output, float *biases, int batch, int n, int size)
+{
+    int num = n*size*batch;
+
+    add_bias_kernel<<<cuda_gridsize(num), BLOCK>>>(output, biases, batch, n, size);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void backward_bias_conn_kernel(float *bias_updates, float *delta, int batch, int n)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= n) return;
+    int b;
+    float sum = 0;
+    for(b = 0; b < batch; ++b){
+        int i = b*n + index;
+        sum += delta[i];
+    }
+    bias_updates[index] += sum;
+}
+
+__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
+{
+    __shared__ float part[BLOCK];
+    int i,b;
+    int filter = blockIdx.x;
+    int p = threadIdx.x;
+    float sum = 0;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < size; i += BLOCK){
+            int index = p + i + size*(filter + n*b);
+            sum += (p+i < size) ? delta[index] : 0;
+        }
+    }
+    part[p] = sum;
+    __syncthreads();
+    if (p == 0) {
+        for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
+    }
+}
+
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
+{
+    if(size == 1){
+        backward_bias_conn_kernel<<<cuda_gridsize(n), BLOCK>>>(bias_updates, delta, batch, n);
+    }else{
+        backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size);
+    }
+    check_error(cudaPeekAtLastError());
+}
+
+/*
+__global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    int f1 = index / n;
+    int f2 = index % n;
+    if (f2 <= f1) return;
+    
+    float sum = 0;
+    float norm1 = 0;
+    float norm2 = 0;
+    int b, i;
+    for(b = 0; b <  batch; ++b){
+        for(i = 0; i < size; ++i){
+            int i1 = b * size * n + f1 * size + i;
+            int i2 = b * size * n + f2 * size + i;
+            sum += output[i1] * output[i2];
+            norm1 += output[i1] * output[i1];
+            norm2 += output[i2] * output[i2];
+        }
+    }
+    norm1 = sqrt(norm1);
+    norm2 = sqrt(norm2);
+    float norm = norm1 * norm2;
+    sum = sum / norm;
+    for(b = 0; b <  batch; ++b){
+        for(i = 0; i < size; ++i){
+            int i1 = b * size * n + f1 * size + i;
+            int i2 = b * size * n + f2 * size + i;
+            delta[i1] += - scale * sum * output[i2] / norm;
+            delta[i2] += - scale * sum * output[i1] / norm;
+        }
+    }
+}
+
+void dot_error_gpu(layer l)
+{
+    dot_kernel<<<cuda_gridsize(l.n*l.n), BLOCK>>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu);
+    check_error(cudaPeekAtLastError());
+}
+*/
+
+
+__global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= N) return;
+
+    float mhat = m[index] / (1.f - powf(B1, t));
+    float vhat = v[index] / (1.f - powf(B2, t));
+    
+    x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps);
+}
+
+extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
+{
+    adam_kernel<<<cuda_gridsize(n), BLOCK>>>(n, x, m, v, B1, B2, rate, eps, t);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t)
+{
+    scal_gpu(n, B1, m, 1);
+    scal_gpu(n, B2, v, 1);
+    axpy_gpu(n, -decay*batch, w, 1, d, 1);
+
+    axpy_gpu(n, (1-B1), d, 1, m, 1);
+    mul_gpu(n, d, 1, d, 1);
+    axpy_gpu(n, (1-B2), d, 1, v, 1);
+
+    adam_gpu(n, w, m, v, B1, B2, rate, eps, t);
+    fill_gpu(n, 0, d, 1);
+}
+
+__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= N) return;
+    int f = (index/spatial)%filters;
+    
+    x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f));
+}
+
+__global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= N) return;
+    int f = (index/spatial)%filters;
+    
+    delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
+}
+
+extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
+{
+    size_t N = batch*filters*spatial;
+    normalize_delta_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void  variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= filters) return;
+    int j,k;
+    variance_delta[i] = 0;
+    for(j = 0; j < batch; ++j){
+        for(k = 0; k < spatial; ++k){
+            int index = j*filters*spatial + i*spatial + k;
+            variance_delta[i] += delta[index]*(x[index] - mean[i]);
+        }
+    }
+    variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f));
+}
+
+__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
+{
+    int k;
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= groups) return;
+    sum[i] = 0;
+    for(k = 0; k < n; ++k){
+        sum[i] += x[k*groups + i];
+    }
+}
+
+__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+    const int threads = BLOCK;
+    __shared__ float local[threads];
+
+    int id = threadIdx.x;
+    local[id] = 0;
+
+    int filter = blockIdx.x;
+
+    int i, j;
+    for(j = 0; j < batch; ++j){
+        for(i = 0; i < spatial; i += threads){
+            int index = j*spatial*filters + filter*spatial + i + id;
+            local[id] += (i+id < spatial) ? delta[index] : 0;
+        }
+    }
+
+    __syncthreads();
+
+    if(id == 0){
+        mean_delta[filter] = 0;
+        for(i = 0; i < threads; ++i){
+            mean_delta[filter] += local[i];
+        }
+        mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f));
+    }
+}
+
+__global__ void  fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+    const int threads = BLOCK;
+    __shared__ float local[threads];
+
+    int id = threadIdx.x;
+    local[id] = 0;
+
+    int filter = blockIdx.x;
+
+    int i, j;
+    for(j = 0; j < batch; ++j){
+        for(i = 0; i < spatial; i += threads){
+            int index = j*spatial*filters + filter*spatial + i + id;
+
+            local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0;
+        }
+    }
+
+    __syncthreads();
+
+    if(id == 0){
+        variance_delta[filter] = 0;
+        for(i = 0; i < threads; ++i){
+            variance_delta[filter] += local[i];
+        }
+        variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f));
+    }
+}
+
+
+__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= filters) return;
+    int j,k;
+    mean_delta[i] = 0;
+    for (j = 0; j < batch; ++j) {
+        for (k = 0; k < spatial; ++k) {
+            int index = j*filters*spatial + i*spatial + k;
+            mean_delta[i] += delta[index];
+        }
+    }
+    mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f));
+}
+
+extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+    mean_delta_kernel<<<cuda_gridsize(filters), BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+    fast_mean_delta_kernel<<<filters, BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+    fast_variance_delta_kernel<<<filters, BLOCK>>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void  mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
+{
+    float scale = 1.f/(batch * spatial);
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= filters) return;
+    int j,k;
+    mean[i] = 0;
+    for(j = 0; j < batch; ++j){
+        for(k = 0; k < spatial; ++k){
+            int index = j*filters*spatial + i*spatial + k;
+            mean[i] += x[index];
+        }
+    }
+    mean[i] *= scale;
+}
+
+__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+    float scale = 1.f/(batch * spatial - 1);
+    int j,k;
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= filters) return;
+    variance[i] = 0;
+    for(j = 0; j < batch; ++j){
+        for(k = 0; k < spatial; ++k){
+            int index = j*filters*spatial + i*spatial + k;
+            variance[i] += powf((x[index] - mean[i]), 2);
+        }
+    }
+    variance[i] *= scale;
+}
+
+__global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i >= N) return;
+    int in_index = i;
+    int in_w = i%w;
+    i = i/w;
+    int in_h = i%h;
+    i = i/h;
+    int in_c = i%c;
+    i = i/c;
+    int b = i%batch;
+
+    int out_c = c/(stride*stride);
+
+    int c2 = in_c % out_c;
+    int offset = in_c / out_c;
+    int w2 = in_w*stride + offset % stride;
+    int h2 = in_h*stride + offset / stride;
+    //printf("%d\n", offset);
+    int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
+
+   // printf("%d %d %d\n", w2, h2, c2);
+    //printf("%d %d\n", in_index, out_index);
+    //if(out_index >= N || out_index < 0) printf("bad bad bad \n");
+
+    if(forward) out[out_index] = x[in_index];
+    else out[in_index] = x[out_index];
+    //if(forward) out[1] = x[1];
+    //else out[0] = x[0];
+}
+
+__global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX,  float *Y, int OFFY, int INCY)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) Y[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
+}
+
+__global__ void pow_kernel(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) Y[i*INCY] = pow(X[i*INCX], ALPHA);
+}
+
+__global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) X[i*INCX] = ALPHA;
+}
+
+__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX]));
+}
+
+__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) {
+        if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0;
+    }
+}
+
+__global__ void add_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) X[i*INCX] += ALPHA;
+}
+
+__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) X[i*INCX] *= ALPHA;
+}
+
+__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) X[i*INCX] = ALPHA;
+}
+
+__global__ void copy_kernel(int N,  float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX];
+}
+
+__global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < N) Y[i*INCY] *= X[i*INCX];
+}
+
+
+extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+    size_t N = batch*filters*spatial;
+    normalize_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, batch, filters, spatial);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void l2norm_kernel(int N, float *x, float *dx, int batch, int filters, int spatial)
+{
+    int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (index >= N) return;
+    int b = index / spatial;
+    int i = index % spatial;
+    int f;
+    float sum = 0;
+    for(f = 0; f < filters; ++f){
+        int index = b*filters*spatial + f*spatial + i;
+        sum += powf(x[index], 2);
+    }
+    sum = sqrtf(sum);
+    if(sum == 0) sum = 1;
+    //printf("%f\n", sum);
+    for(f = 0; f < filters; ++f){
+        int index = b*filters*spatial + f*spatial + i;
+        x[index] /= sum;
+        dx[index] = (1 - x[index]) / sum;
+    }
+}
+
+extern "C" void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial)
+{
+    size_t N = batch*spatial;
+    l2norm_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, dx, batch, filters, spatial);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void  fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
+{
+    const int threads = BLOCK;
+    __shared__ float local[threads];
+
+    int id = threadIdx.x;
+    local[id] = 0;
+
+    int filter = blockIdx.x;
+
+    int i, j;
+    for(j = 0; j < batch; ++j){
+        for(i = 0; i < spatial; i += threads){
+            int index = j*spatial*filters + filter*spatial + i + id;
+            local[id] += (i+id < spatial) ? x[index] : 0;
+        }
+    }
+
+    __syncthreads();
+
+    if(id == 0){
+        mean[filter] = 0;
+        for(i = 0; i < threads; ++i){
+            mean[filter] += local[i];
+        }
+        mean[filter] /= spatial * batch;
+    }
+}
+
+__global__ void  fast_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+    const int threads = BLOCK;
+    __shared__ float local[threads];
+
+    int id = threadIdx.x;
+    local[id] = 0;
+
+    int filter = blockIdx.x;
+
+    int i, j;
+    for(j = 0; j < batch; ++j){
+        for(i = 0; i < spatial; i += threads){
+            int index = j*spatial*filters + filter*spatial + i + id;
+
+            local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0;
+        }
+    }
+
+    __syncthreads();
+
+    if(id == 0){
+        variance[filter] = 0;
+        for(i = 0; i < threads; ++i){
+            variance[filter] += local[i];
+        }
+        variance[filter] /= (spatial * batch - 1);
+    }
+}
+
+extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
+{
+    fast_mean_kernel<<<filters, BLOCK>>>(x, batch, filters, spatial, mean);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+    fast_variance_kernel<<<filters, BLOCK>>>(x, mean, batch, filters, spatial, variance);
+    check_error(cudaPeekAtLastError());
+}
+
+
+extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
+{
+    mean_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, batch, filters, spatial, mean);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+    variance_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, mean, batch, filters, spatial, variance);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
+{
+    axpy_gpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY);
+}
+
+extern "C" void pow_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
+{
+    pow_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX, Y, INCY);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
+{
+    axpy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void copy_gpu(int N, float * X, int INCX, float * Y, int INCY)
+{
+    copy_gpu_offset(N, X, 0, INCX, Y, 0, INCY);
+}
+
+extern "C" void mul_gpu(int N, float * X, int INCX, float * Y, int INCY)
+{
+    mul_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, INCX, Y, INCY);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
+{
+    copy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, OFFX, INCX, Y, OFFY, INCY);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void flatten_kernel(int N, float *x, int spatial, int layers, int batch, int forward, float *out)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i >= N) return;
+    int in_s = i%spatial;
+    i = i/spatial;
+    int in_c = i%layers;
+    i = i/layers;
+    int b = i;
+
+    int i1 = b*layers*spatial + in_c*spatial + in_s;
+    int i2 = b*layers*spatial + in_s*layers +  in_c;
+
+    if (forward) out[i2] = x[i1];
+    else out[i1] = x[i2];
+}
+
+extern "C" void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out)
+{
+    int size = spatial*batch*layers;
+    flatten_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, spatial, layers, batch, forward, out);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
+{
+    int size = w*h*c*batch;
+    reorg_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, w, h, c, batch, stride, forward, out);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void mask_kernel(int n,  float *x, float mask_num, float *mask, float val)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n && mask[i] == mask_num) x[i] = val;
+}
+
+extern "C" void mask_gpu(int N, float * X, float mask_num, float * mask, float val)
+{
+    mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask, val);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void scale_mask_kernel(int n,  float *x, float mask_num, float *mask, float scale)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n && mask[i] == mask_num) x[i] *= scale;
+}
+
+extern "C" void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale)
+{
+    scale_mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask, scale);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void const_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    const_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void constrain_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    constrain_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+
+extern "C" void add_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    add_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void scal_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    scal_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void supp_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    supp_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void fill_gpu(int N, float ALPHA, float * X, int INCX)
+{
+    fill_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (id >= size) return;
+    int i = id % minw;
+    id /= minw;
+    int j = id % minh;
+    id /= minh;
+    int k = id % minc;
+    id /= minc;
+    int b = id % batch;
+
+    int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
+    int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
+    out[out_index] = s1*out[out_index] + s2*add[add_index];
+    //out[out_index] += add[add_index];
+}
+
+extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out)
+{
+    int minw = (w1 < w2) ? w1 : w2;
+    int minh = (h1 < h2) ? h1 : h2;
+    int minc = (c1 < c2) ? c1 : c2;
+
+    int stride = w1/w2;
+    int sample = w2/w1;
+    assert(stride == h1/h2);
+    assert(sample == h2/h1);
+    if(stride < 1) stride = 1;
+    if(sample < 1) sample = 1;
+
+    int size = batch * minw * minh * minc;
+    shortcut_kernel<<<cuda_gridsize(size), BLOCK>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, s1, s2, out);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        float diff = truth[i] - pred[i];
+        float abs_val = fabsf(diff);
+        if(abs_val < 1) {
+            error[i] = diff * diff;
+            delta[i] = diff;
+        }
+        else {
+            error[i] = 2*abs_val - 1;
+            delta[i] = (diff > 0) ? 1 : -1;
+        }
+    }
+}
+
+extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    smooth_l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        float t = truth[i];
+        float p = pred[i];
+        error[i] = (t) ? -log(p) : 0;
+        delta[i] = t-p;
+    }
+}
+
+extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    softmax_x_ent_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void logistic_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        float t = truth[i];
+        float p = pred[i];
+        error[i] = -t*log(p+.0000001) - (1-t)*log(1-p+.0000001);
+        delta[i] = t-p;
+    }
+}
+
+extern "C" void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    logistic_x_ent_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        float diff = truth[i] - pred[i];
+        error[i] = diff * diff; //I know this is technically wrong, deal with it.
+        delta[i] = diff;
+    }
+}
+
+extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    l2_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        float diff = truth[i] - pred[i];
+        error[i] = abs(diff);
+        delta[i] = (diff > 0) ? 1 : -1;
+    }
+}
+
+extern "C" void l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void wgan_kernel(int n, float *pred, float *truth, float *delta, float *error)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        error[i] = truth[i] ? -pred[i] : pred[i];
+        delta[i] = (truth[i] > 0) ? 1 : -1;
+    }
+}
+
+extern "C" void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error)
+{
+    wgan_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
+    check_error(cudaPeekAtLastError());
+}
+
+
+
+
+__global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
+    }
+}
+
+__global__ void deinter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < (NX+NY)*B){
+        int b = i / (NX+NY);
+        int j = i % (NX+NY);
+        if (j < NX){
+            if(X) X[b*NX + j] += OUT[i];
+        } else {
+            if(Y) Y[b*NY + j - NX] += OUT[i];
+        }
+    }
+}
+
+extern "C" void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    deinter_kernel<<<cuda_gridsize((NX+NY)*B), BLOCK>>>(NX, X, NY, Y, B, OUT);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void inter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < (NX+NY)*B){
+        int b = i / (NX+NY);
+        int j = i % (NX+NY);
+        if (j < NX){
+            OUT[i] = X[b*NX + j];
+        } else {
+            OUT[i] = Y[b*NY + j - NX];
+        }
+    }
+}
+
+extern "C" void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
+{
+    inter_kernel<<<cuda_gridsize((NX+NY)*B), BLOCK>>>(NX, X, NY, Y, B, OUT);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c)
+{
+    weighted_sum_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, s, c);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        if(da) da[i] += dc[i] * s[i];
+        if(db) db[i] += dc[i] * (1-s[i]);
+        ds[i] += dc[i] * (a[i] - b[i]);
+    }
+}
+
+extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc)
+{
+    weighted_delta_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, s, da, db, ds, dc);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void mult_add_into_kernel(int n, float *a, float *b, float *c)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i < n){
+        c[i] += a[i]*b[i];
+    }
+}
+
+extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c)
+{
+    mult_add_into_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, c);
+    check_error(cudaPeekAtLastError());
+}
+
+
+__device__ void softmax_device(float *input, int n, float temp, int stride, float *output)
+{
+    int i;
+    float sum = 0;
+    float largest = -INFINITY;
+    for(i = 0; i < n; ++i){
+        int val = input[i*stride];
+        largest = (val>largest) ? val : largest;
+    }
+    for(i = 0; i < n; ++i){
+        float e = expf(input[i*stride]/temp - largest/temp);
+        sum += e;
+        output[i*stride] = e;
+    }
+    for(i = 0; i < n; ++i){
+        output[i*stride] /= sum;
+    }
+}
+
+
+__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (id >= spatial*batch*groups) return;
+    int s = id % spatial;
+    id = id / spatial;
+    int g = id % groups;
+    int b = id / groups;
+    int goff = group_offset[g]*spatial;
+    int boff = b*stride;
+    softmax_device(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s);
+}
+
+extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier)
+{
+    int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
+    int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
+    /*
+       static int *tree_groups_size = 0;
+       static int *tree_groups_offset = 0;
+       if(!tree_groups_size){
+       tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
+       tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
+       }
+     */
+    int num = spatial*batch*hier.groups;
+    softmax_tree_kernel<<<cuda_gridsize(num), BLOCK>>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset);
+    check_error(cudaPeekAtLastError());
+    cuda_free((float *)tree_groups_size);
+    cuda_free((float *)tree_groups_offset);
+}
+
+__global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (id >= batch*groups) return;
+    int b = id / groups;
+    int g = id % groups;
+    softmax_device(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset);
+}
+
+extern "C" void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
+{
+    softmax_kernel<<<cuda_gridsize(batch*groups), BLOCK>>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output);
+    check_error(cudaPeekAtLastError());
+}
+
+
+__global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
+{
+    size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(i >= N) return;
+    int out_index = i;
+    int out_w = i%(w*stride);
+    i = i/(w*stride);
+    int out_h = i%(h*stride);
+    i = i/(h*stride);
+    int out_c = i%c;
+    i = i/c;
+    int b = i%batch;
+
+    int in_w = out_w / stride;
+    int in_h = out_h / stride;
+    int in_c = out_c;
+
+    int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w;
+
+
+    if(forward) out[out_index] += scale * x[in_index];
+    else atomicAdd(x+in_index, scale * out[out_index]);
+}
+extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
+{
+    size_t size = w*h*c*batch*stride*stride;
+    upsample_kernel<<<cuda_gridsize(size), BLOCK>>>(size, in, w, h, c, batch, stride, forward, scale, out);
+    check_error(cudaPeekAtLastError());
+}
diff --git a/hanzi_detection/src/box.c b/hanzi_detection/src/box.c
new file mode 100755
index 0000000..8a1772c
--- /dev/null
+++ b/hanzi_detection/src/box.c
@@ -0,0 +1,357 @@
+#include "box.h"
+#include <stdio.h>
+#include <math.h>
+#include <stdlib.h>
+
+int nms_comparator(const void *pa, const void *pb)
+{
+    detection a = *(detection *)pa;
+    detection b = *(detection *)pb;
+    float diff = 0;
+    if(b.sort_class >= 0){
+        diff = a.prob[b.sort_class] - b.prob[b.sort_class];
+    } else {
+        diff = a.objectness - b.objectness;
+    }
+    if(diff < 0) return 1;
+    else if(diff > 0) return -1;
+    return 0;
+}
+
+void do_nms_obj(detection *dets, int total, int classes, float thresh)
+{
+    int i, j, k;
+    k = total-1;
+    for(i = 0; i <= k; ++i){
+        if(dets[i].objectness == 0){
+            detection swap = dets[i];
+            dets[i] = dets[k];
+            dets[k] = swap;
+            --k;
+            --i;
+        }
+    }
+    total = k+1;
+
+    for(i = 0; i < total; ++i){
+        dets[i].sort_class = -1;
+    }
+
+    qsort(dets, total, sizeof(detection), nms_comparator);
+    for(i = 0; i < total; ++i){
+        if(dets[i].objectness == 0) continue;
+        box a = dets[i].bbox;
+        for(j = i+1; j < total; ++j){
+            if(dets[j].objectness == 0) continue;
+            box b = dets[j].bbox;
+            if (box_iou(a, b) > thresh){
+                dets[j].objectness = 0;
+                for(k = 0; k < classes; ++k){
+                    dets[j].prob[k] = 0;
+                }
+            }
+        }
+    }
+}
+
+
+void do_nms_sort(detection *dets, int total, int classes, float thresh)
+{
+    int i, j, k;
+    k = total-1;
+    for(i = 0; i <= k; ++i){
+        if(dets[i].objectness == 0){
+            detection swap = dets[i];
+            dets[i] = dets[k];
+            dets[k] = swap;
+            --k;
+            --i;
+        }
+    }
+    total = k+1;
+
+    for(k = 0; k < classes; ++k){
+        for(i = 0; i < total; ++i){
+            dets[i].sort_class = k;
+        }
+        qsort(dets, total, sizeof(detection), nms_comparator);
+        for(i = 0; i < total; ++i){
+            if(dets[i].prob[k] == 0) continue;
+            box a = dets[i].bbox;
+            for(j = i+1; j < total; ++j){
+                box b = dets[j].bbox;
+                if (box_iou(a, b) > thresh){
+                    dets[j].prob[k] = 0;
+                }
+            }
+        }
+    }
+}
+
+box float_to_box(float *f, int stride)
+{
+    box b = {0};
+    b.x = f[0];
+    b.y = f[1*stride];
+    b.w = f[2*stride];
+    b.h = f[3*stride];
+    return b;
+}
+
+dbox derivative(box a, box b)
+{
+    dbox d;
+    d.dx = 0;
+    d.dw = 0;
+    float l1 = a.x - a.w/2;
+    float l2 = b.x - b.w/2;
+    if (l1 > l2){
+        d.dx -= 1;
+        d.dw += .5;
+    }
+    float r1 = a.x + a.w/2;
+    float r2 = b.x + b.w/2;
+    if(r1 < r2){
+        d.dx += 1;
+        d.dw += .5;
+    }
+    if (l1 > r2) {
+        d.dx = -1;
+        d.dw = 0;
+    }
+    if (r1 < l2){
+        d.dx = 1;
+        d.dw = 0;
+    }
+
+    d.dy = 0;
+    d.dh = 0;
+    float t1 = a.y - a.h/2;
+    float t2 = b.y - b.h/2;
+    if (t1 > t2){
+        d.dy -= 1;
+        d.dh += .5;
+    }
+    float b1 = a.y + a.h/2;
+    float b2 = b.y + b.h/2;
+    if(b1 < b2){
+        d.dy += 1;
+        d.dh += .5;
+    }
+    if (t1 > b2) {
+        d.dy = -1;
+        d.dh = 0;
+    }
+    if (b1 < t2){
+        d.dy = 1;
+        d.dh = 0;
+    }
+    return d;
+}
+
+float overlap(float x1, float w1, float x2, float w2)
+{
+    float l1 = x1 - w1/2;
+    float l2 = x2 - w2/2;
+    float left = l1 > l2 ? l1 : l2;
+    float r1 = x1 + w1/2;
+    float r2 = x2 + w2/2;
+    float right = r1 < r2 ? r1 : r2;
+    return right - left;
+}
+
+float box_intersection(box a, box b)
+{
+    float w = overlap(a.x, a.w, b.x, b.w);
+    float h = overlap(a.y, a.h, b.y, b.h);
+    if(w < 0 || h < 0) return 0;
+    float area = w*h;
+    return area;
+}
+
+float box_union(box a, box b)
+{
+    float i = box_intersection(a, b);
+    float u = a.w*a.h + b.w*b.h - i;
+    return u;
+}
+
+float box_iou(box a, box b)
+{
+    return box_intersection(a, b)/box_union(a, b);
+}
+
+float box_rmse(box a, box b)
+{
+    return sqrt(pow(a.x-b.x, 2) + 
+                pow(a.y-b.y, 2) + 
+                pow(a.w-b.w, 2) + 
+                pow(a.h-b.h, 2));
+}
+
+dbox dintersect(box a, box b)
+{
+    float w = overlap(a.x, a.w, b.x, b.w);
+    float h = overlap(a.y, a.h, b.y, b.h);
+    dbox dover = derivative(a, b);
+    dbox di;
+
+    di.dw = dover.dw*h;
+    di.dx = dover.dx*h;
+    di.dh = dover.dh*w;
+    di.dy = dover.dy*w;
+
+    return di;
+}
+
+dbox dunion(box a, box b)
+{
+    dbox du;
+
+    dbox di = dintersect(a, b);
+    du.dw = a.h - di.dw;
+    du.dh = a.w - di.dh;
+    du.dx = -di.dx;
+    du.dy = -di.dy;
+
+    return du;
+}
+
+
+void test_dunion()
+{
+    box a = {0, 0, 1, 1};
+    box dxa= {0+.0001, 0, 1, 1};
+    box dya= {0, 0+.0001, 1, 1};
+    box dwa= {0, 0, 1+.0001, 1};
+    box dha= {0, 0, 1, 1+.0001};
+
+    box b = {.5, .5, .2, .2};
+    dbox di = dunion(a,b);
+    printf("Union: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh);
+    float inter =  box_union(a, b);
+    float xinter = box_union(dxa, b);
+    float yinter = box_union(dya, b);
+    float winter = box_union(dwa, b);
+    float hinter = box_union(dha, b);
+    xinter = (xinter - inter)/(.0001);
+    yinter = (yinter - inter)/(.0001);
+    winter = (winter - inter)/(.0001);
+    hinter = (hinter - inter)/(.0001);
+    printf("Union Manual %f %f %f %f\n", xinter, yinter, winter, hinter);
+}
+void test_dintersect()
+{
+    box a = {0, 0, 1, 1};
+    box dxa= {0+.0001, 0, 1, 1};
+    box dya= {0, 0+.0001, 1, 1};
+    box dwa= {0, 0, 1+.0001, 1};
+    box dha= {0, 0, 1, 1+.0001};
+
+    box b = {.5, .5, .2, .2};
+    dbox di = dintersect(a,b);
+    printf("Inter: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh);
+    float inter =  box_intersection(a, b);
+    float xinter = box_intersection(dxa, b);
+    float yinter = box_intersection(dya, b);
+    float winter = box_intersection(dwa, b);
+    float hinter = box_intersection(dha, b);
+    xinter = (xinter - inter)/(.0001);
+    yinter = (yinter - inter)/(.0001);
+    winter = (winter - inter)/(.0001);
+    hinter = (hinter - inter)/(.0001);
+    printf("Inter Manual %f %f %f %f\n", xinter, yinter, winter, hinter);
+}
+
+void test_box()
+{
+    test_dintersect();
+    test_dunion();
+    box a = {0, 0, 1, 1};
+    box dxa= {0+.00001, 0, 1, 1};
+    box dya= {0, 0+.00001, 1, 1};
+    box dwa= {0, 0, 1+.00001, 1};
+    box dha= {0, 0, 1, 1+.00001};
+
+    box b = {.5, 0, .2, .2};
+
+    float iou = box_iou(a,b);
+    iou = (1-iou)*(1-iou);
+    printf("%f\n", iou);
+    dbox d = diou(a, b);
+    printf("%f %f %f %f\n", d.dx, d.dy, d.dw, d.dh);
+
+    float xiou = box_iou(dxa, b);
+    float yiou = box_iou(dya, b);
+    float wiou = box_iou(dwa, b);
+    float hiou = box_iou(dha, b);
+    xiou = ((1-xiou)*(1-xiou) - iou)/(.00001);
+    yiou = ((1-yiou)*(1-yiou) - iou)/(.00001);
+    wiou = ((1-wiou)*(1-wiou) - iou)/(.00001);
+    hiou = ((1-hiou)*(1-hiou) - iou)/(.00001);
+    printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou);
+}
+
+dbox diou(box a, box b)
+{
+    float u = box_union(a,b);
+    float i = box_intersection(a,b);
+    dbox di = dintersect(a,b);
+    dbox du = dunion(a,b);
+    dbox dd = {0,0,0,0};
+
+    if(i <= 0 || 1) {
+        dd.dx = b.x - a.x;
+        dd.dy = b.y - a.y;
+        dd.dw = b.w - a.w;
+        dd.dh = b.h - a.h;
+        return dd;
+    }
+
+    dd.dx = 2*pow((1-(i/u)),1)*(di.dx*u - du.dx*i)/(u*u);
+    dd.dy = 2*pow((1-(i/u)),1)*(di.dy*u - du.dy*i)/(u*u);
+    dd.dw = 2*pow((1-(i/u)),1)*(di.dw*u - du.dw*i)/(u*u);
+    dd.dh = 2*pow((1-(i/u)),1)*(di.dh*u - du.dh*i)/(u*u);
+    return dd;
+}
+
+
+void do_nms(box *boxes, float **probs, int total, int classes, float thresh)
+{
+    int i, j, k;
+    for(i = 0; i < total; ++i){
+        int any = 0;
+        for(k = 0; k < classes; ++k) any = any || (probs[i][k] > 0);
+        if(!any) {
+            continue;
+        }
+        for(j = i+1; j < total; ++j){
+            if (box_iou(boxes[i], boxes[j]) > thresh){
+                for(k = 0; k < classes; ++k){
+                    if (probs[i][k] < probs[j][k]) probs[i][k] = 0;
+                    else probs[j][k] = 0;
+                }
+            }
+        }
+    }
+}
+
+box encode_box(box b, box anchor)
+{
+    box encode;
+    encode.x = (b.x - anchor.x) / anchor.w;
+    encode.y = (b.y - anchor.y) / anchor.h;
+    encode.w = log2(b.w / anchor.w);
+    encode.h = log2(b.h / anchor.h);
+    return encode;
+}
+
+box decode_box(box b, box anchor)
+{
+    box decode;
+    decode.x = b.x * anchor.w + anchor.x;
+    decode.y = b.y * anchor.h + anchor.y;
+    decode.w = pow(2., b.w) * anchor.w;
+    decode.h = pow(2., b.h) * anchor.h;
+    return decode;
+}
diff --git a/hanzi_detection/src/box.h b/hanzi_detection/src/box.h
new file mode 100755
index 0000000..dda3e59
--- /dev/null
+++ b/hanzi_detection/src/box.h
@@ -0,0 +1,14 @@
+#ifndef BOX_H
+#define BOX_H
+#include "darknet.h"
+
+typedef struct{
+    float dx, dy, dw, dh;
+} dbox;
+
+float box_rmse(box a, box b);
+dbox diou(box a, box b);
+box decode_box(box b, box anchor);
+box encode_box(box b, box anchor);
+
+#endif
diff --git a/hanzi_detection/src/classifier.h b/hanzi_detection/src/classifier.h
new file mode 100755
index 0000000..8b13789
--- /dev/null
+++ b/hanzi_detection/src/classifier.h
@@ -0,0 +1 @@
+
diff --git a/hanzi_detection/src/col2im.c b/hanzi_detection/src/col2im.c
new file mode 100755
index 0000000..5c4605e
--- /dev/null
+++ b/hanzi_detection/src/col2im.c
@@ -0,0 +1,39 @@
+#include <stdio.h>
+#include <math.h>
+void col2im_add_pixel(float *im, int height, int width, int channels,
+                        int row, int col, int channel, int pad, float val)
+{
+    row -= pad;
+    col -= pad;
+
+    if (row < 0 || col < 0 ||
+        row >= height || col >= width) return;
+    im[col + width*(row + height*channel)] += val;
+}
+//This one might be too, can't remember.
+void col2im_cpu(float* data_col,
+         int channels,  int height,  int width,
+         int ksize,  int stride, int pad, float* data_im) 
+{
+    int c,h,w;
+    int height_col = (height + 2*pad - ksize) / stride + 1;
+    int width_col = (width + 2*pad - ksize) / stride + 1;
+
+    int channels_col = channels * ksize * ksize;
+    for (c = 0; c < channels_col; ++c) {
+        int w_offset = c % ksize;
+        int h_offset = (c / ksize) % ksize;
+        int c_im = c / ksize / ksize;
+        for (h = 0; h < height_col; ++h) {
+            for (w = 0; w < width_col; ++w) {
+                int im_row = h_offset + h * stride;
+                int im_col = w_offset + w * stride;
+                int col_index = (c * height_col + h) * width_col + w;
+                double val = data_col[col_index];
+                col2im_add_pixel(data_im, height, width, channels,
+                        im_row, im_col, c_im, pad, val);
+            }
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/col2im.h b/hanzi_detection/src/col2im.h
new file mode 100755
index 0000000..3fbe053
--- /dev/null
+++ b/hanzi_detection/src/col2im.h
@@ -0,0 +1,13 @@
+#ifndef COL2IM_H
+#define COL2IM_H
+
+void col2im_cpu(float* data_col,
+        int channels, int height, int width,
+        int ksize, int stride, int pad, float* data_im);
+
+#ifdef GPU
+void col2im_gpu(float *data_col,
+        int channels, int height, int width,
+        int ksize, int stride, int pad, float *data_im);
+#endif
+#endif
diff --git a/hanzi_detection/src/col2im_kernels.cu b/hanzi_detection/src/col2im_kernels.cu
new file mode 100755
index 0000000..ba45e0f
--- /dev/null
+++ b/hanzi_detection/src/col2im_kernels.cu
@@ -0,0 +1,58 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "col2im.h"
+#include "cuda.h"
+}
+
+// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
+// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE
+
+__global__ void col2im_gpu_kernel(const int n, const float* data_col,
+        const int height, const int width, const int ksize,
+        const int pad,
+        const int stride,
+        const int height_col, const int width_col,
+        float *data_im) {
+    int index = blockIdx.x*blockDim.x+threadIdx.x;
+    for(; index < n; index += blockDim.x*gridDim.x){
+        float val = 0;
+        int w = index % width + pad;
+        int h = (index / width) % height + pad;
+        int c = index / (width * height);
+        // compute the start and end of the output
+        int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
+        int w_col_end = min(w / stride + 1, width_col);
+        int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
+        int h_col_end = min(h / stride + 1, height_col);
+        // equivalent implementation
+        int offset =
+            (c * ksize * ksize + h * ksize + w) * height_col * width_col;
+        int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
+        int coeff_w_col = (1 - stride * height_col * width_col);
+        for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
+            for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
+                val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
+            }
+        }
+        data_im[index] += val;
+    }
+}
+
+void col2im_gpu(float *data_col,
+        int channels, int height, int width,
+        int ksize, int stride, int pad, float *data_im){
+    // We are going to launch channels * height_col * width_col kernels, each
+    // kernel responsible for copying a single-channel grid.
+    int height_col = (height + 2 * pad - ksize) / stride + 1;
+    int width_col = (width + 2 * pad - ksize) / stride + 1;
+    int num_kernels = channels * height * width;
+    col2im_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
+        BLOCK>>>(
+                num_kernels, data_col, height, width, ksize, pad,
+                stride, height_col,
+                width_col, data_im);
+}
+
diff --git a/hanzi_detection/src/compare.c b/hanzi_detection/src/compare.c
new file mode 100755
index 0000000..d2d2b3b
--- /dev/null
+++ b/hanzi_detection/src/compare.c
@@ -0,0 +1,352 @@
+#include <stdio.h>
+
+#include "network.h"
+#include "detection_layer.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "box.h"
+
+void train_compare(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    char *backup_directory = "/home/pjreddie/backup/";
+    printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    int imgs = 1024;
+    list *plist = get_paths("data/compare.train.list");
+    char **paths = (char **)list_to_array(plist);
+    int N = plist->size;
+    printf("%d\n", N);
+    clock_t time;
+    pthread_t load_thread;
+    data train;
+    data buffer;
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.paths = paths;
+    args.classes = 20;
+    args.n = imgs;
+    args.m = N;
+    args.d = &buffer;
+    args.type = COMPARE_DATA;
+
+    load_thread = load_data_in_thread(args);
+    int epoch = *net.seen/N;
+    int i = 0;
+    while(1){
+        ++i;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+
+        load_thread = load_data_in_thread(args);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen);
+        free_data(train);
+        if(i%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d_minor_%d.weights",backup_directory,base, epoch, i);
+            save_weights(net, buff);
+        }
+        if(*net.seen/N > epoch){
+            epoch = *net.seen/N;
+            i = 0;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+            if(epoch%22 == 0) net.learning_rate *= .1;
+        }
+    }
+    pthread_join(load_thread, 0);
+    free_data(buffer);
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void validate_compare(char *filename, char *weightfile)
+{
+    int i = 0;
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+
+    list *plist = get_paths("data/compare.val.list");
+    //list *plist = get_paths("data/compare.val.old");
+    char **paths = (char **)list_to_array(plist);
+    int N = plist->size/2;
+    free_list(plist);
+
+    clock_t time;
+    int correct = 0;
+    int total = 0;
+    int splits = 10;
+    int num = (i+1)*N/splits - i*N/splits;
+
+    data val, buffer;
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.paths = paths;
+    args.classes = 20;
+    args.n = num;
+    args.m = 0;
+    args.d = &buffer;
+    args.type = COMPARE_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    for(i = 1; i <= splits; ++i){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        val = buffer;
+
+        num = (i+1)*N/splits - i*N/splits;
+        char **part = paths+(i*N/splits);
+        if(i != splits){
+            args.paths = part;
+            load_thread = load_data_in_thread(args);
+        }
+        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+        time=clock();
+        matrix pred = network_predict_data(net, val);
+        int j,k;
+        for(j = 0; j < val.y.rows; ++j){
+            for(k = 0; k < 20; ++k){
+                if(val.y.vals[j][k*2] != val.y.vals[j][k*2+1]){
+                    ++total;
+                    if((val.y.vals[j][k*2] < val.y.vals[j][k*2+1]) == (pred.vals[j][k*2] < pred.vals[j][k*2+1])){
+                        ++correct;
+                    }
+                }
+            }
+        }
+        free_matrix(pred);
+        printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float)correct/total, sec(clock()-time), val.X.rows);
+        free_data(val);
+    }
+}
+
+typedef struct {
+    network net;
+    char *filename;
+    int class;
+    int classes;
+    float elo;
+    float *elos;
+} sortable_bbox;
+
+int total_compares = 0;
+int current_class = 0;
+
+int elo_comparator(const void*a, const void *b)
+{
+    sortable_bbox box1 = *(sortable_bbox*)a;
+    sortable_bbox box2 = *(sortable_bbox*)b;
+    if(box1.elos[current_class] == box2.elos[current_class]) return 0;
+    if(box1.elos[current_class] >  box2.elos[current_class]) return -1;
+    return 1;
+}
+
+int bbox_comparator(const void *a, const void *b)
+{
+    ++total_compares;
+    sortable_bbox box1 = *(sortable_bbox*)a;
+    sortable_bbox box2 = *(sortable_bbox*)b;
+    network net = box1.net;
+    int class   = box1.class;
+
+    image im1 = load_image_color(box1.filename, net.w, net.h);
+    image im2 = load_image_color(box2.filename, net.w, net.h);
+    float *X  = calloc(net.w*net.h*net.c, sizeof(float));
+    memcpy(X,                   im1.data, im1.w*im1.h*im1.c*sizeof(float));
+    memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
+    float *predictions = network_predict(net, X);
+    
+    free_image(im1);
+    free_image(im2);
+    free(X);
+    if (predictions[class*2] > predictions[class*2+1]){
+        return 1;
+    }
+    return -1;
+}
+
+void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result)
+{
+    int k = 32;
+    float EA = 1./(1+pow(10, (b->elos[class] - a->elos[class])/400.));
+    float EB = 1./(1+pow(10, (a->elos[class] - b->elos[class])/400.));
+    float SA = result ? 1 : 0;
+    float SB = result ? 0 : 1;
+    a->elos[class] += k*(SA - EA);
+    b->elos[class] += k*(SB - EB);
+}
+
+void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, int class)
+{
+    image im1 = load_image_color(a->filename, net.w, net.h);
+    image im2 = load_image_color(b->filename, net.w, net.h);
+    float *X  = calloc(net.w*net.h*net.c, sizeof(float));
+    memcpy(X,                   im1.data, im1.w*im1.h*im1.c*sizeof(float));
+    memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
+    float *predictions = network_predict(net, X);
+    ++total_compares;
+
+    int i;
+    for(i = 0; i < classes; ++i){
+        if(class < 0 || class == i){
+            int result = predictions[i*2] > predictions[i*2+1];
+            bbox_update(a, b, i, result);
+        }
+    }
+    
+    free_image(im1);
+    free_image(im2);
+    free(X);
+}
+
+void SortMaster3000(char *filename, char *weightfile)
+{
+    int i = 0;
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+    set_batch_network(&net, 1);
+
+    list *plist = get_paths("data/compare.sort.list");
+    //list *plist = get_paths("data/compare.val.old");
+    char **paths = (char **)list_to_array(plist);
+    int N = plist->size;
+    free_list(plist);
+    sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox));
+    printf("Sorting %d boxes...\n", N);
+    for(i = 0; i < N; ++i){
+        boxes[i].filename = paths[i];
+        boxes[i].net = net;
+        boxes[i].class = 7;
+        boxes[i].elo = 1500;
+    }
+    clock_t time=clock();
+    qsort(boxes, N, sizeof(sortable_bbox), bbox_comparator);
+    for(i = 0; i < N; ++i){
+        printf("%s\n", boxes[i].filename);
+    }
+    printf("Sorted in %d compares, %f secs\n", total_compares, sec(clock()-time));
+}
+
+void BattleRoyaleWithCheese(char *filename, char *weightfile)
+{
+    int classes = 20;
+    int i,j;
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+    set_batch_network(&net, 1);
+
+    list *plist = get_paths("data/compare.sort.list");
+    //list *plist = get_paths("data/compare.small.list");
+    //list *plist = get_paths("data/compare.cat.list");
+    //list *plist = get_paths("data/compare.val.old");
+    char **paths = (char **)list_to_array(plist);
+    int N = plist->size;
+    int total = N;
+    free_list(plist);
+    sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox));
+    printf("Battling %d boxes...\n", N);
+    for(i = 0; i < N; ++i){
+        boxes[i].filename = paths[i];
+        boxes[i].net = net;
+        boxes[i].classes = classes;
+        boxes[i].elos = calloc(classes, sizeof(float));;
+        for(j = 0; j < classes; ++j){
+            boxes[i].elos[j] = 1500;
+        }
+    }
+    int round;
+    clock_t time=clock();
+    for(round = 1; round <= 4; ++round){
+        clock_t round_time=clock();
+        printf("Round: %d\n", round);
+        shuffle(boxes, N, sizeof(sortable_bbox));
+        for(i = 0; i < N/2; ++i){
+            bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, -1);
+        }
+        printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N);
+    }
+
+    int class;
+
+    for (class = 0; class < classes; ++class){
+
+        N = total;
+        current_class = class;
+        qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
+        N /= 2;
+
+        for(round = 1; round <= 100; ++round){
+            clock_t round_time=clock();
+            printf("Round: %d\n", round);
+
+            sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10);
+            for(i = 0; i < N/2; ++i){
+                bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, class);
+            }
+            qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
+            if(round <= 20) N = (N*9/10)/2*2;
+
+            printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N);
+        }
+        char buff[256];
+        sprintf(buff, "results/battle_%d.log", class);
+        FILE *outfp = fopen(buff, "w");
+        for(i = 0; i < N; ++i){
+            fprintf(outfp, "%s %f\n", boxes[i].filename, boxes[i].elos[class]);
+        }
+        fclose(outfp);
+    }
+    printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time));
+}
+
+void run_compare(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    //char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "train")) train_compare(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_compare(cfg, weights);
+    else if(0==strcmp(argv[2], "sort")) SortMaster3000(cfg, weights);
+    else if(0==strcmp(argv[2], "battle")) BattleRoyaleWithCheese(cfg, weights);
+    /*
+       else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
+       else if(0==strcmp(argv[2], "extract")) extract_boxes(cfg, weights);
+       else if(0==strcmp(argv[2], "valid")) validate_recall(cfg, weights);
+     */
+}
diff --git a/hanzi_detection/src/connected_layer.c b/hanzi_detection/src/connected_layer.c
new file mode 100755
index 0000000..353f4e5
--- /dev/null
+++ b/hanzi_detection/src/connected_layer.c
@@ -0,0 +1,336 @@
+#include "connected_layer.h"
+#include "convolutional_layer.h"
+#include "batchnorm_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam)
+{
+    int i;
+    layer l = {0};
+    l.learning_rate_scale = 1;
+    l.type = CONNECTED;
+
+    l.inputs = inputs;
+    l.outputs = outputs;
+    l.batch=batch;
+    l.batch_normalize = batch_normalize;
+    l.h = 1;
+    l.w = 1;
+    l.c = inputs;
+    l.out_h = 1;
+    l.out_w = 1;
+    l.out_c = outputs;
+
+    l.output = calloc(batch*outputs, sizeof(float));
+    l.delta = calloc(batch*outputs, sizeof(float));
+
+    l.weight_updates = calloc(inputs*outputs, sizeof(float));
+    l.bias_updates = calloc(outputs, sizeof(float));
+
+    l.weights = calloc(outputs*inputs, sizeof(float));
+    l.biases = calloc(outputs, sizeof(float));
+
+    l.forward = forward_connected_layer;
+    l.backward = backward_connected_layer;
+    l.update = update_connected_layer;
+
+    //float scale = 1./sqrt(inputs);
+    float scale = sqrt(2./inputs);
+    for(i = 0; i < outputs*inputs; ++i){
+        l.weights[i] = scale*rand_uniform(-1, 1);
+    }
+
+    for(i = 0; i < outputs; ++i){
+        l.biases[i] = 0;
+    }
+
+    if(adam){
+        l.m = calloc(l.inputs*l.outputs, sizeof(float));
+        l.v = calloc(l.inputs*l.outputs, sizeof(float));
+        l.bias_m = calloc(l.outputs, sizeof(float));
+        l.scale_m = calloc(l.outputs, sizeof(float));
+        l.bias_v = calloc(l.outputs, sizeof(float));
+        l.scale_v = calloc(l.outputs, sizeof(float));
+    }
+    if(batch_normalize){
+        l.scales = calloc(outputs, sizeof(float));
+        l.scale_updates = calloc(outputs, sizeof(float));
+        for(i = 0; i < outputs; ++i){
+            l.scales[i] = 1;
+        }
+
+        l.mean = calloc(outputs, sizeof(float));
+        l.mean_delta = calloc(outputs, sizeof(float));
+        l.variance = calloc(outputs, sizeof(float));
+        l.variance_delta = calloc(outputs, sizeof(float));
+
+        l.rolling_mean = calloc(outputs, sizeof(float));
+        l.rolling_variance = calloc(outputs, sizeof(float));
+
+        l.x = calloc(batch*outputs, sizeof(float));
+        l.x_norm = calloc(batch*outputs, sizeof(float));
+    }
+
+#ifdef GPU
+    l.forward_gpu = forward_connected_layer_gpu;
+    l.backward_gpu = backward_connected_layer_gpu;
+    l.update_gpu = update_connected_layer_gpu;
+
+    l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
+    l.biases_gpu = cuda_make_array(l.biases, outputs);
+
+    l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
+    l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
+
+    l.output_gpu = cuda_make_array(l.output, outputs*batch);
+    l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
+    if (adam) {
+        l.m_gpu =       cuda_make_array(0, inputs*outputs);
+        l.v_gpu =       cuda_make_array(0, inputs*outputs);
+        l.bias_m_gpu =  cuda_make_array(0, outputs);
+        l.bias_v_gpu =  cuda_make_array(0, outputs);
+        l.scale_m_gpu = cuda_make_array(0, outputs);
+        l.scale_v_gpu = cuda_make_array(0, outputs);
+    }
+
+    if(batch_normalize){
+        l.mean_gpu = cuda_make_array(l.mean, outputs);
+        l.variance_gpu = cuda_make_array(l.variance, outputs);
+
+        l.rolling_mean_gpu = cuda_make_array(l.mean, outputs);
+        l.rolling_variance_gpu = cuda_make_array(l.variance, outputs);
+
+        l.mean_delta_gpu = cuda_make_array(l.mean, outputs);
+        l.variance_delta_gpu = cuda_make_array(l.variance, outputs);
+
+        l.scales_gpu = cuda_make_array(l.scales, outputs);
+        l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs);
+
+        l.x_gpu = cuda_make_array(l.output, l.batch*outputs);
+        l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs);
+#ifdef CUDNN
+        cudnnCreateTensorDescriptor(&l.normTensorDesc);
+        cudnnCreateTensorDescriptor(&l.dstTensorDesc);
+        cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
+        cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); 
+#endif
+    }
+#endif
+    l.activation = activation;
+    fprintf(stderr, "connected                            %4d  ->  %4d\n", inputs, outputs);
+    return l;
+}
+
+void update_connected_layer(layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+    axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+    scal_cpu(l.outputs, momentum, l.bias_updates, 1);
+
+    if(l.batch_normalize){
+        axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
+        scal_cpu(l.outputs, momentum, l.scale_updates, 1);
+    }
+
+    axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
+    axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+    scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
+}
+
+void forward_connected_layer(layer l, network net)
+{
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+    int m = l.batch;
+    int k = l.inputs;
+    int n = l.outputs;
+    float *a = net.input;
+    float *b = l.weights;
+    float *c = l.output;
+    gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+    if(l.batch_normalize){
+        forward_batchnorm_layer(l, net);
+    } else {
+        add_bias(l.output, l.biases, l.batch, l.outputs, 1);
+    }
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_connected_layer(layer l, network net)
+{
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+
+    if(l.batch_normalize){
+        backward_batchnorm_layer(l, net);
+    } else {
+        backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1);
+    }
+
+    int m = l.outputs;
+    int k = l.batch;
+    int n = l.inputs;
+    float *a = l.delta;
+    float *b = net.input;
+    float *c = l.weight_updates;
+    gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
+
+    m = l.batch;
+    k = l.outputs;
+    n = l.inputs;
+
+    a = l.delta;
+    b = l.weights;
+    c = net.delta;
+
+    if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+}
+
+
+void denormalize_connected_layer(layer l)
+{
+    int i, j;
+    for(i = 0; i < l.outputs; ++i){
+        float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001);
+        for(j = 0; j < l.inputs; ++j){
+            l.weights[i*l.inputs + j] *= scale;
+        }
+        l.biases[i] -= l.rolling_mean[i] * scale;
+        l.scales[i] = 1;
+        l.rolling_mean[i] = 0;
+        l.rolling_variance[i] = 1;
+    }
+}
+
+
+void statistics_connected_layer(layer l)
+{
+    if(l.batch_normalize){
+        printf("Scales ");
+        print_statistics(l.scales, l.outputs);
+        /*
+           printf("Rolling Mean ");
+           print_statistics(l.rolling_mean, l.outputs);
+           printf("Rolling Variance ");
+           print_statistics(l.rolling_variance, l.outputs);
+         */
+    }
+    printf("Biases ");
+    print_statistics(l.biases, l.outputs);
+    printf("Weights ");
+    print_statistics(l.weights, l.outputs);
+}
+
+#ifdef GPU
+
+void pull_connected_layer(layer l)
+{
+    cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
+    cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
+    cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
+    cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+    if (l.batch_normalize){
+        cuda_pull_array(l.scales_gpu, l.scales, l.outputs);
+        cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
+        cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
+    }
+}
+
+void push_connected_layer(layer l)
+{
+    cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
+    cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+    cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
+    cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+    if (l.batch_normalize){
+        cuda_push_array(l.scales_gpu, l.scales, l.outputs);
+        cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
+        cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
+    }
+}
+
+void update_connected_layer_gpu(layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+    if(a.adam){
+        adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t);
+        adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
+        if(l.scales_gpu){
+            adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
+        }
+    }else{
+        axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+        scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);
+
+        if(l.batch_normalize){
+            axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
+            scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1);
+        }
+
+        axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+        axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+        scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
+    }
+}
+
+void forward_connected_layer_gpu(layer l, network net)
+{
+    fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
+
+    int m = l.batch;
+    int k = l.inputs;
+    int n = l.outputs;
+    float * a = net.input_gpu;
+    float * b = l.weights_gpu;
+    float * c = l.output_gpu;
+    gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+    if (l.batch_normalize) {
+        forward_batchnorm_layer_gpu(l, net);
+    } else {
+        add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1);
+    }
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_connected_layer_gpu(layer l, network net)
+{
+    constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+    if(l.batch_normalize){
+        backward_batchnorm_layer_gpu(l, net);
+    } else {
+        backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1);
+    }
+
+    int m = l.outputs;
+    int k = l.batch;
+    int n = l.inputs;
+    float * a = l.delta_gpu;
+    float * b = net.input_gpu;
+    float * c = l.weight_updates_gpu;
+    gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
+
+    m = l.batch;
+    k = l.outputs;
+    n = l.inputs;
+
+    a = l.delta_gpu;
+    b = l.weights_gpu;
+    c = net.delta_gpu;
+
+    if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
+}
+#endif
diff --git a/hanzi_detection/src/connected_layer.h b/hanzi_detection/src/connected_layer.h
new file mode 100755
index 0000000..6727a96
--- /dev/null
+++ b/hanzi_detection/src/connected_layer.h
@@ -0,0 +1,23 @@
+#ifndef CONNECTED_LAYER_H
+#define CONNECTED_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam);
+
+void forward_connected_layer(layer l, network net);
+void backward_connected_layer(layer l, network net);
+void update_connected_layer(layer l, update_args a);
+
+#ifdef GPU
+void forward_connected_layer_gpu(layer l, network net);
+void backward_connected_layer_gpu(layer l, network net);
+void update_connected_layer_gpu(layer l, update_args a);
+void push_connected_layer(layer l);
+void pull_connected_layer(layer l);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/convolutional_kernels.cu b/hanzi_detection/src/convolutional_kernels.cu
new file mode 100755
index 0000000..4a1047b
--- /dev/null
+++ b/hanzi_detection/src/convolutional_kernels.cu
@@ -0,0 +1,330 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "convolutional_layer.h"
+#include "batchnorm_layer.h"
+#include "gemm.h"
+#include "blas.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "utils.h"
+#include "cuda.h"
+}
+
+__global__ void binarize_kernel(float *x, int n, float *binary)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i >= n) return;
+    binary[i] = (x[i] >= 0) ? 1 : -1;
+}
+
+void binarize_gpu(float *x, int n, float *binary)
+{
+    binarize_kernel<<<cuda_gridsize(n), BLOCK>>>(x, n, binary);
+    check_error(cudaPeekAtLastError());
+}
+
+__global__ void binarize_input_kernel(float *input, int n, int size, float *binary)
+{
+    int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (s >= size) return;
+    int i = 0;
+    float mean = 0;
+    for(i = 0; i < n; ++i){
+        mean += fabsf(input[i*size + s]);
+    }
+    mean = mean / n;
+    for(i = 0; i < n; ++i){
+        binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
+    }
+}
+
+void binarize_input_gpu(float *input, int n, int size, float *binary)
+{
+    binarize_input_kernel<<<cuda_gridsize(size), BLOCK>>>(input, n, size, binary);
+    check_error(cudaPeekAtLastError());
+}
+
+
+__global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary)
+{
+    int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (f >= n) return;
+    int i = 0;
+    float mean = 0;
+    for(i = 0; i < size; ++i){
+        mean += fabsf(weights[f*size + i]);
+    }
+    mean = mean / size;
+    for(i = 0; i < size; ++i){
+        binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
+        //binary[f*size + i] = weights[f*size + i];
+    }
+}
+
+void binarize_weights_gpu(float *weights, int n, int size, float *binary)
+{
+    binarize_weights_kernel<<<cuda_gridsize(n), BLOCK>>>(weights, n, size, binary);
+    check_error(cudaPeekAtLastError());
+}
+
+void forward_convolutional_layer_gpu(convolutional_layer l, network net)
+{
+    fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
+    if(l.binary){
+        binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu);
+        swap_binary(&l);
+    }
+
+    if(l.xnor){
+        binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu);
+        swap_binary(&l);
+        binarize_gpu(net.input_gpu, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
+        net.input_gpu = l.binary_input_gpu;
+    }
+
+#ifdef CUDNN
+    float one = 1;
+    cudnnConvolutionForward(cudnn_handle(),
+                &one,
+                l.srcTensorDesc,
+                net.input_gpu,
+                l.weightDesc,
+                l.weights_gpu,
+                l.convDesc,
+                l.fw_algo,
+                net.workspace,
+                l.workspace_size,
+                &one,
+                l.dstTensorDesc,
+                l.output_gpu);
+
+#else
+    int i, j;
+    int m = l.n/l.groups;
+    int k = l.size*l.size*l.c/l.groups;
+    int n = l.out_w*l.out_h;
+    for(i = 0; i < l.batch; ++i){
+        for(j = 0; j < l.groups; ++j){
+            float *a = l.weights_gpu + j*l.nweights/l.groups;
+            float *b = net.workspace;
+            float *c = l.output_gpu + (i*l.groups + j)*n*m;
+            float *im = net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
+
+            if (l.size == 1){
+                b = im;
+            } else {
+                im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
+            }
+            gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
+        }
+    }
+#endif
+
+    if (l.batch_normalize) {
+        forward_batchnorm_layer_gpu(l, net);
+    } else {
+        add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+    }
+
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
+    //if(l.dot > 0) dot_error_gpu(l);
+    if(l.binary || l.xnor) swap_binary(&l);
+}
+
+__global__ void smooth_kernel(float *x, int n, int w, int h, int c, int size, float rate, float *delta)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int j = id % w;
+    id /= w;
+    int i = id % h;
+    id /= h;
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int w_offset = -(size/2.f);
+    int h_offset = -(size/2.f);
+
+    int out_index = j + w*(i + h*(k + c*b));
+    int l, m;
+    for(l = 0; l < size; ++l){
+        for(m = 0; m < size; ++m){
+            int cur_h = h_offset + i + l;
+            int cur_w = w_offset + j + m;
+            int index = cur_w + w*(cur_h + h*(k + b*c));
+            int valid = (cur_h >= 0 && cur_h < h &&
+                    cur_w >= 0 && cur_w < w);
+            delta[out_index] += valid ? rate*(x[index] - x[out_index]) : 0;
+        }
+    }
+}
+
+extern "C" void smooth_layer(layer l, int size, float rate)
+{
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.out_c;
+
+    size_t n = h*w*c*l.batch;
+
+    smooth_kernel<<<cuda_gridsize(n), BLOCK>>>(l.output_gpu, n, l.w, l.h, l.c, size, rate, l.delta_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
+void backward_convolutional_layer_gpu(convolutional_layer l, network net)
+{
+    if(l.smooth){
+        smooth_layer(l, 5, l.smooth);
+    }
+    //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+
+
+    if(l.batch_normalize){
+        backward_batchnorm_layer_gpu(l, net);
+    } else {
+        backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
+    }
+    float *original_input = net.input_gpu;
+
+    if(l.xnor) net.input_gpu = l.binary_input_gpu;
+#ifdef CUDNN
+    float one = 1;
+    cudnnConvolutionBackwardFilter(cudnn_handle(),
+            &one,
+            l.srcTensorDesc,
+            net.input_gpu,
+            l.ddstTensorDesc,
+            l.delta_gpu,
+            l.convDesc,
+            l.bf_algo,
+            net.workspace,
+            l.workspace_size,
+            &one,
+            l.dweightDesc,
+            l.weight_updates_gpu);
+
+    if(net.delta_gpu){
+        if(l.binary || l.xnor) swap_binary(&l);
+        cudnnConvolutionBackwardData(cudnn_handle(),
+                &one,
+                l.weightDesc,
+                l.weights_gpu,
+                l.ddstTensorDesc,
+                l.delta_gpu,
+                l.convDesc,
+                l.bd_algo,
+                net.workspace,
+                l.workspace_size,
+                &one,
+                l.dsrcTensorDesc,
+                net.delta_gpu);
+        if(l.binary || l.xnor) swap_binary(&l);
+        if(l.xnor) gradient_array_gpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, net.delta_gpu);
+    }
+
+#else
+    int m = l.n/l.groups;
+    int n = l.size*l.size*l.c/l.groups;
+    int k = l.out_w*l.out_h;
+
+    int i, j;
+    for(i = 0; i < l.batch; ++i){
+        for(j = 0; j < l.groups; ++j){
+            float *a = l.delta_gpu + (i*l.groups + j)*m*k;
+            float *b = net.workspace;
+            float *c = l.weight_updates_gpu + j*l.nweights/l.groups;
+
+            float *im  = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
+            float *imd = net.delta_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
+
+            im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
+            gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+            if (net.delta_gpu) {
+                if (l.binary || l.xnor) swap_binary(&l);
+                a = l.weights_gpu + j*l.nweights/l.groups;
+                b = l.delta_gpu + (i*l.groups + j)*m*k;
+                c = net.workspace;
+                if (l.size == 1) {
+                    c = imd;
+                }
+
+                gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k);
+
+                if (l.size != 1) {
+                    col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
+                }
+                if(l.binary || l.xnor) {
+                    swap_binary(&l);
+                }
+            }
+            if(l.xnor) gradient_array_gpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, net.delta_gpu + i*l.c*l.h*l.w);
+        }
+    }
+#endif
+}
+
+void pull_convolutional_layer(layer l)
+{
+    cuda_pull_array(l.weights_gpu, l.weights, l.nweights);
+    cuda_pull_array(l.biases_gpu, l.biases, l.n);
+    cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+    cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
+    if (l.batch_normalize){
+        cuda_pull_array(l.scales_gpu, l.scales, l.n);
+        cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n);
+        cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n);
+    }
+}
+
+void push_convolutional_layer(layer l)
+{
+    cuda_push_array(l.weights_gpu, l.weights, l.nweights);
+    cuda_push_array(l.biases_gpu, l.biases, l.n);
+    cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+    cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
+    if (l.batch_normalize){
+        cuda_push_array(l.scales_gpu, l.scales, l.n);
+        cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n);
+        cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n);
+    }
+}
+
+void update_convolutional_layer_gpu(layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    if(a.adam){
+        adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t);
+        adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t);
+        if(l.scales_gpu){
+            adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t);
+        }
+    }else{
+        axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+        axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+        scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1);
+
+        axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+        scal_gpu(l.n, momentum, l.bias_updates_gpu, 1);
+
+        if(l.scales_gpu){
+            axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
+            scal_gpu(l.n, momentum, l.scale_updates_gpu, 1);
+        }
+    }
+    if(l.clip){
+        constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1);
+    }
+}
+
+
diff --git a/hanzi_detection/src/convolutional_layer.c b/hanzi_detection/src/convolutional_layer.c
new file mode 100755
index 0000000..1fb58b0
--- /dev/null
+++ b/hanzi_detection/src/convolutional_layer.c
@@ -0,0 +1,622 @@
+#include "convolutional_layer.h"
+#include "utils.h"
+#include "batchnorm_layer.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+#include <stdio.h>
+#include <time.h>
+
+#ifdef AI2
+#include "xnor_layer.h"
+#endif
+
+void swap_binary(convolutional_layer *l)
+{
+    float *swap = l->weights;
+    l->weights = l->binary_weights;
+    l->binary_weights = swap;
+
+#ifdef GPU
+    swap = l->weights_gpu;
+    l->weights_gpu = l->binary_weights_gpu;
+    l->binary_weights_gpu = swap;
+#endif
+}
+
+void binarize_weights(float *weights, int n, int size, float *binary)
+{
+    int i, f;
+    for(f = 0; f < n; ++f){
+        float mean = 0;
+        for(i = 0; i < size; ++i){
+            mean += fabs(weights[f*size + i]);
+        }
+        mean = mean / size;
+        for(i = 0; i < size; ++i){
+            binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
+        }
+    }
+}
+
+void binarize_cpu(float *input, int n, float *binary)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        binary[i] = (input[i] > 0) ? 1 : -1;
+    }
+}
+
+void binarize_input(float *input, int n, int size, float *binary)
+{
+    int i, s;
+    for(s = 0; s < size; ++s){
+        float mean = 0;
+        for(i = 0; i < n; ++i){
+            mean += fabs(input[i*size + s]);
+        }
+        mean = mean / n;
+        for(i = 0; i < n; ++i){
+            binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
+        }
+    }
+}
+
+int convolutional_out_height(convolutional_layer l)
+{
+    return (l.h + 2*l.pad - l.size) / l.stride + 1;
+}
+
+int convolutional_out_width(convolutional_layer l)
+{
+    return (l.w + 2*l.pad - l.size) / l.stride + 1;
+}
+
+image get_convolutional_image(convolutional_layer l)
+{
+    return float_to_image(l.out_w,l.out_h,l.out_c,l.output);
+}
+
+image get_convolutional_delta(convolutional_layer l)
+{
+    return float_to_image(l.out_w,l.out_h,l.out_c,l.delta);
+}
+
+static size_t get_workspace_size(layer l){
+#ifdef CUDNN
+    if(gpu_index >= 0){
+        size_t most = 0;
+        size_t s = 0;
+        cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
+                l.srcTensorDesc,
+                l.weightDesc,
+                l.convDesc,
+                l.dstTensorDesc,
+                l.fw_algo,
+                &s);
+        if (s > most) most = s;
+        cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
+                l.srcTensorDesc,
+                l.ddstTensorDesc,
+                l.convDesc,
+                l.dweightDesc,
+                l.bf_algo,
+                &s);
+        if (s > most) most = s;
+        cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
+                l.weightDesc,
+                l.ddstTensorDesc,
+                l.convDesc,
+                l.dsrcTensorDesc,
+                l.bd_algo,
+                &s);
+        if (s > most) most = s;
+        return most;
+    }
+#endif
+    return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float);
+}
+
+#ifdef GPU
+#ifdef CUDNN
+void cudnn_convolutional_setup(layer *l)
+{
+    cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
+    cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
+
+    cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); 
+    cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
+    cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); 
+
+    cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); 
+    cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); 
+    #if CUDNN_MAJOR >= 6
+    cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);
+    #else
+    cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
+    #endif
+
+    #if CUDNN_MAJOR >= 7
+    cudnnSetConvolutionGroupCount(l->convDesc, l->groups);
+    #else
+    if(l->groups > 1){
+        error("CUDNN < 7 doesn't support groups, please upgrade!");
+    }
+    #endif
+
+    cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
+            l->srcTensorDesc,
+            l->weightDesc,
+            l->convDesc,
+            l->dstTensorDesc,
+            CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+            2000000000,
+            &l->fw_algo);
+    cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
+            l->weightDesc,
+            l->ddstTensorDesc,
+            l->convDesc,
+            l->dsrcTensorDesc,
+            CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+            2000000000,
+            &l->bd_algo);
+    cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
+            l->srcTensorDesc,
+            l->ddstTensorDesc,
+            l->convDesc,
+            l->dweightDesc,
+            CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+            2000000000,
+            &l->bf_algo);
+}
+#endif
+#endif
+
+convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam)
+{
+    int i;
+    convolutional_layer l = {0};
+    l.type = CONVOLUTIONAL;
+
+    l.groups = groups;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.n = n;
+    l.binary = binary;
+    l.xnor = xnor;
+    l.batch = batch;
+    l.stride = stride;
+    l.size = size;
+    l.pad = padding;
+    l.batch_normalize = batch_normalize;
+
+    l.weights = calloc(c/groups*n*size*size, sizeof(float));
+    l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));
+
+    l.biases = calloc(n, sizeof(float));
+    l.bias_updates = calloc(n, sizeof(float));
+
+    l.nweights = c/groups*n*size*size;
+    l.nbiases = n;
+
+    // float scale = 1./sqrt(size*size*c);
+    float scale = sqrt(2./(size*size*c/l.groups));
+    //printf("convscale %f\n", scale);
+    //scale = .02;
+    //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
+    for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
+    int out_w = convolutional_out_width(l);
+    int out_h = convolutional_out_height(l);
+    l.out_h = out_h;
+    l.out_w = out_w;
+    l.out_c = n;
+    l.outputs = l.out_h * l.out_w * l.out_c;
+    l.inputs = l.w * l.h * l.c;
+
+    l.output = calloc(l.batch*l.outputs, sizeof(float));
+    l.delta  = calloc(l.batch*l.outputs, sizeof(float));
+
+    l.forward = forward_convolutional_layer;
+    l.backward = backward_convolutional_layer;
+    l.update = update_convolutional_layer;
+    if(binary){
+        l.binary_weights = calloc(l.nweights, sizeof(float));
+        l.cweights = calloc(l.nweights, sizeof(char));
+        l.scales = calloc(n, sizeof(float));
+    }
+    if(xnor){
+        l.binary_weights = calloc(l.nweights, sizeof(float));
+        l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
+    }
+
+    if(batch_normalize){
+        l.scales = calloc(n, sizeof(float));
+        l.scale_updates = calloc(n, sizeof(float));
+        for(i = 0; i < n; ++i){
+            l.scales[i] = 1;
+        }
+
+        l.mean = calloc(n, sizeof(float));
+        l.variance = calloc(n, sizeof(float));
+
+        l.mean_delta = calloc(n, sizeof(float));
+        l.variance_delta = calloc(n, sizeof(float));
+
+        l.rolling_mean = calloc(n, sizeof(float));
+        l.rolling_variance = calloc(n, sizeof(float));
+        l.x = calloc(l.batch*l.outputs, sizeof(float));
+        l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
+    }
+    if(adam){
+        l.m = calloc(l.nweights, sizeof(float));
+        l.v = calloc(l.nweights, sizeof(float));
+        l.bias_m = calloc(n, sizeof(float));
+        l.scale_m = calloc(n, sizeof(float));
+        l.bias_v = calloc(n, sizeof(float));
+        l.scale_v = calloc(n, sizeof(float));
+    }
+
+#ifdef GPU
+    l.forward_gpu = forward_convolutional_layer_gpu;
+    l.backward_gpu = backward_convolutional_layer_gpu;
+    l.update_gpu = update_convolutional_layer_gpu;
+
+    if(gpu_index >= 0){
+        if (adam) {
+            l.m_gpu = cuda_make_array(l.m, l.nweights);
+            l.v_gpu = cuda_make_array(l.v, l.nweights);
+            l.bias_m_gpu = cuda_make_array(l.bias_m, n);
+            l.bias_v_gpu = cuda_make_array(l.bias_v, n);
+            l.scale_m_gpu = cuda_make_array(l.scale_m, n);
+            l.scale_v_gpu = cuda_make_array(l.scale_v, n);
+        }
+
+        l.weights_gpu = cuda_make_array(l.weights, l.nweights);
+        l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights);
+
+        l.biases_gpu = cuda_make_array(l.biases, n);
+        l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
+
+        l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
+        l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
+
+        if(binary){
+            l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
+        }
+        if(xnor){
+            l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
+            l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
+        }
+
+        if(batch_normalize){
+            l.mean_gpu = cuda_make_array(l.mean, n);
+            l.variance_gpu = cuda_make_array(l.variance, n);
+
+            l.rolling_mean_gpu = cuda_make_array(l.mean, n);
+            l.rolling_variance_gpu = cuda_make_array(l.variance, n);
+
+            l.mean_delta_gpu = cuda_make_array(l.mean, n);
+            l.variance_delta_gpu = cuda_make_array(l.variance, n);
+
+            l.scales_gpu = cuda_make_array(l.scales, n);
+            l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
+
+            l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
+            l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
+        }
+#ifdef CUDNN
+        cudnnCreateTensorDescriptor(&l.normTensorDesc);
+        cudnnCreateTensorDescriptor(&l.srcTensorDesc);
+        cudnnCreateTensorDescriptor(&l.dstTensorDesc);
+        cudnnCreateFilterDescriptor(&l.weightDesc);
+        cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
+        cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
+        cudnnCreateFilterDescriptor(&l.dweightDesc);
+        cudnnCreateConvolutionDescriptor(&l.convDesc);
+        cudnn_convolutional_setup(&l);
+#endif
+    }
+#endif
+    l.workspace_size = get_workspace_size(l);
+    l.activation = activation;
+
+    fprintf(stderr, "conv  %5d %2d x%2d /%2d  %4d x%4d x%4d   ->  %4d x%4d x%4d  %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.);
+
+    return l;
+}
+
+void denormalize_convolutional_layer(convolutional_layer l)
+{
+    int i, j;
+    for(i = 0; i < l.n; ++i){
+        float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
+        for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){
+            l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale;
+        }
+        l.biases[i] -= l.rolling_mean[i] * scale;
+        l.scales[i] = 1;
+        l.rolling_mean[i] = 0;
+        l.rolling_variance[i] = 1;
+    }
+}
+
+/*
+void test_convolutional_layer()
+{
+    convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0);
+    l.batch_normalize = 1;
+    float data[] = {1,1,1,1,1,
+        1,1,1,1,1,
+        1,1,1,1,1,
+        1,1,1,1,1,
+        1,1,1,1,1,
+        2,2,2,2,2,
+        2,2,2,2,2,
+        2,2,2,2,2,
+        2,2,2,2,2,
+        2,2,2,2,2,
+        3,3,3,3,3,
+        3,3,3,3,3,
+        3,3,3,3,3,
+        3,3,3,3,3,
+        3,3,3,3,3};
+    //net.input = data;
+    //forward_convolutional_layer(l);
+}
+*/
+
+void resize_convolutional_layer(convolutional_layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+    int out_w = convolutional_out_width(*l);
+    int out_h = convolutional_out_height(*l);
+
+    l->out_w = out_w;
+    l->out_h = out_h;
+
+    l->outputs = l->out_h * l->out_w * l->out_c;
+    l->inputs = l->w * l->h * l->c;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta  = realloc(l->delta,  l->batch*l->outputs*sizeof(float));
+    if(l->batch_normalize){
+        l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
+        l->x_norm  = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
+    }
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =  cuda_make_array(l->delta,  l->batch*l->outputs);
+    l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+
+    if(l->batch_normalize){
+        cuda_free(l->x_gpu);
+        cuda_free(l->x_norm_gpu);
+
+        l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+        l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+    }
+#ifdef CUDNN
+    cudnn_convolutional_setup(l);
+#endif
+#endif
+    l->workspace_size = get_workspace_size(*l);
+}
+
+void add_bias(float *output, float *biases, int batch, int n, int size)
+{
+    int i,j,b;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < n; ++i){
+            for(j = 0; j < size; ++j){
+                output[(b*n + i)*size + j] += biases[i];
+            }
+        }
+    }
+}
+
+void scale_bias(float *output, float *scales, int batch, int n, int size)
+{
+    int i,j,b;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < n; ++i){
+            for(j = 0; j < size; ++j){
+                output[(b*n + i)*size + j] *= scales[i];
+            }
+        }
+    }
+}
+
+void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
+{
+    int i,b;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < n; ++i){
+            bias_updates[i] += sum_array(delta+size*(i+b*n), size);
+        }
+    }
+}
+
+void forward_convolutional_layer(convolutional_layer l, network net)
+{
+    int i, j;
+
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+
+    if(l.xnor){
+        binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights);
+        swap_binary(&l);
+        binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input);
+        net.input = l.binary_input;
+    }
+
+    int m = l.n/l.groups;
+    int k = l.size*l.size*l.c/l.groups;
+    int n = l.out_w*l.out_h;
+    for(i = 0; i < l.batch; ++i){
+        for(j = 0; j < l.groups; ++j){
+            float *a = l.weights + j*l.nweights/l.groups;
+            float *b = net.workspace;
+            float *c = l.output + (i*l.groups + j)*n*m;
+            float *im =  net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
+
+            if (l.size == 1) {
+                b = im;
+            } else {
+                im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
+            }
+            gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+        }
+    }
+
+    if(l.batch_normalize){
+        forward_batchnorm_layer(l, net);
+    } else {
+        add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w);
+    }
+
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+    if(l.binary || l.xnor) swap_binary(&l);
+}
+
+void backward_convolutional_layer(convolutional_layer l, network net)
+{
+    int i, j;
+    int m = l.n/l.groups;
+    int n = l.size*l.size*l.c/l.groups;
+    int k = l.out_w*l.out_h;
+
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+
+    if(l.batch_normalize){
+        backward_batchnorm_layer(l, net);
+    } else {
+        backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
+    }
+
+    for(i = 0; i < l.batch; ++i){
+        for(j = 0; j < l.groups; ++j){
+            float *a = l.delta + (i*l.groups + j)*m*k;
+            float *b = net.workspace;
+            float *c = l.weight_updates + j*l.nweights/l.groups;
+
+            float *im  = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
+            float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
+
+            if(l.size == 1){
+                b = im;
+            } else {
+                im2col_cpu(im, l.c/l.groups, l.h, l.w, 
+                        l.size, l.stride, l.pad, b);
+            }
+
+            gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+            if (net.delta) {
+                a = l.weights + j*l.nweights/l.groups;
+                b = l.delta + (i*l.groups + j)*m*k;
+                c = net.workspace;
+                if (l.size == 1) {
+                    c = imd;
+                }
+
+                gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
+
+                if (l.size != 1) {
+                    col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
+                }
+            }
+        }
+    }
+}
+
+void update_convolutional_layer(convolutional_layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+    scal_cpu(l.n, momentum, l.bias_updates, 1);
+
+    if(l.scales){
+        axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
+        scal_cpu(l.n, momentum, l.scale_updates, 1);
+    }
+
+    axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1);
+    axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+    scal_cpu(l.nweights, momentum, l.weight_updates, 1);
+}
+
+
+image get_convolutional_weight(convolutional_layer l, int i)
+{
+    int h = l.size;
+    int w = l.size;
+    int c = l.c/l.groups;
+    return float_to_image(w,h,c,l.weights+i*h*w*c);
+}
+
+void rgbgr_weights(convolutional_layer l)
+{
+    int i;
+    for(i = 0; i < l.n; ++i){
+        image im = get_convolutional_weight(l, i);
+        if (im.c == 3) {
+            rgbgr_image(im);
+        }
+    }
+}
+
+void rescale_weights(convolutional_layer l, float scale, float trans)
+{
+    int i;
+    for(i = 0; i < l.n; ++i){
+        image im = get_convolutional_weight(l, i);
+        if (im.c == 3) {
+            scale_image(im, scale);
+            float sum = sum_array(im.data, im.w*im.h*im.c);
+            l.biases[i] += sum*trans;
+        }
+    }
+}
+
+image *get_weights(convolutional_layer l)
+{
+    image *weights = calloc(l.n, sizeof(image));
+    int i;
+    for(i = 0; i < l.n; ++i){
+        weights[i] = copy_image(get_convolutional_weight(l, i));
+        normalize_image(weights[i]);
+        /*
+           char buff[256];
+           sprintf(buff, "filter%d", i);
+           save_image(weights[i], buff);
+         */
+    }
+    //error("hey");
+    return weights;
+}
+
+image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_weights)
+{
+    image *single_weights = get_weights(l);
+    show_images(single_weights, l.n, window);
+
+    image delta = get_convolutional_image(l);
+    image dc = collapse_image_layers(delta, 1);
+    char buff[256];
+    sprintf(buff, "%s: Output", window);
+    //show_image(dc, buff);
+    //save_image(dc, buff);
+    free_image(dc);
+    return single_weights;
+}
+
diff --git a/hanzi_detection/src/convolutional_layer.h b/hanzi_detection/src/convolutional_layer.h
new file mode 100755
index 0000000..6c261f5
--- /dev/null
+++ b/hanzi_detection/src/convolutional_layer.h
@@ -0,0 +1,50 @@
+#ifndef CONVOLUTIONAL_LAYER_H
+#define CONVOLUTIONAL_LAYER_H
+
+#include "cuda.h"
+#include "image.h"
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+typedef layer convolutional_layer;
+
+#ifdef GPU
+void forward_convolutional_layer_gpu(convolutional_layer layer, network net);
+void backward_convolutional_layer_gpu(convolutional_layer layer, network net);
+void update_convolutional_layer_gpu(convolutional_layer layer, update_args a);
+
+void push_convolutional_layer(convolutional_layer layer);
+void pull_convolutional_layer(convolutional_layer layer);
+
+void add_bias_gpu(float *output, float *biases, int batch, int n, int size);
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size);
+void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t);
+#ifdef CUDNN
+void cudnn_convolutional_setup(layer *l);
+#endif
+#endif
+
+convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam);
+void resize_convolutional_layer(convolutional_layer *layer, int w, int h);
+void forward_convolutional_layer(const convolutional_layer layer, network net);
+void update_convolutional_layer(convolutional_layer layer, update_args a);
+image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_weights);
+void binarize_weights(float *weights, int n, int size, float *binary);
+void swap_binary(convolutional_layer *l);
+void binarize_weights2(float *weights, int n, int size, char *binary, float *scales);
+
+void backward_convolutional_layer(convolutional_layer layer, network net);
+
+void add_bias(float *output, float *biases, int batch, int n, int size);
+void backward_bias(float *bias_updates, float *delta, int batch, int n, int size);
+
+image get_convolutional_image(convolutional_layer layer);
+image get_convolutional_delta(convolutional_layer layer);
+image get_convolutional_weight(convolutional_layer layer, int i);
+
+int convolutional_out_height(convolutional_layer layer);
+int convolutional_out_width(convolutional_layer layer);
+
+#endif
+
diff --git a/hanzi_detection/src/cost_layer.c b/hanzi_detection/src/cost_layer.c
new file mode 100755
index 0000000..2138ff2
--- /dev/null
+++ b/hanzi_detection/src/cost_layer.c
@@ -0,0 +1,176 @@
+#include "cost_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include <math.h>
+#include <string.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+COST_TYPE get_cost_type(char *s)
+{
+    if (strcmp(s, "seg")==0) return SEG;
+    if (strcmp(s, "sse")==0) return SSE;
+    if (strcmp(s, "masked")==0) return MASKED;
+    if (strcmp(s, "smooth")==0) return SMOOTH;
+    if (strcmp(s, "L1")==0) return L1;
+    if (strcmp(s, "wgan")==0) return WGAN;
+    fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s);
+    return SSE;
+}
+
+char *get_cost_string(COST_TYPE a)
+{
+    switch(a){
+        case SEG:
+            return "seg";
+        case SSE:
+            return "sse";
+        case MASKED:
+            return "masked";
+        case SMOOTH:
+            return "smooth";
+        case L1:
+            return "L1";
+        case WGAN:
+            return "wgan";
+    }
+    return "sse";
+}
+
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
+{
+    fprintf(stderr, "cost                                           %4d\n",  inputs);
+    cost_layer l = {0};
+    l.type = COST;
+
+    l.scale = scale;
+    l.batch = batch;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.cost_type = cost_type;
+    l.delta = calloc(inputs*batch, sizeof(float));
+    l.output = calloc(inputs*batch, sizeof(float));
+    l.cost = calloc(1, sizeof(float));
+
+    l.forward = forward_cost_layer;
+    l.backward = backward_cost_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_cost_layer_gpu;
+    l.backward_gpu = backward_cost_layer_gpu;
+
+    l.delta_gpu = cuda_make_array(l.output, inputs*batch);
+    l.output_gpu = cuda_make_array(l.delta, inputs*batch);
+    #endif
+    return l;
+}
+
+void resize_cost_layer(cost_layer *l, int inputs)
+{
+    l->inputs = inputs;
+    l->outputs = inputs;
+    l->delta = realloc(l->delta, inputs*l->batch*sizeof(float));
+    l->output = realloc(l->output, inputs*l->batch*sizeof(float));
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+    l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
+    l->output_gpu = cuda_make_array(l->output, inputs*l->batch);
+#endif
+}
+
+void forward_cost_layer(cost_layer l, network net)
+{
+    if (!net.truth) return;
+    if(l.cost_type == MASKED){
+        int i;
+        for(i = 0; i < l.batch*l.inputs; ++i){
+            if(net.truth[i] == SECRET_NUM) net.input[i] = SECRET_NUM;
+        }
+    }
+    if(l.cost_type == SMOOTH){
+        smooth_l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
+    }else if(l.cost_type == L1){
+        l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
+    } else {
+        l2_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
+    }
+    l.cost[0] = sum_array(l.output, l.batch*l.inputs);
+}
+
+void backward_cost_layer(const cost_layer l, network net)
+{
+    axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void pull_cost_layer(cost_layer l)
+{
+    cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+void push_cost_layer(cost_layer l)
+{
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+int float_abs_compare (const void * a, const void * b)
+{
+    float fa = *(const float*) a;
+    if(fa < 0) fa = -fa;
+    float fb = *(const float*) b;
+    if(fb < 0) fb = -fb;
+    return (fa > fb) - (fa < fb);
+}
+
+void forward_cost_layer_gpu(cost_layer l, network net)
+{
+    if (!net.truth) return;
+    if(l.smooth){
+        scal_gpu(l.batch*l.inputs, (1-l.smooth), net.truth_gpu, 1);
+        add_gpu(l.batch*l.inputs, l.smooth * 1./l.inputs, net.truth_gpu, 1);
+    }
+
+    if(l.cost_type == SMOOTH){
+        smooth_l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
+    } else if (l.cost_type == L1){
+        l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
+    } else if (l.cost_type == WGAN){
+        wgan_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
+    } else {
+        l2_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
+    }
+
+    if (l.cost_type == SEG && l.noobject_scale != 1) {
+        scale_mask_gpu(l.batch*l.inputs, l.delta_gpu, 0, net.truth_gpu, l.noobject_scale);
+        scale_mask_gpu(l.batch*l.inputs, l.output_gpu, 0, net.truth_gpu, l.noobject_scale);
+    }
+    if (l.cost_type == MASKED) {
+        mask_gpu(l.batch*l.inputs, net.delta_gpu, SECRET_NUM, net.truth_gpu, 0);
+    }
+
+    if(l.ratio){
+        cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+        qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare);
+        int n = (1-l.ratio) * l.batch*l.inputs;
+        float thresh = l.delta[n];
+        thresh = 0;
+        printf("%f\n", thresh);
+        supp_gpu(l.batch*l.inputs, thresh, l.delta_gpu, 1);
+    }
+
+    if(l.thresh){
+        supp_gpu(l.batch*l.inputs, l.thresh*1./l.inputs, l.delta_gpu, 1);
+    }
+
+    cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs);
+    l.cost[0] = sum_array(l.output, l.batch*l.inputs);
+}
+
+void backward_cost_layer_gpu(const cost_layer l, network net)
+{
+    axpy_gpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+
diff --git a/hanzi_detection/src/cost_layer.h b/hanzi_detection/src/cost_layer.h
new file mode 100755
index 0000000..ceb64de
--- /dev/null
+++ b/hanzi_detection/src/cost_layer.h
@@ -0,0 +1,20 @@
+#ifndef COST_LAYER_H
+#define COST_LAYER_H
+#include "layer.h"
+#include "network.h"
+
+typedef layer cost_layer;
+
+COST_TYPE get_cost_type(char *s);
+char *get_cost_string(COST_TYPE a);
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale);
+void forward_cost_layer(const cost_layer l, network net);
+void backward_cost_layer(const cost_layer l, network net);
+void resize_cost_layer(cost_layer *l, int inputs);
+
+#ifdef GPU
+void forward_cost_layer_gpu(cost_layer l, network net);
+void backward_cost_layer_gpu(const cost_layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/crnn_layer.c b/hanzi_detection/src/crnn_layer.c
new file mode 100755
index 0000000..7dd29f6
--- /dev/null
+++ b/hanzi_detection/src/crnn_layer.c
@@ -0,0 +1,283 @@
+#include "crnn_layer.h"
+#include "convolutional_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+static void increment_layer(layer *l, int steps)
+{
+    int num = l->outputs*l->batch*steps;
+    l->output += num;
+    l->delta += num;
+    l->x += num;
+    l->x_norm += num;
+
+#ifdef GPU
+    l->output_gpu += num;
+    l->delta_gpu += num;
+    l->x_gpu += num;
+    l->x_norm_gpu += num;
+#endif
+}
+
+layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize)
+{
+    fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
+    batch = batch / steps;
+    layer l = {0};
+    l.batch = batch;
+    l.type = CRNN;
+    l.steps = steps;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.out_h = h;
+    l.out_w = w;
+    l.out_c = output_filters;
+    l.inputs = h*w*c;
+    l.hidden = h * w * hidden_filters;
+    l.outputs = l.out_h * l.out_w * l.out_c;
+
+    l.state = calloc(l.hidden*batch*(steps+1), sizeof(float));
+
+    l.input_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 1, 3, 1, 1,  activation, batch_normalize, 0, 0, 0);
+    l.input_layer->batch = batch;
+
+    l.self_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 1, 3, 1, 1,  activation, batch_normalize, 0, 0, 0);
+    l.self_layer->batch = batch;
+
+    l.output_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 1, 3, 1, 1,  activation, batch_normalize, 0, 0, 0);
+    l.output_layer->batch = batch;
+
+    l.output = l.output_layer->output;
+    l.delta = l.output_layer->delta;
+
+    l.forward = forward_crnn_layer;
+    l.backward = backward_crnn_layer;
+    l.update = update_crnn_layer;
+
+#ifdef GPU
+    l.forward_gpu = forward_crnn_layer_gpu;
+    l.backward_gpu = backward_crnn_layer_gpu;
+    l.update_gpu = update_crnn_layer_gpu;
+
+    l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1));
+    l.output_gpu = l.output_layer->output_gpu;
+    l.delta_gpu = l.output_layer->delta_gpu;
+#endif
+
+    return l;
+}
+
+void update_crnn_layer(layer l, update_args a)
+{
+    update_convolutional_layer(*(l.input_layer),  a);
+    update_convolutional_layer(*(l.self_layer),   a);
+    update_convolutional_layer(*(l.output_layer), a);
+}
+
+void forward_crnn_layer(layer l, network net)
+{
+    network s = net;
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
+    fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1);
+    fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1);
+    if(net.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input = net.input;
+        forward_convolutional_layer(input_layer, s);
+
+        s.input = l.state;
+        forward_convolutional_layer(self_layer, s);
+
+        float *old_state = l.state;
+        if(net.train) l.state += l.hidden*l.batch;
+        if(l.shortcut){
+            copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
+        }else{
+            fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+        }
+        axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
+        axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+        s.input = l.state;
+        forward_convolutional_layer(output_layer, s);
+
+        net.input += l.inputs*l.batch;
+        increment_layer(&input_layer, 1);
+        increment_layer(&self_layer, 1);
+        increment_layer(&output_layer, 1);
+    }
+}
+
+void backward_crnn_layer(layer l, network net)
+{
+    network s = net;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    increment_layer(&input_layer, l.steps-1);
+    increment_layer(&self_layer, l.steps-1);
+    increment_layer(&output_layer, l.steps-1);
+
+    l.state += l.hidden*l.batch*l.steps;
+    for (i = l.steps-1; i >= 0; --i) {
+        copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
+        axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+        s.input = l.state;
+        s.delta = self_layer.delta;
+        backward_convolutional_layer(output_layer, s);
+
+        l.state -= l.hidden*l.batch;
+        /*
+           if(i > 0){
+           copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
+           axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
+           }else{
+           fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+           }
+         */
+
+        s.input = l.state;
+        s.delta = self_layer.delta - l.hidden*l.batch;
+        if (i == 0) s.delta = 0;
+        backward_convolutional_layer(self_layer, s);
+
+        copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
+        if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
+        s.input = net.input + i*l.inputs*l.batch;
+        if(net.delta) s.delta = net.delta + i*l.inputs*l.batch;
+        else s.delta = 0;
+        backward_convolutional_layer(input_layer, s);
+
+        increment_layer(&input_layer, -1);
+        increment_layer(&self_layer, -1);
+        increment_layer(&output_layer, -1);
+    }
+}
+
+#ifdef GPU
+
+void pull_crnn_layer(layer l)
+{
+    pull_convolutional_layer(*(l.input_layer));
+    pull_convolutional_layer(*(l.self_layer));
+    pull_convolutional_layer(*(l.output_layer));
+}
+
+void push_crnn_layer(layer l)
+{
+    push_convolutional_layer(*(l.input_layer));
+    push_convolutional_layer(*(l.self_layer));
+    push_convolutional_layer(*(l.output_layer));
+}
+
+void update_crnn_layer_gpu(layer l, update_args a)
+{
+    update_convolutional_layer_gpu(*(l.input_layer),  a);
+    update_convolutional_layer_gpu(*(l.self_layer),   a);
+    update_convolutional_layer_gpu(*(l.output_layer), a);
+}
+
+void forward_crnn_layer_gpu(layer l, network net)
+{
+    network s = net;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
+    fill_gpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
+    fill_gpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
+    if(net.train) fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1);
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input_gpu = net.input_gpu;
+        forward_convolutional_layer_gpu(input_layer, s);
+
+        s.input_gpu = l.state_gpu;
+        forward_convolutional_layer_gpu(self_layer, s);
+
+        float *old_state = l.state_gpu;
+        if(net.train) l.state_gpu += l.hidden*l.batch;
+        if(l.shortcut){
+            copy_gpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
+        }else{
+            fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1);
+        }
+        axpy_gpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
+        axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        forward_convolutional_layer_gpu(output_layer, s);
+
+        net.input_gpu += l.inputs*l.batch;
+        increment_layer(&input_layer, 1);
+        increment_layer(&self_layer, 1);
+        increment_layer(&output_layer, 1);
+    }
+}
+
+void backward_crnn_layer_gpu(layer l, network net)
+{
+    network s = net;
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+    increment_layer(&input_layer,  l.steps - 1);
+    increment_layer(&self_layer,   l.steps - 1);
+    increment_layer(&output_layer, l.steps - 1);
+    l.state_gpu += l.hidden*l.batch*l.steps;
+    for (i = l.steps-1; i >= 0; --i) {
+        copy_gpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
+        axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        s.delta_gpu = self_layer.delta_gpu;
+        backward_convolutional_layer_gpu(output_layer, s);
+
+        l.state_gpu -= l.hidden*l.batch;
+
+        s.input_gpu = l.state_gpu;
+        s.delta_gpu = self_layer.delta_gpu - l.hidden*l.batch;
+        if (i == 0) s.delta_gpu = 0;
+        backward_convolutional_layer_gpu(self_layer, s);
+
+        copy_gpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
+        if (i > 0 && l.shortcut) axpy_gpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
+        s.input_gpu = net.input_gpu + i*l.inputs*l.batch;
+        if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch;
+        else s.delta_gpu = 0;
+        backward_convolutional_layer_gpu(input_layer, s);
+
+        increment_layer(&input_layer,  -1);
+        increment_layer(&self_layer,   -1);
+        increment_layer(&output_layer, -1);
+    }
+}
+#endif
diff --git a/hanzi_detection/src/crnn_layer.h b/hanzi_detection/src/crnn_layer.h
new file mode 100755
index 0000000..515f378
--- /dev/null
+++ b/hanzi_detection/src/crnn_layer.h
@@ -0,0 +1,24 @@
+
+#ifndef CRNN_LAYER_H
+#define CRNN_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize);
+
+void forward_crnn_layer(layer l, network net);
+void backward_crnn_layer(layer l, network net);
+void update_crnn_layer(layer l, update_args a);
+
+#ifdef GPU
+void forward_crnn_layer_gpu(layer l, network net);
+void backward_crnn_layer_gpu(layer l, network net);
+void update_crnn_layer_gpu(layer l, update_args a);
+void push_crnn_layer(layer l);
+void pull_crnn_layer(layer l);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/crop_layer.c b/hanzi_detection/src/crop_layer.c
new file mode 100755
index 0000000..3b91852
--- /dev/null
+++ b/hanzi_detection/src/crop_layer.c
@@ -0,0 +1,103 @@
+#include "crop_layer.h"
+#include "cuda.h"
+#include <stdio.h>
+
+image get_crop_image(crop_layer l)
+{
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.out_c;
+    return float_to_image(w,h,c,l.output);
+}
+
+void backward_crop_layer(const crop_layer l, network net){}
+void backward_crop_layer_gpu(const crop_layer l, network net){}
+
+crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure)
+{
+    fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
+    crop_layer l = {0};
+    l.type = CROP;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.scale = (float)crop_height / h;
+    l.flip = flip;
+    l.angle = angle;
+    l.saturation = saturation;
+    l.exposure = exposure;
+    l.out_w = crop_width;
+    l.out_h = crop_height;
+    l.out_c = c;
+    l.inputs = l.w * l.h * l.c;
+    l.outputs = l.out_w * l.out_h * l.out_c;
+    l.output = calloc(l.outputs*batch, sizeof(float));
+    l.forward = forward_crop_layer;
+    l.backward = backward_crop_layer;
+
+    #ifdef GPU
+    l.forward_gpu = forward_crop_layer_gpu;
+    l.backward_gpu = backward_crop_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
+    l.rand_gpu   = cuda_make_array(0, l.batch*8);
+    #endif
+    return l;
+}
+
+void resize_crop_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->out_w =  l->scale*w;
+    l->out_h =  l->scale*h;
+
+    l->inputs = l->w * l->h * l->c;
+    l->outputs = l->out_h * l->out_w * l->out_c;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    #ifdef GPU
+    cuda_free(l->output_gpu);
+    l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
+    #endif
+}
+
+
+void forward_crop_layer(const crop_layer l, network net)
+{
+    int i,j,c,b,row,col;
+    int index;
+    int count = 0;
+    int flip = (l.flip && rand()%2);
+    int dh = rand()%(l.h - l.out_h + 1);
+    int dw = rand()%(l.w - l.out_w + 1);
+    float scale = 2;
+    float trans = -1;
+    if(l.noadjust){
+        scale = 1;
+        trans = 0;
+    }
+    if(!net.train){
+        flip = 0;
+        dh = (l.h - l.out_h)/2;
+        dw = (l.w - l.out_w)/2;
+    }
+    for(b = 0; b < l.batch; ++b){
+        for(c = 0; c < l.c; ++c){
+            for(i = 0; i < l.out_h; ++i){
+                for(j = 0; j < l.out_w; ++j){
+                    if(flip){
+                        col = l.w - dw - j - 1;    
+                    }else{
+                        col = j + dw;
+                    }
+                    row = i + dh;
+                    index = col+l.w*(row+l.h*(c + l.c*b)); 
+                    l.output[count++] = net.input[index]*scale + trans;
+                }
+            }
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/crop_layer.h b/hanzi_detection/src/crop_layer.h
new file mode 100755
index 0000000..3b5883c
--- /dev/null
+++ b/hanzi_detection/src/crop_layer.h
@@ -0,0 +1,20 @@
+#ifndef CROP_LAYER_H
+#define CROP_LAYER_H
+
+#include "image.h"
+#include "layer.h"
+#include "network.h"
+
+typedef layer crop_layer;
+
+image get_crop_image(crop_layer l);
+crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure);
+void forward_crop_layer(const crop_layer l, network net);
+void resize_crop_layer(layer *l, int w, int h);
+
+#ifdef GPU
+void forward_crop_layer_gpu(crop_layer l, network net);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/crop_layer_kernels.cu b/hanzi_detection/src/crop_layer_kernels.cu
new file mode 100755
index 0000000..b5b9f55
--- /dev/null
+++ b/hanzi_detection/src/crop_layer_kernels.cu
@@ -0,0 +1,225 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "crop_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "image.h"
+}
+
+__device__ float get_pixel_kernel(float *image, int w, int h, int x, int y, int c)
+{
+    if(x < 0 || x >= w || y < 0 || y >= h) return 0;
+    return image[x + w*(y + c*h)];
+}
+
+__device__ float3 rgb_to_hsv_kernel(float3 rgb)
+{
+    float r = rgb.x;
+    float g = rgb.y; 
+    float b = rgb.z;
+
+    float h, s, v;
+    float max = (r > g) ? ( (r > b) ? r : b) : ( (g > b) ? g : b);
+    float min = (r < g) ? ( (r < b) ? r : b) : ( (g < b) ? g : b);
+    float delta = max - min;
+    v = max;
+    if(max == 0){
+        s = 0;
+        h = -1;
+    }else{
+        s = delta/max;
+        if(r == max){
+            h = (g - b) / delta;
+        } else if (g == max) {
+            h = 2 + (b - r) / delta;
+        } else {
+            h = 4 + (r - g) / delta;
+        }
+        if (h < 0) h += 6;
+    }
+    return make_float3(h, s, v);
+}
+
+__device__ float3 hsv_to_rgb_kernel(float3 hsv)
+{
+    float h = hsv.x;
+    float s = hsv.y; 
+    float v = hsv.z;
+
+    float r, g, b;
+    float f, p, q, t;
+
+    if (s == 0) {
+        r = g = b = v;
+    } else {
+        int index = (int) floorf(h);
+        f = h - index;
+        p = v*(1-s);
+        q = v*(1-s*f);
+        t = v*(1-s*(1-f));
+        if(index == 0){
+            r = v; g = t; b = p;
+        } else if(index == 1){
+            r = q; g = v; b = p;
+        } else if(index == 2){
+            r = p; g = v; b = t;
+        } else if(index == 3){
+            r = p; g = q; b = v;
+        } else if(index == 4){
+            r = t; g = p; b = v;
+        } else {
+            r = v; g = p; b = q;
+        }
+    }
+    r = (r < 0) ? 0 : ((r > 1) ? 1 : r);
+    g = (g < 0) ? 0 : ((g > 1) ? 1 : g);
+    b = (b < 0) ? 0 : ((b > 1) ? 1 : b);
+    return make_float3(r, g, b);
+}
+
+__device__ float bilinear_interpolate_kernel(float *image, int w, int h, float x, float y, int c)
+{
+    int ix = (int) floorf(x);
+    int iy = (int) floorf(y);
+
+    float dx = x - ix;
+    float dy = y - iy;
+
+    float val = (1-dy) * (1-dx) * get_pixel_kernel(image, w, h, ix, iy, c) + 
+        dy     * (1-dx) * get_pixel_kernel(image, w, h, ix, iy+1, c) + 
+        (1-dy) *   dx   * get_pixel_kernel(image, w, h, ix+1, iy, c) +
+        dy     *   dx   * get_pixel_kernel(image, w, h, ix+1, iy+1, c);
+    return val;
+}
+
+__global__ void levels_image_kernel(float *image, float *rand, int batch, int w, int h, int train, float saturation, float exposure, float translate, float scale, float shift)
+{
+    int size = batch * w * h;
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= size) return;
+    int x = id % w;
+    id /= w;
+    int y = id % h;
+    id /= h;
+    float rshift = rand[0];
+    float gshift = rand[1];
+    float bshift = rand[2];
+    float r0 = rand[8*id + 0];
+    float r1 = rand[8*id + 1];
+    float r2 = rand[8*id + 2];
+    float r3 = rand[8*id + 3];
+
+    saturation = r0*(saturation - 1) + 1;
+    saturation = (r1 > .5f) ? 1.f/saturation : saturation;
+    exposure = r2*(exposure - 1) + 1;
+    exposure = (r3 > .5f) ? 1.f/exposure : exposure;
+
+    size_t offset = id * h * w * 3;
+    image += offset;
+    float r = image[x + w*(y + h*0)];
+    float g = image[x + w*(y + h*1)];
+    float b = image[x + w*(y + h*2)];
+    float3 rgb = make_float3(r,g,b);
+    if(train){
+        float3 hsv = rgb_to_hsv_kernel(rgb);
+        hsv.y *= saturation;
+        hsv.z *= exposure;
+        rgb = hsv_to_rgb_kernel(hsv);
+    } else {
+        shift = 0;
+    }
+    image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5f)*shift;
+    image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5f)*shift;
+    image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5f)*shift;
+}
+
+__global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= size) return;
+
+    float cx = w/2.f;
+    float cy = h/2.f;
+
+    int count = id;
+    int j = id % crop_width;
+    id /= crop_width;
+    int i = id % crop_height;
+    id /= crop_height;
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    float r4 = rand[8*b + 4];
+    float r5 = rand[8*b + 5];
+    float r6 = rand[8*b + 6];
+    float r7 = rand[8*b + 7];
+
+    float dw = (w - crop_width)*r4;
+    float dh = (h - crop_height)*r5;
+    flip = (flip && (r6 > .5f));
+    angle = 2*angle*r7 - angle;
+    if(!train){
+        dw = (w - crop_width)/2.f;
+        dh = (h - crop_height)/2.f;
+        flip = 0;
+        angle = 0;
+    }
+
+    input += w*h*c*b;
+
+    float x = (flip) ? w - dw - j - 1 : j + dw;    
+    float y = i + dh;
+
+    float rx = cosf(angle)*(x-cx) - sinf(angle)*(y-cy) + cx;
+    float ry = sinf(angle)*(x-cx) + cosf(angle)*(y-cy) + cy;
+
+    output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k);
+}
+
+extern "C" void forward_crop_layer_gpu(crop_layer layer, network net)
+{
+    cuda_random(layer.rand_gpu, layer.batch*8);
+
+    float radians = layer.angle*3.14159265f/180.f;
+
+    float scale = 2;
+    float translate = -1;
+    if(layer.noadjust){
+        scale = 1;
+        translate = 0;
+    }
+
+    int size = layer.batch * layer.w * layer.h;
+
+    levels_image_kernel<<<cuda_gridsize(size), BLOCK>>>(net.input_gpu, layer.rand_gpu, layer.batch, layer.w, layer.h, net.train, layer.saturation, layer.exposure, translate, scale, layer.shift);
+    check_error(cudaPeekAtLastError());
+
+    size = layer.batch*layer.c*layer.out_w*layer.out_h;
+
+    forward_crop_layer_kernel<<<cuda_gridsize(size), BLOCK>>>(net.input_gpu, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, net.train, layer.flip, radians, layer.output_gpu);
+    check_error(cudaPeekAtLastError());
+
+/*
+       cuda_pull_array(layer.output_gpu, layer.output, size);
+       image im = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 0*(size/layer.batch));
+       image im2 = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 1*(size/layer.batch));
+       image im3 = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 2*(size/layer.batch));
+
+       translate_image(im, -translate);
+       scale_image(im, 1/scale);
+       translate_image(im2, -translate);
+       scale_image(im2, 1/scale);
+       translate_image(im3, -translate);
+       scale_image(im3, 1/scale);
+       
+       show_image(im, "cropped");
+       show_image(im2, "cropped2");
+       show_image(im3, "cropped3");
+       cvWaitKey(0);
+       */
+}
+
diff --git a/hanzi_detection/src/cuda.c b/hanzi_detection/src/cuda.c
new file mode 100755
index 0000000..48aba6e
--- /dev/null
+++ b/hanzi_detection/src/cuda.c
@@ -0,0 +1,178 @@
+int gpu_index = 0;
+
+#ifdef GPU
+
+#include "cuda.h"
+#include "utils.h"
+#include "blas.h"
+#include <assert.h>
+#include <stdlib.h>
+#include <time.h>
+
+void cuda_set_device(int n)
+{
+    gpu_index = n;
+    cudaError_t status = cudaSetDevice(n);
+    check_error(status);
+}
+
+int cuda_get_device()
+{
+    int n = 0;
+    cudaError_t status = cudaGetDevice(&n);
+    check_error(status);
+    return n;
+}
+
+void check_error(cudaError_t status)
+{
+    //cudaDeviceSynchronize();
+    cudaError_t status2 = cudaGetLastError();
+    if (status != cudaSuccess)
+    {   
+        const char *s = cudaGetErrorString(status);
+        char buffer[256];
+        printf("CUDA Error: %s\n", s);
+        assert(0);
+        snprintf(buffer, 256, "CUDA Error: %s", s);
+        error(buffer);
+    } 
+    if (status2 != cudaSuccess)
+    {   
+        const char *s = cudaGetErrorString(status);
+        char buffer[256];
+        printf("CUDA Error Prev: %s\n", s);
+        assert(0);
+        snprintf(buffer, 256, "CUDA Error Prev: %s", s);
+        error(buffer);
+    } 
+}
+
+dim3 cuda_gridsize(size_t n){
+    size_t k = (n-1) / BLOCK + 1;
+    size_t x = k;
+    size_t y = 1;
+    if(x > 65535){
+        x = ceil(sqrt(k));
+        y = (n-1)/(x*BLOCK) + 1;
+    }
+    dim3 d = {x, y, 1};
+    //printf("%ld %ld %ld %ld\n", n, x, y, x*y*BLOCK);
+    return d;
+}
+
+#ifdef CUDNN
+cudnnHandle_t cudnn_handle()
+{
+    static int init[16] = {0};
+    static cudnnHandle_t handle[16];
+    int i = cuda_get_device();
+    if(!init[i]) {
+        cudnnCreate(&handle[i]);
+        init[i] = 1;
+    }
+    return handle[i];
+}
+#endif
+
+cublasHandle_t blas_handle()
+{
+    static int init[16] = {0};
+    static cublasHandle_t handle[16];
+    int i = cuda_get_device();
+    if(!init[i]) {
+        cublasCreate(&handle[i]);
+        init[i] = 1;
+    }
+    return handle[i];
+}
+
+float *cuda_make_array(float *x, size_t n)
+{
+    float *x_gpu;
+    size_t size = sizeof(float)*n;
+    cudaError_t status = cudaMalloc((void **)&x_gpu, size);
+    check_error(status);
+    if(x){
+        status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice);
+        check_error(status);
+    } else {
+        fill_gpu(n, 0, x_gpu, 1);
+    }
+    if(!x_gpu) error("Cuda malloc failed\n");
+    return x_gpu;
+}
+
+void cuda_random(float *x_gpu, size_t n)
+{
+    static curandGenerator_t gen[16];
+    static int init[16] = {0};
+    int i = cuda_get_device();
+    if(!init[i]){
+        curandCreateGenerator(&gen[i], CURAND_RNG_PSEUDO_DEFAULT);
+        curandSetPseudoRandomGeneratorSeed(gen[i], time(0));
+        init[i] = 1;
+    }
+    curandGenerateUniform(gen[i], x_gpu, n);
+    check_error(cudaPeekAtLastError());
+}
+
+float cuda_compare(float *x_gpu, float *x, size_t n, char *s)
+{
+    float *tmp = calloc(n, sizeof(float));
+    cuda_pull_array(x_gpu, tmp, n);
+    //int i;
+    //for(i = 0; i < n; ++i) printf("%f %f\n", tmp[i], x[i]);
+    axpy_cpu(n, -1, x, 1, tmp, 1);
+    float err = dot_cpu(n, tmp, 1, tmp, 1);
+    printf("Error %s: %f\n", s, sqrt(err/n));
+    free(tmp);
+    return err;
+}
+
+int *cuda_make_int_array(int *x, size_t n)
+{
+    int *x_gpu;
+    size_t size = sizeof(int)*n;
+    cudaError_t status = cudaMalloc((void **)&x_gpu, size);
+    check_error(status);
+    if(x){
+        status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice);
+        check_error(status);
+    }
+    if(!x_gpu) error("Cuda malloc failed\n");
+    return x_gpu;
+}
+
+void cuda_free(float *x_gpu)
+{
+    cudaError_t status = cudaFree(x_gpu);
+    check_error(status);
+}
+
+void cuda_push_array(float *x_gpu, float *x, size_t n)
+{
+    size_t size = sizeof(float)*n;
+    cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice);
+    check_error(status);
+}
+
+void cuda_pull_array(float *x_gpu, float *x, size_t n)
+{
+    size_t size = sizeof(float)*n;
+    cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost);
+    check_error(status);
+}
+
+float cuda_mag_array(float *x_gpu, size_t n)
+{
+    float *temp = calloc(n, sizeof(float));
+    cuda_pull_array(x_gpu, temp, n);
+    float m = mag_array(temp, n);
+    free(temp);
+    return m;
+}
+#else
+void cuda_set_device(int n){}
+
+#endif
diff --git a/hanzi_detection/src/cuda.h b/hanzi_detection/src/cuda.h
new file mode 100755
index 0000000..a1bc216
--- /dev/null
+++ b/hanzi_detection/src/cuda.h
@@ -0,0 +1,20 @@
+#ifndef CUDA_H
+#define CUDA_H
+
+#include "darknet.h"
+
+#ifdef GPU
+
+void check_error(cudaError_t status);
+cublasHandle_t blas_handle();
+int *cuda_make_int_array(int *x, size_t n);
+void cuda_random(float *x_gpu, size_t n);
+float cuda_compare(float *x_gpu, float *x, size_t n, char *s);
+dim3 cuda_gridsize(size_t n);
+
+#ifdef CUDNN
+cudnnHandle_t cudnn_handle();
+#endif
+
+#endif
+#endif
diff --git a/hanzi_detection/src/data.c b/hanzi_detection/src/data.c
new file mode 100755
index 0000000..59051b4
--- /dev/null
+++ b/hanzi_detection/src/data.c
@@ -0,0 +1,1685 @@
+#include "data.h"
+#include "utils.h"
+#include "image.h"
+#include "cuda.h"
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;
+
+list *get_paths(char *filename)
+{
+    char *path;
+    FILE *file = fopen(filename, "r");
+    if(!file) file_error(filename);
+    list *lines = make_list();
+    while((path=fgetl(file))){
+        list_insert(lines, path);
+    }
+    fclose(file);
+    return lines;
+}
+
+/*
+char **get_random_paths_indexes(char **paths, int n, int m, int *indexes)
+{
+    char **random_paths = calloc(n, sizeof(char*));
+    int i;
+    pthread_mutex_lock(&mutex);
+    for(i = 0; i < n; ++i){
+        int index = rand()%m;
+        indexes[i] = index;
+        random_paths[i] = paths[index];
+        if(i == 0) printf("%s\n", paths[index]);
+    }
+    pthread_mutex_unlock(&mutex);
+    return random_paths;
+}
+*/
+
+char **get_random_paths(char **paths, int n, int m)
+{
+    char **random_paths = calloc(n, sizeof(char*));
+    int i;
+    pthread_mutex_lock(&mutex);
+    for(i = 0; i < n; ++i){
+        int index = rand()%m;
+        random_paths[i] = paths[index];
+        //if(i == 0) printf("%s\n", paths[index]);
+    }
+    pthread_mutex_unlock(&mutex);
+    return random_paths;
+}
+
+char **find_replace_paths(char **paths, int n, char *find, char *replace)
+{
+    char **replace_paths = calloc(n, sizeof(char*));
+    int i;
+    for(i = 0; i < n; ++i){
+        char replaced[4096];
+        find_replace(paths[i], find, replace, replaced);
+        replace_paths[i] = copy_string(replaced);
+    }
+    return replace_paths;
+}
+
+matrix load_image_paths_gray(char **paths, int n, int w, int h)
+{
+    int i;
+    matrix X;
+    X.rows = n;
+    X.vals = calloc(X.rows, sizeof(float*));
+    X.cols = 0;
+
+    for(i = 0; i < n; ++i){
+        image im = load_image(paths[i], w, h, 3);
+
+        image gray = grayscale_image(im);
+        free_image(im);
+        im = gray;
+
+        X.vals[i] = im.data;
+        X.cols = im.h*im.w*im.c;
+    }
+    return X;
+}
+
+matrix load_image_paths(char **paths, int n, int w, int h)
+{
+    int i;
+    matrix X;
+    X.rows = n;
+    X.vals = calloc(X.rows, sizeof(float*));
+    X.cols = 0;
+
+    for(i = 0; i < n; ++i){
+        image im = load_image_color(paths[i], w, h);
+        X.vals[i] = im.data;
+        X.cols = im.h*im.w*im.c;
+    }
+    return X;
+}
+
+matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
+{
+    int i;
+    matrix X;
+    X.rows = n;
+    X.vals = calloc(X.rows, sizeof(float*));
+    X.cols = 0;
+
+    for(i = 0; i < n; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image crop;
+        if(center){
+            crop = center_crop_image(im, size, size);
+        } else {
+            crop = random_augment_image(im, angle, aspect, min, max, size, size);
+        }
+        int flip = rand()%2;
+        if (flip) flip_image(crop);
+        random_distort_image(crop, hue, saturation, exposure);
+
+        /*
+        show_image(im, "orig");
+        show_image(crop, "crop");
+        cvWaitKey(0);
+        */
+        //grayscale_image_3c(crop);
+        free_image(im);
+        X.vals[i] = crop.data;
+        X.cols = crop.h*crop.w*crop.c;
+    }
+    return X;
+}
+
+
+box_label *read_boxes(char *filename, int *n)
+{
+    FILE *file = fopen(filename, "r");
+    if(!file) file_error(filename);
+    float x, y, h, w;
+    int id;
+    int count = 0;
+    int size = 64;
+    box_label *boxes = calloc(size, sizeof(box_label));
+    while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
+        if(count == size) {
+            size = size * 2;
+            boxes = realloc(boxes, size*sizeof(box_label));
+        }
+        boxes[count].id = id;
+        boxes[count].x = x;
+        boxes[count].y = y;
+        boxes[count].h = h;
+        boxes[count].w = w;
+        boxes[count].left   = x - w/2;
+        boxes[count].right  = x + w/2;
+        boxes[count].top    = y - h/2;
+        boxes[count].bottom = y + h/2;
+        ++count;
+    }
+    fclose(file);
+    *n = count;
+    return boxes;
+}
+
+void randomize_boxes(box_label *b, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        box_label swap = b[i];
+        int index = rand()%n;
+        b[i] = b[index];
+        b[index] = swap;
+    }
+}
+
+void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float sy, int flip)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        if(boxes[i].x == 0 && boxes[i].y == 0) {
+            boxes[i].x = 999999;
+            boxes[i].y = 999999;
+            boxes[i].w = 999999;
+            boxes[i].h = 999999;
+            continue;
+        }
+        boxes[i].left   = boxes[i].left  * sx - dx;
+        boxes[i].right  = boxes[i].right * sx - dx;
+        boxes[i].top    = boxes[i].top   * sy - dy;
+        boxes[i].bottom = boxes[i].bottom* sy - dy;
+
+        if(flip){
+            float swap = boxes[i].left;
+            boxes[i].left = 1. - boxes[i].right;
+            boxes[i].right = 1. - swap;
+        }
+
+        boxes[i].left =  constrain(0, 1, boxes[i].left);
+        boxes[i].right = constrain(0, 1, boxes[i].right);
+        boxes[i].top =   constrain(0, 1, boxes[i].top);
+        boxes[i].bottom =   constrain(0, 1, boxes[i].bottom);
+
+        boxes[i].x = (boxes[i].left+boxes[i].right)/2;
+        boxes[i].y = (boxes[i].top+boxes[i].bottom)/2;
+        boxes[i].w = (boxes[i].right - boxes[i].left);
+        boxes[i].h = (boxes[i].bottom - boxes[i].top);
+
+        boxes[i].w = constrain(0, 1, boxes[i].w);
+        boxes[i].h = constrain(0, 1, boxes[i].h);
+    }
+}
+
+void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "labels", labelpath);
+    find_replace(labelpath, "JPEGImages", "labels", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+
+    int count = 0;
+    box_label *boxes = read_boxes(labelpath, &count);
+    randomize_boxes(boxes, count);
+    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
+    float x,y,w,h;
+    int id;
+    int i;
+
+    for (i = 0; i < count && i < 90; ++i) {
+        x =  boxes[i].x;
+        y =  boxes[i].y;
+        w =  boxes[i].w;
+        h =  boxes[i].h;
+        id = boxes[i].id;
+
+        if (w < .0 || h < .0) continue;
+
+        int index = (4+classes) * i;
+
+        truth[index++] = x;
+        truth[index++] = y;
+        truth[index++] = w;
+        truth[index++] = h;
+
+        if (id < classes) truth[index+id] = 1;
+    }
+    free(boxes);
+}
+
+void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "labels", labelpath);
+    find_replace(labelpath, "JPEGImages", "labels", labelpath);
+
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".png", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    int count = 0;
+    box_label *boxes = read_boxes(labelpath, &count);
+    randomize_boxes(boxes, count);
+    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
+    float x,y,w,h;
+    int id;
+    int i;
+
+    for (i = 0; i < count; ++i) {
+        x =  boxes[i].x;
+        y =  boxes[i].y;
+        w =  boxes[i].w;
+        h =  boxes[i].h;
+        id = boxes[i].id;
+
+        if (w < .005 || h < .005) continue;
+
+        int col = (int)(x*num_boxes);
+        int row = (int)(y*num_boxes);
+
+        x = x*num_boxes - col;
+        y = y*num_boxes - row;
+
+        int index = (col+row*num_boxes)*(5+classes);
+        if (truth[index]) continue;
+        truth[index++] = 1;
+
+        if (id < classes) truth[index+id] = 1;
+        index += classes;
+
+        truth[index++] = x;
+        truth[index++] = y;
+        truth[index++] = w;
+        truth[index++] = h;
+    }
+    free(boxes);
+}
+
+void load_rle(image im, int *rle, int n)
+{
+    int count = 0;
+    int curr = 0;
+    int i,j;
+    for(i = 0; i < n; ++i){
+        for(j = 0; j < rle[i]; ++j){
+            im.data[count++] = curr;
+        }
+        curr = 1 - curr;
+    }
+    for(; count < im.h*im.w*im.c; ++count){
+        im.data[count] = curr;
+    }
+}
+
+void or_image(image src, image dest, int c)
+{
+    int i;
+    for(i = 0; i < src.w*src.h; ++i){
+        if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1;
+    }
+}
+
+void exclusive_image(image src)
+{
+    int k, j, i;
+    int s = src.w*src.h;
+    for(k = 0; k < src.c-1; ++k){
+        for(i = 0; i < s; ++i){
+            if (src.data[k*s + i]){
+                for(j = k+1; j < src.c; ++j){
+                    src.data[j*s + i] = 0;
+                }
+            }
+        }
+    }
+}
+
+box bound_image(image im)
+{
+    int x,y;
+    int minx = im.w;
+    int miny = im.h;
+    int maxx = 0;
+    int maxy = 0;
+    for(y = 0; y < im.h; ++y){
+        for(x = 0; x < im.w; ++x){
+            if(im.data[y*im.w + x]){
+                minx = (x < minx) ? x : minx;
+                miny = (y < miny) ? y : miny;
+                maxx = (x > maxx) ? x : maxx;
+                maxy = (y > maxy) ? y : maxy;
+            }
+        }
+    }
+    box b = {minx, miny, maxx-minx + 1, maxy-miny + 1};
+    //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
+    return b;
+}
+
+void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "mask", labelpath);
+    find_replace(labelpath, "JPEGImages", "mask", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
+    char buff[32788];
+    int id;
+    int i = 0;
+    int j;
+    image part = make_image(w, h, 1);
+    while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
+        int n = 0;
+        int *rle = read_intlist(buff, &n, 0);
+        load_rle(part, rle, n);
+        image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
+        if(flip) flip_image(sized);
+
+        image mask = resize_image(sized, mw, mh);
+        truth[i*(mw*mh+1)] = id;
+        for(j = 0; j < mw*mh; ++j){
+            truth[i*(mw*mh + 1) + 1 + j] = mask.data[j];
+        }
+        ++i;
+
+        free_image(mask);
+        free_image(sized);
+        free(rle);
+    }
+    if(i < num_boxes) truth[i*(mw*mh+1)] = -1;
+    fclose(file);
+    free_image(part);
+}
+
+void fill_truth_mask(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "mask", labelpath);
+    find_replace(labelpath, "JPEGImages", "mask", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
+    char buff[32788];
+    int id;
+    int i = 0;
+    image part = make_image(w, h, 1);
+    while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
+        int n = 0;
+        int *rle = read_intlist(buff, &n, 0);
+        load_rle(part, rle, n);
+        image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
+        if(flip) flip_image(sized);
+        box b = bound_image(sized);
+        if(b.w > 0){
+            image crop = crop_image(sized, b.x, b.y, b.w, b.h);
+            image mask = resize_image(crop, mw, mh);
+            truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w;
+            truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h;
+            truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w;
+            truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h;
+            int j;
+            for(j = 0; j < mw*mh; ++j){
+                truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j];
+            }
+            truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id;
+            free_image(crop);
+            free_image(mask);
+            ++i;
+        }
+        free_image(sized);
+        free(rle);
+    }
+    fclose(file);
+    free_image(part);
+}
+
+
+void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "labels", labelpath);
+    find_replace(labelpath, "JPEGImages", "labels", labelpath);
+
+    find_replace(labelpath, "raw", "labels", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".png", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    int count = 0;
+    box_label *boxes = read_boxes(labelpath, &count);
+    randomize_boxes(boxes, count);
+    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
+    if(count > num_boxes) count = num_boxes;
+    float x,y,w,h;
+    int id;
+    int i;
+    int sub = 0;
+
+    for (i = 0; i < count; ++i) {
+        x =  boxes[i].x;
+        y =  boxes[i].y;
+        w =  boxes[i].w;
+        h =  boxes[i].h;
+        id = boxes[i].id;
+
+        if ((w < .001 || h < .001)) {
+            ++sub;
+            continue;
+        }
+
+        truth[(i-sub)*5+0] = x;
+        truth[(i-sub)*5+1] = y;
+        truth[(i-sub)*5+2] = w;
+        truth[(i-sub)*5+3] = h;
+        truth[(i-sub)*5+4] = id;
+    }
+    free(boxes);
+}
+
+#define NUMCHARS 37
+
+void print_letters(float *pred, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        int index = max_index(pred+i*NUMCHARS, NUMCHARS);
+        printf("%c", int_to_alphanum(index));
+    }
+    printf("\n");
+}
+
+void fill_truth_captcha(char *path, int n, float *truth)
+{
+    char *begin = strrchr(path, '/');
+    ++begin;
+    int i;
+    for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
+        int index = alphanum_to_int(begin[i]);
+        if(index > 35) printf("Bad %c\n", begin[i]);
+        truth[i*NUMCHARS+index] = 1;
+    }
+    for(;i < n; ++i){
+        truth[i*NUMCHARS + NUMCHARS-1] = 1;
+    }
+}
+
+data load_data_captcha(char **paths, int n, int m, int k, int w, int h)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+    d.X = load_image_paths(paths, n, w, h);
+    d.y = make_matrix(n, k*NUMCHARS);
+    int i;
+    for(i = 0; i < n; ++i){
+        fill_truth_captcha(paths[i], k, d.y.vals[i]);
+    }
+    if(m) free(paths);
+    return d;
+}
+
+data load_data_captcha_encode(char **paths, int n, int m, int w, int h)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+    d.X = load_image_paths(paths, n, w, h);
+    d.X.cols = 17100;
+    d.y = d.X;
+    if(m) free(paths);
+    return d;
+}
+
+void fill_truth(char *path, char **labels, int k, float *truth)
+{
+    int i;
+    memset(truth, 0, k*sizeof(float));
+    int count = 0;
+    for(i = 0; i < k; ++i){
+        if(strstr(path, labels[i])){
+            truth[i] = 1;
+            ++count;
+            //printf("%s %s %d\n", path, labels[i], i);
+        }
+    }
+    if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path);
+}
+
+void fill_hierarchy(float *truth, int k, tree *hierarchy)
+{
+    int j;
+    for(j = 0; j < k; ++j){
+        if(truth[j]){
+            int parent = hierarchy->parent[j];
+            while(parent >= 0){
+                truth[parent] = 1;
+                parent = hierarchy->parent[parent];
+            }
+        }
+    }
+    int i;
+    int count = 0;
+    for(j = 0; j < hierarchy->groups; ++j){
+        //printf("%d\n", count);
+        int mask = 1;
+        for(i = 0; i < hierarchy->group_size[j]; ++i){
+            if(truth[count + i]){
+                mask = 0;
+                break;
+            }
+        }
+        if (mask) {
+            for(i = 0; i < hierarchy->group_size[j]; ++i){
+                truth[count + i] = SECRET_NUM;
+            }
+        }
+        count += hierarchy->group_size[j];
+    }
+}
+
+matrix load_regression_labels_paths(char **paths, int n, int k)
+{
+    matrix y = make_matrix(n, k);
+    int i,j;
+    for(i = 0; i < n; ++i){
+        char labelpath[4096];
+        find_replace(paths[i], "images", "labels", labelpath);
+        find_replace(labelpath, "JPEGImages", "labels", labelpath);
+        find_replace(labelpath, ".BMP", ".txt", labelpath);
+        find_replace(labelpath, ".JPEG", ".txt", labelpath);
+        find_replace(labelpath, ".JPG", ".txt", labelpath);
+        find_replace(labelpath, ".JPeG", ".txt", labelpath);
+        find_replace(labelpath, ".Jpeg", ".txt", labelpath);
+        find_replace(labelpath, ".PNG", ".txt", labelpath);
+        find_replace(labelpath, ".TIF", ".txt", labelpath);
+        find_replace(labelpath, ".bmp", ".txt", labelpath);
+        find_replace(labelpath, ".jpeg", ".txt", labelpath);
+        find_replace(labelpath, ".jpg", ".txt", labelpath);
+        find_replace(labelpath, ".png", ".txt", labelpath);
+        find_replace(labelpath, ".tif", ".txt", labelpath);
+
+        FILE *file = fopen(labelpath, "r");
+        for(j = 0; j < k; ++j){
+            fscanf(file, "%f", &(y.vals[i][j]));
+        }
+        fclose(file);
+    }
+    return y;
+}
+
+matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy)
+{
+    matrix y = make_matrix(n, k);
+    int i;
+    for(i = 0; i < n && labels; ++i){
+        fill_truth(paths[i], labels, k, y.vals[i]);
+        if(hierarchy){
+            fill_hierarchy(y.vals[i], k, hierarchy);
+        }
+    }
+    return y;
+}
+
+matrix load_tags_paths(char **paths, int n, int k)
+{
+    matrix y = make_matrix(n, k);
+    int i;
+    //int count = 0;
+    for(i = 0; i < n; ++i){
+        char label[4096];
+        find_replace(paths[i], "images", "labels", label);
+        find_replace(label, ".jpg", ".txt", label);
+        FILE *file = fopen(label, "r");
+        if (!file) continue;
+        //++count;
+        int tag;
+        while(fscanf(file, "%d", &tag) == 1){
+            if(tag < k){
+                y.vals[i][tag] = 1;
+            }
+        }
+        fclose(file);
+    }
+    //printf("%d/%d\n", count, n);
+    return y;
+}
+
+char **get_labels(char *filename)
+{
+    list *plist = get_paths(filename);
+    char **labels = (char **)list_to_array(plist);
+    free_list(plist);
+    return labels;
+}
+
+void free_data(data d)
+{
+    if(!d.shallow){
+        free_matrix(d.X);
+        free_matrix(d.y);
+    }else{
+        free(d.X.vals);
+        free(d.y.vals);
+    }
+}
+
+image get_segmentation_image(char *path, int w, int h, int classes)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "mask", labelpath);
+    find_replace(labelpath, "JPEGImages", "mask", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    image mask = make_image(w, h, classes);
+    FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
+    char buff[32788];
+    int id;
+    image part = make_image(w, h, 1);
+    while(fscanf(file, "%d %s", &id, buff) == 2){
+        int n = 0;
+        int *rle = read_intlist(buff, &n, 0);
+        load_rle(part, rle, n);
+        or_image(part, mask, id);
+        free(rle);
+    }
+    //exclusive_image(mask);
+    fclose(file);
+    free_image(part);
+    return mask;
+}
+
+image get_segmentation_image2(char *path, int w, int h, int classes)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "mask", labelpath);
+    find_replace(labelpath, "JPEGImages", "mask", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    image mask = make_image(w, h, classes+1);
+    int i;
+    for(i = 0; i < w*h; ++i){
+        mask.data[w*h*classes + i] = 1;
+    }
+    FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
+    char buff[32788];
+    int id;
+    image part = make_image(w, h, 1);
+    while(fscanf(file, "%d %s", &id, buff) == 2){
+        int n = 0;
+        int *rle = read_intlist(buff, &n, 0);
+        load_rle(part, rle, n);
+        or_image(part, mask, id);
+        for(i = 0; i < w*h; ++i){
+            if(part.data[i]) mask.data[w*h*classes + i] = 0;
+        }
+        free(rle);
+    }
+    //exclusive_image(mask);
+    fclose(file);
+    free_image(part);
+    return mask;
+}
+
+data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+
+    d.y.rows = n;
+    d.y.cols = h*w*classes/div/div;
+    d.y.vals = calloc(d.X.rows, sizeof(float*));
+
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+        augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
+        image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
+
+        int flip = rand()%2;
+        if(flip) flip_image(sized);
+        random_distort_image(sized, hue, saturation, exposure);
+        d.X.vals[i] = sized.data;
+
+        image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes);
+        //image mask = make_image(orig.w, orig.h, classes+1);
+        image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect);
+
+        if(flip) flip_image(sized_m);
+        d.y.vals[i] = sized_m.data;
+
+        free_image(orig);
+        free_image(mask);
+
+        /*
+           image rgb = mask_to_rgb(sized_m, classes);
+           show_image(rgb, "part");
+           show_image(sized, "orig");
+           cvWaitKey(0);
+           free_image(rgb);
+         */
+    }
+    free(random_paths);
+    return d;
+}
+
+data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int div, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+    d.y = make_matrix(n, (((w/div)*(h/div))+1)*boxes);
+
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+        augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
+        image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
+
+        int flip = rand()%2;
+        if(flip) flip_image(sized);
+        random_distort_image(sized, hue, saturation, exposure);
+        d.X.vals[i] = sized.data;
+        //show_image(sized, "image");
+
+        fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, w/div, h/div);
+
+        free_image(orig);
+
+        /*
+           image rgb = mask_to_rgb(sized_m, classes);
+           show_image(rgb, "part");
+           show_image(sized, "orig");
+           cvWaitKey(0);
+           free_image(rgb);
+         */
+    }
+    free(random_paths);
+    return d;
+}
+
+data load_data_mask(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+    d.y = make_matrix(n, (coords+1)*boxes);
+
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+        augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
+        image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
+
+        int flip = rand()%2;
+        if(flip) flip_image(sized);
+        random_distort_image(sized, hue, saturation, exposure);
+        d.X.vals[i] = sized.data;
+        //show_image(sized, "image");
+
+        fill_truth_mask(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14);
+
+        free_image(orig);
+
+        /*
+           image rgb = mask_to_rgb(sized_m, classes);
+           show_image(rgb, "part");
+           show_image(sized, "orig");
+           cvWaitKey(0);
+           free_image(rgb);
+         */
+    }
+    free(random_paths);
+    return d;
+}
+
+data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+
+    int k = size*size*(5+classes);
+    d.y = make_matrix(n, k);
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+
+        int oh = orig.h;
+        int ow = orig.w;
+
+        int dw = (ow*jitter);
+        int dh = (oh*jitter);
+
+        int pleft  = rand_uniform(-dw, dw);
+        int pright = rand_uniform(-dw, dw);
+        int ptop   = rand_uniform(-dh, dh);
+        int pbot   = rand_uniform(-dh, dh);
+
+        int swidth =  ow - pleft - pright;
+        int sheight = oh - ptop - pbot;
+
+        float sx = (float)swidth  / ow;
+        float sy = (float)sheight / oh;
+
+        int flip = rand()%2;
+        image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
+
+        float dx = ((float)pleft/ow)/sx;
+        float dy = ((float)ptop /oh)/sy;
+
+        image sized = resize_image(cropped, w, h);
+        if(flip) flip_image(sized);
+        random_distort_image(sized, hue, saturation, exposure);
+        d.X.vals[i] = sized.data;
+
+        fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy);
+
+        free_image(orig);
+        free_image(cropped);
+    }
+    free(random_paths);
+    return d;
+}
+
+data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
+{
+    if(m) paths = get_random_paths(paths, 2*n, m);
+    int i,j;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*6;
+
+    int k = 2*(classes);
+    d.y = make_matrix(n, k);
+    for(i = 0; i < n; ++i){
+        image im1 = load_image_color(paths[i*2],   w, h);
+        image im2 = load_image_color(paths[i*2+1], w, h);
+
+        d.X.vals[i] = calloc(d.X.cols, sizeof(float));
+        memcpy(d.X.vals[i],         im1.data, h*w*3*sizeof(float));
+        memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float));
+
+        int id;
+        float iou;
+
+        char imlabel1[4096];
+        char imlabel2[4096];
+        find_replace(paths[i*2],   "imgs", "labels", imlabel1);
+        find_replace(imlabel1, "jpg", "txt", imlabel1);
+        FILE *fp1 = fopen(imlabel1, "r");
+
+        while(fscanf(fp1, "%d %f", &id, &iou) == 2){
+            if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
+        }
+
+        find_replace(paths[i*2+1], "imgs", "labels", imlabel2);
+        find_replace(imlabel2, "jpg", "txt", imlabel2);
+        FILE *fp2 = fopen(imlabel2, "r");
+
+        while(fscanf(fp2, "%d %f", &id, &iou) == 2){
+            if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
+        }
+
+        for (j = 0; j < classes; ++j){
+            if (d.y.vals[i][2*j] > .5 &&  d.y.vals[i][2*j+1] < .5){
+                d.y.vals[i][2*j] = 1;
+                d.y.vals[i][2*j+1] = 0;
+            } else if (d.y.vals[i][2*j] < .5 &&  d.y.vals[i][2*j+1] > .5){
+                d.y.vals[i][2*j] = 0;
+                d.y.vals[i][2*j+1] = 1;
+            } else {
+                d.y.vals[i][2*j]   = SECRET_NUM;
+                d.y.vals[i][2*j+1] = SECRET_NUM;
+            }
+        }
+        fclose(fp1);
+        fclose(fp2);
+
+        free_image(im1);
+        free_image(im2);
+    }
+    if(m) free(paths);
+    return d;
+}
+
+data load_data_swag(char **paths, int n, int classes, float jitter)
+{
+    int index = rand()%n;
+    char *random_path = paths[index];
+
+    image orig = load_image_color(random_path, 0, 0);
+    int h = orig.h;
+    int w = orig.w;
+
+    data d = {0};
+    d.shallow = 0;
+    d.w = w;
+    d.h = h;
+
+    d.X.rows = 1;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+    int k = (4+classes)*90;
+    d.y = make_matrix(1, k);
+
+    int dw = w*jitter;
+    int dh = h*jitter;
+
+    int pleft  = rand_uniform(-dw, dw);
+    int pright = rand_uniform(-dw, dw);
+    int ptop   = rand_uniform(-dh, dh);
+    int pbot   = rand_uniform(-dh, dh);
+
+    int swidth =  w - pleft - pright;
+    int sheight = h - ptop - pbot;
+
+    float sx = (float)swidth  / w;
+    float sy = (float)sheight / h;
+
+    int flip = rand()%2;
+    image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
+
+    float dx = ((float)pleft/w)/sx;
+    float dy = ((float)ptop /h)/sy;
+
+    image sized = resize_image(cropped, w, h);
+    if(flip) flip_image(sized);
+    d.X.vals[0] = sized.data;
+
+    fill_truth_swag(random_path, d.y.vals[0], classes, flip, dx, dy, 1./sx, 1./sy);
+
+    free_image(orig);
+    free_image(cropped);
+
+    return d;
+}
+
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+    d.y = make_matrix(n, 5*boxes);
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+        image sized = make_image(w, h, orig.c);
+        fill_image(sized, .5);
+
+        float dw = jitter * orig.w;
+        float dh = jitter * orig.h;
+
+        float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh));
+        //float scale = rand_uniform(.25, 2);
+        float scale = 1;
+
+        float nw, nh;
+
+        if(new_ar < 1){
+            nh = scale * h;
+            nw = nh * new_ar;
+        } else {
+            nw = scale * w;
+            nh = nw / new_ar;
+        }
+
+        float dx = rand_uniform(0, w - nw);
+        float dy = rand_uniform(0, h - nh);
+
+        place_image(orig, nw, nh, dx, dy, sized);
+
+        random_distort_image(sized, hue, saturation, exposure);
+
+        int flip = rand()%2;
+        if(flip) flip_image(sized);
+        d.X.vals[i] = sized.data;
+
+
+        fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h);
+
+        free_image(orig);
+    }
+    free(random_paths);
+    return d;
+}
+
+void *load_thread(void *ptr)
+{
+    //printf("Loading data: %d\n", rand());
+    load_args a = *(struct load_args*)ptr;
+    if(a.exposure == 0) a.exposure = 1;
+    if(a.saturation == 0) a.saturation = 1;
+    if(a.aspect == 0) a.aspect = 1;
+
+    if (a.type == OLD_CLASSIFICATION_DATA){
+        *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
+    } else if (a.type == REGRESSION_DATA){
+        *a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+    } else if (a.type == CLASSIFICATION_DATA){
+        *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center);
+    } else if (a.type == SUPER_DATA){
+        *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
+    } else if (a.type == WRITING_DATA){
+        *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
+    } else if (a.type == ISEG_DATA){
+        *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.scale, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+    } else if (a.type == INSTANCE_DATA){
+        *a.d = load_data_mask(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+    } else if (a.type == SEGMENTATION_DATA){
+        *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale);
+    } else if (a.type == REGION_DATA){
+        *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
+    } else if (a.type == DETECTION_DATA){
+        *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
+    } else if (a.type == SWAG_DATA){
+        *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
+    } else if (a.type == COMPARE_DATA){
+        *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
+    } else if (a.type == IMAGE_DATA){
+        *(a.im) = load_image_color(a.path, 0, 0);
+        *(a.resized) = resize_image(*(a.im), a.w, a.h);
+    } else if (a.type == LETTERBOX_DATA){
+        *(a.im) = load_image_color(a.path, 0, 0);
+        *(a.resized) = letterbox_image(*(a.im), a.w, a.h);
+    } else if (a.type == TAG_DATA){
+        *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+    }
+    free(ptr);
+    return 0;
+}
+
+pthread_t load_data_in_thread(load_args args)
+{
+    pthread_t thread;
+    struct load_args *ptr = calloc(1, sizeof(struct load_args));
+    *ptr = args;
+    if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed");
+    return thread;
+}
+
+void *load_threads(void *ptr)
+{
+    int i;
+    load_args args = *(load_args *)ptr;
+    if (args.threads == 0) args.threads = 1;
+    data *out = args.d;
+    int total = args.n;
+    free(ptr);
+    data *buffers = calloc(args.threads, sizeof(data));
+    pthread_t *threads = calloc(args.threads, sizeof(pthread_t));
+    for(i = 0; i < args.threads; ++i){
+        args.d = buffers + i;
+        args.n = (i+1) * total/args.threads - i * total/args.threads;
+        threads[i] = load_data_in_thread(args);
+    }
+    for(i = 0; i < args.threads; ++i){
+        pthread_join(threads[i], 0);
+    }
+    *out = concat_datas(buffers, args.threads);
+    out->shallow = 0;
+    for(i = 0; i < args.threads; ++i){
+        buffers[i].shallow = 1;
+        free_data(buffers[i]);
+    }
+    free(buffers);
+    free(threads);
+    return 0;
+}
+
+void load_data_blocking(load_args args)
+{
+    struct load_args *ptr = calloc(1, sizeof(struct load_args));
+    *ptr = args;
+    load_thread(ptr);
+}
+
+pthread_t load_data(load_args args)
+{
+    pthread_t thread;
+    struct load_args *ptr = calloc(1, sizeof(struct load_args));
+    *ptr = args;
+    if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed");
+    return thread;
+}
+
+data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
+    data d = {0};
+    d.shallow = 0;
+    d.X = load_image_paths(paths, n, w, h);
+    d.y = load_image_paths_gray(replace_paths, n, out_w, out_h);
+    if(m) free(paths);
+    int i;
+    for(i = 0; i < n; ++i) free(replace_paths[i]);
+    free(replace_paths);
+    return d;
+}
+
+data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+    d.X = load_image_paths(paths, n, w, h);
+    d.y = load_labels_paths(paths, n, labels, k, 0);
+    if(m) free(paths);
+    return d;
+}
+
+/*
+   data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+   {
+   data d = {0};
+   d.indexes = calloc(n, sizeof(int));
+   if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
+   d.shallow = 0;
+   d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
+   d.y = load_labels_paths(paths, n, labels, k);
+   if(m) free(paths);
+   return d;
+   }
+ */
+
+data load_data_super(char **paths, int n, int m, int w, int h, int scale)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+
+    int i;
+    d.X.rows = n;
+    d.X.vals = calloc(n, sizeof(float*));
+    d.X.cols = w*h*3;
+
+    d.y.rows = n;
+    d.y.vals = calloc(n, sizeof(float*));
+    d.y.cols = w*scale * h*scale * 3;
+
+    for(i = 0; i < n; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image crop = random_crop_image(im, w*scale, h*scale);
+        int flip = rand()%2;
+        if (flip) flip_image(crop);
+        image resize = resize_image(crop, w, h);
+        d.X.vals[i] = resize.data;
+        d.y.vals[i] = crop.data;
+        free_image(im);
+    }
+
+    if(m) free(paths);
+    return d;
+}
+
+data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
+    d.y = load_regression_labels_paths(paths, n, k);
+    if(m) free(paths);
+    return d;
+}
+
+data select_data(data *orig, int *inds)
+{
+    data d = {0};
+    d.shallow = 1;
+    d.w = orig[0].w;
+    d.h = orig[0].h;
+
+    d.X.rows = orig[0].X.rows;
+    d.y.rows = orig[0].X.rows;
+
+    d.X.cols = orig[0].X.cols;
+    d.y.cols = orig[0].y.cols;
+
+    d.X.vals = calloc(orig[0].X.rows, sizeof(float *));
+    d.y.vals = calloc(orig[0].y.rows, sizeof(float *));
+    int i;
+    for(i = 0; i < d.X.rows; ++i){
+        d.X.vals[i] = orig[inds[i]].X.vals[i];
+        d.y.vals[i] = orig[inds[i]].y.vals[i];
+    }
+    return d;
+}
+
+data *tile_data(data orig, int divs, int size)
+{
+    data *ds = calloc(divs*divs, sizeof(data));
+    int i, j;
+#pragma omp parallel for
+    for(i = 0; i < divs*divs; ++i){
+        data d;
+        d.shallow = 0;
+        d.w = orig.w/divs * size;
+        d.h = orig.h/divs * size;
+        d.X.rows = orig.X.rows;
+        d.X.cols = d.w*d.h*3;
+        d.X.vals = calloc(d.X.rows, sizeof(float*));
+
+        d.y = copy_matrix(orig.y);
+#pragma omp parallel for
+        for(j = 0; j < orig.X.rows; ++j){
+            int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2;
+            int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2;
+            image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[j]);
+            d.X.vals[j] = crop_image(im, x, y, d.w, d.h).data;
+        }
+        ds[i] = d;
+    }
+    return ds;
+}
+
+data resize_data(data orig, int w, int h)
+{
+    data d = {0};
+    d.shallow = 0;
+    d.w = w;
+    d.h = h;
+    int i;
+    d.X.rows = orig.X.rows;
+    d.X.cols = w*h*3;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+
+    d.y = copy_matrix(orig.y);
+#pragma omp parallel for
+    for(i = 0; i < orig.X.rows; ++i){
+        image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]);
+        d.X.vals[i] = resize_image(im, w, h).data;
+    }
+    return d;
+}
+
+data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.shallow = 0;
+    d.w=size;
+    d.h=size;
+    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center);
+    d.y = load_labels_paths(paths, n, labels, k, hierarchy);
+    if(m) free(paths);
+    return d;
+}
+
+data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d = {0};
+    d.w = size;
+    d.h = size;
+    d.shallow = 0;
+    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
+    d.y = load_tags_paths(paths, n, k);
+    if(m) free(paths);
+    return d;
+}
+
+matrix concat_matrix(matrix m1, matrix m2)
+{
+    int i, count = 0;
+    matrix m;
+    m.cols = m1.cols;
+    m.rows = m1.rows+m2.rows;
+    m.vals = calloc(m1.rows + m2.rows, sizeof(float*));
+    for(i = 0; i < m1.rows; ++i){
+        m.vals[count++] = m1.vals[i];
+    }
+    for(i = 0; i < m2.rows; ++i){
+        m.vals[count++] = m2.vals[i];
+    }
+    return m;
+}
+
+data concat_data(data d1, data d2)
+{
+    data d = {0};
+    d.shallow = 1;
+    d.X = concat_matrix(d1.X, d2.X);
+    d.y = concat_matrix(d1.y, d2.y);
+    d.w = d1.w;
+    d.h = d1.h;
+    return d;
+}
+
+data concat_datas(data *d, int n)
+{
+    int i;
+    data out = {0};
+    for(i = 0; i < n; ++i){
+        data new = concat_data(d[i], out);
+        free_data(out);
+        out = new;
+    }
+    return out;
+}
+
+data load_categorical_data_csv(char *filename, int target, int k)
+{
+    data d = {0};
+    d.shallow = 0;
+    matrix X = csv_to_matrix(filename);
+    float *truth_1d = pop_column(&X, target);
+    float **truth = one_hot_encode(truth_1d, X.rows, k);
+    matrix y;
+    y.rows = X.rows;
+    y.cols = k;
+    y.vals = truth;
+    d.X = X;
+    d.y = y;
+    free(truth_1d);
+    return d;
+}
+
+data load_cifar10_data(char *filename)
+{
+    data d = {0};
+    d.shallow = 0;
+    long i,j;
+    matrix X = make_matrix(10000, 3072);
+    matrix y = make_matrix(10000, 10);
+    d.X = X;
+    d.y = y;
+
+    FILE *fp = fopen(filename, "rb");
+    if(!fp) file_error(filename);
+    for(i = 0; i < 10000; ++i){
+        unsigned char bytes[3073];
+        fread(bytes, 1, 3073, fp);
+        int class = bytes[0];
+        y.vals[i][class] = 1;
+        for(j = 0; j < X.cols; ++j){
+            X.vals[i][j] = (double)bytes[j+1];
+        }
+    }
+    scale_data_rows(d, 1./255);
+    //normalize_data_rows(d);
+    fclose(fp);
+    return d;
+}
+
+void get_random_batch(data d, int n, float *X, float *y)
+{
+    int j;
+    for(j = 0; j < n; ++j){
+        int index = rand()%d.X.rows;
+        memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
+        memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
+    }
+}
+
+void get_next_batch(data d, int n, int offset, float *X, float *y)
+{
+    int j;
+    for(j = 0; j < n; ++j){
+        int index = offset + j;
+        memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
+        if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
+    }
+}
+
+void smooth_data(data d)
+{
+    int i, j;
+    float scale = 1. / d.y.cols;
+    float eps = .1;
+    for(i = 0; i < d.y.rows; ++i){
+        for(j = 0; j < d.y.cols; ++j){
+            d.y.vals[i][j] = eps * scale + (1-eps) * d.y.vals[i][j];
+        }
+    }
+}
+
+data load_all_cifar10()
+{
+    data d = {0};
+    d.shallow = 0;
+    int i,j,b;
+    matrix X = make_matrix(50000, 3072);
+    matrix y = make_matrix(50000, 10);
+    d.X = X;
+    d.y = y;
+
+
+    for(b = 0; b < 5; ++b){
+        char buff[256];
+        sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1);
+        FILE *fp = fopen(buff, "rb");
+        if(!fp) file_error(buff);
+        for(i = 0; i < 10000; ++i){
+            unsigned char bytes[3073];
+            fread(bytes, 1, 3073, fp);
+            int class = bytes[0];
+            y.vals[i+b*10000][class] = 1;
+            for(j = 0; j < X.cols; ++j){
+                X.vals[i+b*10000][j] = (double)bytes[j+1];
+            }
+        }
+        fclose(fp);
+    }
+    //normalize_data_rows(d);
+    scale_data_rows(d, 1./255);
+    smooth_data(d);
+    return d;
+}
+
+data load_go(char *filename)
+{
+    FILE *fp = fopen(filename, "rb");
+    matrix X = make_matrix(3363059, 361);
+    matrix y = make_matrix(3363059, 361);
+    int row, col;
+
+    if(!fp) file_error(filename);
+    char *label;
+    int count = 0;
+    while((label = fgetl(fp))){
+        int i;
+        if(count == X.rows){
+            X = resize_matrix(X, count*2);
+            y = resize_matrix(y, count*2);
+        }
+        sscanf(label, "%d %d", &row, &col);
+        char *board = fgetl(fp);
+
+        int index = row*19 + col;
+        y.vals[count][index] = 1;
+
+        for(i = 0; i < 19*19; ++i){
+            float val = 0;
+            if(board[i] == '1') val = 1;
+            else if(board[i] == '2') val = -1;
+            X.vals[count][i] = val;
+        }
+        ++count;
+        free(label);
+        free(board);
+    }
+    X = resize_matrix(X, count);
+    y = resize_matrix(y, count);
+
+    data d = {0};
+    d.shallow = 0;
+    d.X = X;
+    d.y = y;
+
+
+    fclose(fp);
+
+    return d;
+}
+
+
+void randomize_data(data d)
+{
+    int i;
+    for(i = d.X.rows-1; i > 0; --i){
+        int index = rand()%i;
+        float *swap = d.X.vals[index];
+        d.X.vals[index] = d.X.vals[i];
+        d.X.vals[i] = swap;
+
+        swap = d.y.vals[index];
+        d.y.vals[index] = d.y.vals[i];
+        d.y.vals[i] = swap;
+    }
+}
+
+void scale_data_rows(data d, float s)
+{
+    int i;
+    for(i = 0; i < d.X.rows; ++i){
+        scale_array(d.X.vals[i], d.X.cols, s);
+    }
+}
+
+void translate_data_rows(data d, float s)
+{
+    int i;
+    for(i = 0; i < d.X.rows; ++i){
+        translate_array(d.X.vals[i], d.X.cols, s);
+    }
+}
+
+data copy_data(data d)
+{
+    data c = {0};
+    c.w = d.w;
+    c.h = d.h;
+    c.shallow = 0;
+    c.num_boxes = d.num_boxes;
+    c.boxes = d.boxes;
+    c.X = copy_matrix(d.X);
+    c.y = copy_matrix(d.y);
+    return c;
+}
+
+void normalize_data_rows(data d)
+{
+    int i;
+    for(i = 0; i < d.X.rows; ++i){
+        normalize_array(d.X.vals[i], d.X.cols);
+    }
+}
+
+data get_data_part(data d, int part, int total)
+{
+    data p = {0};
+    p.shallow = 1;
+    p.X.rows = d.X.rows * (part + 1) / total - d.X.rows * part / total;
+    p.y.rows = d.y.rows * (part + 1) / total - d.y.rows * part / total;
+    p.X.cols = d.X.cols;
+    p.y.cols = d.y.cols;
+    p.X.vals = d.X.vals + d.X.rows * part / total;
+    p.y.vals = d.y.vals + d.y.rows * part / total;
+    return p;
+}
+
+data get_random_data(data d, int num)
+{
+    data r = {0};
+    r.shallow = 1;
+
+    r.X.rows = num;
+    r.y.rows = num;
+
+    r.X.cols = d.X.cols;
+    r.y.cols = d.y.cols;
+
+    r.X.vals = calloc(num, sizeof(float *));
+    r.y.vals = calloc(num, sizeof(float *));
+
+    int i;
+    for(i = 0; i < num; ++i){
+        int index = rand()%d.X.rows;
+        r.X.vals[i] = d.X.vals[index];
+        r.y.vals[i] = d.y.vals[index];
+    }
+    return r;
+}
+
+data *split_data(data d, int part, int total)
+{
+    data *split = calloc(2, sizeof(data));
+    int i;
+    int start = part*d.X.rows/total;
+    int end = (part+1)*d.X.rows/total;
+    data train;
+    data test;
+    train.shallow = test.shallow = 1;
+
+    test.X.rows = test.y.rows = end-start;
+    train.X.rows = train.y.rows = d.X.rows - (end-start);
+    train.X.cols = test.X.cols = d.X.cols;
+    train.y.cols = test.y.cols = d.y.cols;
+
+    train.X.vals = calloc(train.X.rows, sizeof(float*));
+    test.X.vals = calloc(test.X.rows, sizeof(float*));
+    train.y.vals = calloc(train.y.rows, sizeof(float*));
+    test.y.vals = calloc(test.y.rows, sizeof(float*));
+
+    for(i = 0; i < start; ++i){
+        train.X.vals[i] = d.X.vals[i];
+        train.y.vals[i] = d.y.vals[i];
+    }
+    for(i = start; i < end; ++i){
+        test.X.vals[i-start] = d.X.vals[i];
+        test.y.vals[i-start] = d.y.vals[i];
+    }
+    for(i = end; i < d.X.rows; ++i){
+        train.X.vals[i-(end-start)] = d.X.vals[i];
+        train.y.vals[i-(end-start)] = d.y.vals[i];
+    }
+    split[0] = train;
+    split[1] = test;
+    return split;
+}
+
diff --git a/hanzi_detection/src/data.h b/hanzi_detection/src/data.h
new file mode 100755
index 0000000..781906f
--- /dev/null
+++ b/hanzi_detection/src/data.h
@@ -0,0 +1,50 @@
+#ifndef DATA_H
+#define DATA_H
+#include <pthread.h>
+
+#include "darknet.h"
+#include "matrix.h"
+#include "list.h"
+#include "image.h"
+#include "tree.h"
+
+static inline float distance_from_edge(int x, int max)
+{
+    int dx = (max/2) - x;
+    if (dx < 0) dx = -dx;
+    dx = (max/2) + 1 - dx;
+    dx *= 2;
+    float dist = (float)dx/max;
+    if (dist > 1) dist = 1;
+    return dist;
+}
+void load_data_blocking(load_args args);
+
+
+void print_letters(float *pred, int n);
+data load_data_captcha(char **paths, int n, int m, int k, int w, int h);
+data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure);
+data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
+matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
+data load_data_super(char **paths, int n, int m, int w, int h, int scale);
+data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
+data load_data_regression(char **paths, int n, int m, int classes, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
+data load_go(char *filename);
+
+
+data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h);
+
+void get_random_batch(data d, int n, float *X, float *y);
+data get_data_part(data d, int part, int total);
+data get_random_data(data d, int num);
+data load_categorical_data_csv(char *filename, int target, int k);
+void normalize_data_rows(data d);
+void scale_data_rows(data d, float s);
+void translate_data_rows(data d, float s);
+void randomize_data(data d);
+data *split_data(data d, int part, int total);
+data concat_datas(data *d, int n);
+void fill_truth(char *path, char **labels, int k, float *truth);
+
+#endif
diff --git a/hanzi_detection/src/deconvolutional_kernels.cu b/hanzi_detection/src/deconvolutional_kernels.cu
new file mode 100755
index 0000000..8267dcf
--- /dev/null
+++ b/hanzi_detection/src/deconvolutional_kernels.cu
@@ -0,0 +1,139 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
+#include "batchnorm_layer.h"
+#include "gemm.h"
+#include "blas.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "utils.h"
+#include "cuda.h"
+}
+
+extern "C" void forward_deconvolutional_layer_gpu(layer l, network net)
+{
+    int i;
+
+    int m = l.size*l.size*l.n;
+    int n = l.h*l.w;
+    int k = l.c;
+
+    fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
+
+    for(i = 0; i < l.batch; ++i){
+        float *a = l.weights_gpu;
+        float *b = net.input_gpu + i*l.c*l.h*l.w;
+        float *c = net.workspace;
+
+        gemm_gpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
+
+        col2im_gpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output_gpu+i*l.outputs);
+    }
+    if (l.batch_normalize) {
+        forward_batchnorm_layer_gpu(l, net);
+    } else {
+        add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+    }
+    activate_array_gpu(l.output_gpu, l.batch*l.n*l.out_w*l.out_h, l.activation);
+}
+
+extern "C" void backward_deconvolutional_layer_gpu(layer l, network net)
+{
+    int i;
+
+    //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+
+    if(l.batch_normalize){
+        backward_batchnorm_layer_gpu(l, net);
+    } else {
+        backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
+    }
+
+    //if(net.delta_gpu) memset(net.delta_gpu, 0, l.batch*l.h*l.w*l.c*sizeof(float));
+
+    for(i = 0; i < l.batch; ++i){
+        int m = l.c;
+        int n = l.size*l.size*l.n;
+        int k = l.h*l.w;
+
+        float *a = net.input_gpu + i*m*k;
+        float *b = net.workspace;
+        float *c = l.weight_updates_gpu;
+
+        im2col_gpu(l.delta_gpu + i*l.outputs, l.out_c, l.out_h, l.out_w, 
+                l.size, l.stride, l.pad, b);
+        gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+        if(net.delta_gpu){
+            int m = l.c;
+            int n = l.h*l.w;
+            int k = l.size*l.size*l.n;
+
+            float *a = l.weights_gpu;
+            float *b = net.workspace;
+            float *c = net.delta_gpu + i*n*m;
+
+            gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
+        }
+    }
+}
+
+extern "C" void pull_deconvolutional_layer(layer l)
+{
+    cuda_pull_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size);
+    cuda_pull_array(l.biases_gpu, l.biases, l.n);
+    cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size);
+    cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
+    if (l.batch_normalize){
+        cuda_pull_array(l.scales_gpu, l.scales, l.n);
+        cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n);
+        cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n);
+    }
+}
+
+extern "C" void push_deconvolutional_layer(layer l)
+{
+    cuda_push_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size);
+    cuda_push_array(l.biases_gpu, l.biases, l.n);
+    cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size);
+    cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
+    if (l.batch_normalize){
+        cuda_push_array(l.scales_gpu, l.scales, l.n);
+        cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n);
+        cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n);
+    }
+}
+
+void update_deconvolutional_layer_gpu(layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    if(a.adam){
+        adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t);
+        adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t);
+        if(l.scales_gpu){
+            adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t);
+        }
+    }else{
+        axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+        axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+        scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1);
+
+        axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+        scal_gpu(l.n, momentum, l.bias_updates_gpu, 1);
+
+        if(l.scales_gpu){
+            axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
+            scal_gpu(l.n, momentum, l.scale_updates_gpu, 1);
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/deconvolutional_layer.c b/hanzi_detection/src/deconvolutional_layer.c
new file mode 100755
index 0000000..00c0e85
--- /dev/null
+++ b/hanzi_detection/src/deconvolutional_layer.c
@@ -0,0 +1,312 @@
+#include "deconvolutional_layer.h"
+#include "convolutional_layer.h"
+#include "batchnorm_layer.h"
+#include "utils.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <stdio.h>
+#include <time.h>
+
+
+static size_t get_workspace_size(layer l){
+    return (size_t)l.h*l.w*l.size*l.size*l.n*sizeof(float);
+}
+
+void bilinear_init(layer l)
+{
+    int i,j,f;
+    float center = (l.size-1) / 2.;
+    for(f = 0; f < l.n; ++f){
+        for(j = 0; j < l.size; ++j){
+            for(i = 0; i < l.size; ++i){
+                float val = (1 - fabs(i - center)) * (1 - fabs(j - center));
+                int c = f%l.c;
+                int ind = f*l.size*l.size*l.c + c*l.size*l.size + j*l.size + i;
+                l.weights[ind] = val;
+            }
+        }
+    }
+}
+
+
+layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam)
+{
+    int i;
+    layer l = {0};
+    l.type = DECONVOLUTIONAL;
+
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.n = n;
+    l.batch = batch;
+    l.stride = stride;
+    l.size = size;
+
+    l.nweights = c*n*size*size;
+    l.nbiases = n;
+
+    l.weights = calloc(c*n*size*size, sizeof(float));
+    l.weight_updates = calloc(c*n*size*size, sizeof(float));
+
+    l.biases = calloc(n, sizeof(float));
+    l.bias_updates = calloc(n, sizeof(float));
+    //float scale = n/(size*size*c);
+    //printf("scale: %f\n", scale);
+    float scale = .02;
+    for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
+    //bilinear_init(l);
+    for(i = 0; i < n; ++i){
+        l.biases[i] = 0;
+    }
+    l.pad = padding;
+
+    l.out_h = (l.h - 1) * l.stride + l.size - 2*l.pad;
+    l.out_w = (l.w - 1) * l.stride + l.size - 2*l.pad;
+    l.out_c = n;
+    l.outputs = l.out_w * l.out_h * l.out_c;
+    l.inputs = l.w * l.h * l.c;
+
+    scal_cpu(l.nweights, (float)l.out_w*l.out_h/(l.w*l.h), l.weights, 1);
+
+    l.output = calloc(l.batch*l.outputs, sizeof(float));
+    l.delta  = calloc(l.batch*l.outputs, sizeof(float));
+
+    l.forward = forward_deconvolutional_layer;
+    l.backward = backward_deconvolutional_layer;
+    l.update = update_deconvolutional_layer;
+
+    l.batch_normalize = batch_normalize;
+
+    if(batch_normalize){
+        l.scales = calloc(n, sizeof(float));
+        l.scale_updates = calloc(n, sizeof(float));
+        for(i = 0; i < n; ++i){
+            l.scales[i] = 1;
+        }
+
+        l.mean = calloc(n, sizeof(float));
+        l.variance = calloc(n, sizeof(float));
+
+        l.mean_delta = calloc(n, sizeof(float));
+        l.variance_delta = calloc(n, sizeof(float));
+
+        l.rolling_mean = calloc(n, sizeof(float));
+        l.rolling_variance = calloc(n, sizeof(float));
+        l.x = calloc(l.batch*l.outputs, sizeof(float));
+        l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
+    }
+    if(adam){
+        l.m = calloc(c*n*size*size, sizeof(float));
+        l.v = calloc(c*n*size*size, sizeof(float));
+        l.bias_m = calloc(n, sizeof(float));
+        l.scale_m = calloc(n, sizeof(float));
+        l.bias_v = calloc(n, sizeof(float));
+        l.scale_v = calloc(n, sizeof(float));
+    }
+
+#ifdef GPU
+    l.forward_gpu = forward_deconvolutional_layer_gpu;
+    l.backward_gpu = backward_deconvolutional_layer_gpu;
+    l.update_gpu = update_deconvolutional_layer_gpu;
+
+    if(gpu_index >= 0){
+
+        if (adam) {
+            l.m_gpu = cuda_make_array(l.m, c*n*size*size);
+            l.v_gpu = cuda_make_array(l.v, c*n*size*size);
+            l.bias_m_gpu = cuda_make_array(l.bias_m, n);
+            l.bias_v_gpu = cuda_make_array(l.bias_v, n);
+            l.scale_m_gpu = cuda_make_array(l.scale_m, n);
+            l.scale_v_gpu = cuda_make_array(l.scale_v, n);
+        }
+        l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
+        l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
+
+        l.biases_gpu = cuda_make_array(l.biases, n);
+        l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
+
+        l.delta_gpu = cuda_make_array(l.delta, l.batch*l.out_h*l.out_w*n);
+        l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n);
+
+        if(batch_normalize){
+            l.mean_gpu = cuda_make_array(0, n);
+            l.variance_gpu = cuda_make_array(0, n);
+
+            l.rolling_mean_gpu = cuda_make_array(0, n);
+            l.rolling_variance_gpu = cuda_make_array(0, n);
+
+            l.mean_delta_gpu = cuda_make_array(0, n);
+            l.variance_delta_gpu = cuda_make_array(0, n);
+
+            l.scales_gpu = cuda_make_array(l.scales, n);
+            l.scale_updates_gpu = cuda_make_array(0, n);
+
+            l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
+            l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
+        }
+    }
+    #ifdef CUDNN
+        cudnnCreateTensorDescriptor(&l.dstTensorDesc);
+        cudnnCreateTensorDescriptor(&l.normTensorDesc);
+        cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
+        cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); 
+    #endif
+#endif
+
+    l.activation = activation;
+    l.workspace_size = get_workspace_size(l);
+
+    fprintf(stderr, "deconv%5d %2d x%2d /%2d  %4d x%4d x%4d   ->  %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
+
+    return l;
+}
+
+void denormalize_deconvolutional_layer(layer l)
+{
+    int i, j;
+    for(i = 0; i < l.n; ++i){
+        float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
+        for(j = 0; j < l.c*l.size*l.size; ++j){
+            l.weights[i*l.c*l.size*l.size + j] *= scale;
+        }
+        l.biases[i] -= l.rolling_mean[i] * scale;
+        l.scales[i] = 1;
+        l.rolling_mean[i] = 0;
+        l.rolling_variance[i] = 1;
+    }
+}
+
+void resize_deconvolutional_layer(layer *l, int h, int w)
+{
+    l->h = h;
+    l->w = w;
+    l->out_h = (l->h - 1) * l->stride + l->size - 2*l->pad;
+    l->out_w = (l->w - 1) * l->stride + l->size - 2*l->pad;
+
+    l->outputs = l->out_h * l->out_w * l->out_c;
+    l->inputs = l->w * l->h * l->c;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta  = realloc(l->delta,  l->batch*l->outputs*sizeof(float));
+    if(l->batch_normalize){
+        l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
+        l->x_norm  = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
+    }
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =  cuda_make_array(l->delta,  l->batch*l->outputs);
+    l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+
+    if(l->batch_normalize){
+        cuda_free(l->x_gpu);
+        cuda_free(l->x_norm_gpu);
+
+        l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+        l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+    }
+    #ifdef CUDNN
+        cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
+        cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); 
+    #endif
+#endif
+    l->workspace_size = get_workspace_size(*l);
+}
+
+void forward_deconvolutional_layer(const layer l, network net)
+{
+    int i;
+
+    int m = l.size*l.size*l.n;
+    int n = l.h*l.w;
+    int k = l.c;
+
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+
+    for(i = 0; i < l.batch; ++i){
+        float *a = l.weights;
+        float *b = net.input + i*l.c*l.h*l.w;
+        float *c = net.workspace;
+
+        gemm_cpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
+
+        col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output+i*l.outputs);
+    }
+    if (l.batch_normalize) {
+        forward_batchnorm_layer(l, net);
+    } else {
+        add_bias(l.output, l.biases, l.batch, l.n, l.out_w*l.out_h);
+    }
+    activate_array(l.output, l.batch*l.n*l.out_w*l.out_h, l.activation);
+}
+
+void backward_deconvolutional_layer(layer l, network net)
+{
+    int i;
+
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+
+    if(l.batch_normalize){
+        backward_batchnorm_layer(l, net);
+    } else {
+        backward_bias(l.bias_updates, l.delta, l.batch, l.n, l.out_w*l.out_h);
+    }
+
+    //if(net.delta) memset(net.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float));
+
+    for(i = 0; i < l.batch; ++i){
+        int m = l.c;
+        int n = l.size*l.size*l.n;
+        int k = l.h*l.w;
+
+        float *a = net.input + i*m*k;
+        float *b = net.workspace;
+        float *c = l.weight_updates;
+
+        im2col_cpu(l.delta + i*l.outputs, l.out_c, l.out_h, l.out_w, 
+                l.size, l.stride, l.pad, b);
+        gemm_cpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+        if(net.delta){
+            int m = l.c;
+            int n = l.h*l.w;
+            int k = l.size*l.size*l.n;
+
+            float *a = l.weights;
+            float *b = net.workspace;
+            float *c = net.delta + i*n*m;
+
+            gemm_cpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
+        }
+    }
+}
+
+void update_deconvolutional_layer(layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    int size = l.size*l.size*l.c*l.n;
+    axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+    scal_cpu(l.n, momentum, l.bias_updates, 1);
+
+    if(l.scales){
+        axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
+        scal_cpu(l.n, momentum, l.scale_updates, 1);
+    }
+
+    axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
+    axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+    scal_cpu(size, momentum, l.weight_updates, 1);
+}
+
+
+
diff --git a/hanzi_detection/src/deconvolutional_layer.h b/hanzi_detection/src/deconvolutional_layer.h
new file mode 100755
index 0000000..b254fb9
--- /dev/null
+++ b/hanzi_detection/src/deconvolutional_layer.h
@@ -0,0 +1,25 @@
+#ifndef DECONVOLUTIONAL_LAYER_H
+#define DECONVOLUTIONAL_LAYER_H
+
+#include "cuda.h"
+#include "image.h"
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+#ifdef GPU
+void forward_deconvolutional_layer_gpu(layer l, network net);
+void backward_deconvolutional_layer_gpu(layer l, network net);
+void update_deconvolutional_layer_gpu(layer l, update_args a);
+void push_deconvolutional_layer(layer l);
+void pull_deconvolutional_layer(layer l);
+#endif
+
+layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam);
+void resize_deconvolutional_layer(layer *l, int h, int w);
+void forward_deconvolutional_layer(const layer l, network net);
+void update_deconvolutional_layer(layer l, update_args a);
+void backward_deconvolutional_layer(layer l, network net);
+
+#endif
+
diff --git a/hanzi_detection/src/demo.c b/hanzi_detection/src/demo.c
new file mode 100755
index 0000000..b89efb8
--- /dev/null
+++ b/hanzi_detection/src/demo.c
@@ -0,0 +1,349 @@
+#include "network.h"
+#include "detection_layer.h"
+#include "region_layer.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "box.h"
+#include "image.h"
+#include "demo.h"
+#include <sys/time.h>
+
+#define DEMO 1
+
+#ifdef OPENCV
+
+static char **demo_names;
+static image **demo_alphabet;
+static int demo_classes;
+
+static network *net;
+static image buff [3];
+static image buff_letter[3];
+static int buff_index = 0;
+static void * cap;
+static float fps = 0;
+static float demo_thresh = 0;
+static float demo_hier = .5;
+static int running = 0;
+
+static int demo_frame = 3;
+static int demo_index = 0;
+static float **predictions;
+static float *avg;
+static int demo_done = 0;
+static int demo_total = 0;
+double demo_time;
+
+detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num);
+
+int size_network(network *net)
+{
+    int i;
+    int count = 0;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == YOLO || l.type == REGION || l.type == DETECTION){
+            count += l.outputs;
+        }
+    }
+    return count;
+}
+
+void remember_network(network *net)
+{
+    int i;
+    int count = 0;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == YOLO || l.type == REGION || l.type == DETECTION){
+            memcpy(predictions[demo_index] + count, net->layers[i].output, sizeof(float) * l.outputs);
+            count += l.outputs;
+        }
+    }
+}
+
+detection *avg_predictions(network *net, int *nboxes)
+{
+    int i, j;
+    int count = 0;
+    fill_cpu(demo_total, 0, avg, 1);
+    for(j = 0; j < demo_frame; ++j){
+        axpy_cpu(demo_total, 1./demo_frame, predictions[j], 1, avg, 1);
+    }
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == YOLO || l.type == REGION || l.type == DETECTION){
+            memcpy(l.output, avg + count, sizeof(float) * l.outputs);
+            count += l.outputs;
+        }
+    }
+    detection *dets = get_network_boxes(net, buff[0].w, buff[0].h, demo_thresh, demo_hier, 0, 1, nboxes);
+    return dets;
+}
+
+void *detect_in_thread(void *ptr)
+{
+    running = 1;
+    float nms = .4;
+
+    layer l = net->layers[net->n-1];
+    float *X = buff_letter[(buff_index+2)%3].data;
+    network_predict(net, X);
+
+    /*
+       if(l.type == DETECTION){
+       get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
+       } else */
+    remember_network(net);
+    detection *dets = 0;
+    int nboxes = 0;
+    dets = avg_predictions(net, &nboxes);
+
+
+    /*
+       int i,j;
+       box zero = {0};
+       int classes = l.classes;
+       for(i = 0; i < demo_detections; ++i){
+       avg[i].objectness = 0;
+       avg[i].bbox = zero;
+       memset(avg[i].prob, 0, classes*sizeof(float));
+       for(j = 0; j < demo_frame; ++j){
+       axpy_cpu(classes, 1./demo_frame, dets[j][i].prob, 1, avg[i].prob, 1);
+       avg[i].objectness += dets[j][i].objectness * 1./demo_frame;
+       avg[i].bbox.x += dets[j][i].bbox.x * 1./demo_frame;
+       avg[i].bbox.y += dets[j][i].bbox.y * 1./demo_frame;
+       avg[i].bbox.w += dets[j][i].bbox.w * 1./demo_frame;
+       avg[i].bbox.h += dets[j][i].bbox.h * 1./demo_frame;
+       }
+    //copy_cpu(classes, dets[0][i].prob, 1, avg[i].prob, 1);
+    //avg[i].objectness = dets[0][i].objectness;
+    }
+     */
+
+    if (nms > 0) do_nms_obj(dets, nboxes, l.classes, nms);
+
+    printf("\033[2J");
+    printf("\033[1;1H");
+    printf("\nFPS:%.1f\n",fps);
+    printf("Objects:\n\n");
+    image display = buff[(buff_index+2) % 3];
+    draw_detections(display, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes);
+    free_detections(dets, nboxes);
+
+    demo_index = (demo_index + 1)%demo_frame;
+    running = 0;
+    return 0;
+}
+
+void *fetch_in_thread(void *ptr)
+{
+    free_image(buff[buff_index]);
+    buff[buff_index] = get_image_from_stream(cap);
+    if(buff[buff_index].data == 0) {
+        demo_done = 1;
+        return 0;
+    }
+    letterbox_image_into(buff[buff_index], net->w, net->h, buff_letter[buff_index]);
+    return 0;
+}
+
+void *display_in_thread(void *ptr)
+{
+    int c = show_image(buff[(buff_index + 1)%3], "Demo", 1);
+    if (c != -1) c = c%256;
+    if (c == 27) {
+        demo_done = 1;
+        return 0;
+    } else if (c == 82) {
+        demo_thresh += .02;
+    } else if (c == 84) {
+        demo_thresh -= .02;
+        if(demo_thresh <= .02) demo_thresh = .02;
+    } else if (c == 83) {
+        demo_hier += .02;
+    } else if (c == 81) {
+        demo_hier -= .02;
+        if(demo_hier <= .0) demo_hier = .0;
+    }
+    return 0;
+}
+
+void *display_loop(void *ptr)
+{
+    while(1){
+        display_in_thread(0);
+    }
+}
+
+void *detect_loop(void *ptr)
+{
+    while(1){
+        detect_in_thread(0);
+    }
+}
+
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen)
+{
+    //demo_frame = avg_frames;
+    image **alphabet = load_alphabet();
+    demo_names = names;
+    demo_alphabet = alphabet;
+    demo_classes = classes;
+    demo_thresh = thresh;
+    demo_hier = hier;
+    printf("Demo\n");
+    net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    pthread_t detect_thread;
+    pthread_t fetch_thread;
+
+    srand(2222222);
+
+    int i;
+    demo_total = size_network(net);
+    predictions = calloc(demo_frame, sizeof(float*));
+    for (i = 0; i < demo_frame; ++i){
+        predictions[i] = calloc(demo_total, sizeof(float));
+    }
+    avg = calloc(demo_total, sizeof(float));
+
+    if(filename){
+        printf("video file: %s\n", filename);
+        cap = open_video_stream(filename, 0, 0, 0, 0);
+    }else{
+        cap = open_video_stream(0, cam_index, w, h, frames);
+    }
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+
+    buff[0] = get_image_from_stream(cap);
+    buff[1] = copy_image(buff[0]);
+    buff[2] = copy_image(buff[0]);
+    buff_letter[0] = letterbox_image(buff[0], net->w, net->h);
+    buff_letter[1] = letterbox_image(buff[0], net->w, net->h);
+    buff_letter[2] = letterbox_image(buff[0], net->w, net->h);
+
+    int count = 0;
+    if(!prefix){
+        make_window("Demo", 1352, 1013, fullscreen);
+    }
+
+    demo_time = what_time_is_it_now();
+
+    while(!demo_done){
+        buff_index = (buff_index + 1) %3;
+        if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
+        if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
+        if(!prefix){
+            fps = 1./(what_time_is_it_now() - demo_time);
+            demo_time = what_time_is_it_now();
+            display_in_thread(0);
+        }else{
+            char name[256];
+            sprintf(name, "%s_%08d", prefix, count);
+            save_image(buff[(buff_index + 1)%3], name);
+        }
+        pthread_join(fetch_thread, 0);
+        pthread_join(detect_thread, 0);
+        ++count;
+    }
+}
+
+/*
+   void demo_compare(char *cfg1, char *weight1, char *cfg2, char *weight2, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen)
+   {
+   demo_frame = avg_frames;
+   predictions = calloc(demo_frame, sizeof(float*));
+   image **alphabet = load_alphabet();
+   demo_names = names;
+   demo_alphabet = alphabet;
+   demo_classes = classes;
+   demo_thresh = thresh;
+   demo_hier = hier;
+   printf("Demo\n");
+   net = load_network(cfg1, weight1, 0);
+   set_batch_network(net, 1);
+   pthread_t detect_thread;
+   pthread_t fetch_thread;
+
+   srand(2222222);
+
+   if(filename){
+   printf("video file: %s\n", filename);
+   cap = cvCaptureFromFile(filename);
+   }else{
+   cap = cvCaptureFromCAM(cam_index);
+
+   if(w){
+   cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
+   }
+   if(h){
+   cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
+   }
+   if(frames){
+   cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames);
+   }
+   }
+
+   if(!cap) error("Couldn't connect to webcam.\n");
+
+   layer l = net->layers[net->n-1];
+   demo_detections = l.n*l.w*l.h;
+   int j;
+
+   avg = (float *) calloc(l.outputs, sizeof(float));
+   for(j = 0; j < demo_frame; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
+
+   boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
+   probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
+   for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes+1, sizeof(float));
+
+   buff[0] = get_image_from_stream(cap);
+   buff[1] = copy_image(buff[0]);
+   buff[2] = copy_image(buff[0]);
+   buff_letter[0] = letterbox_image(buff[0], net->w, net->h);
+   buff_letter[1] = letterbox_image(buff[0], net->w, net->h);
+   buff_letter[2] = letterbox_image(buff[0], net->w, net->h);
+   ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c);
+
+   int count = 0;
+   if(!prefix){
+   cvNamedWindow("Demo", CV_WINDOW_NORMAL); 
+   if(fullscreen){
+   cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
+   } else {
+   cvMoveWindow("Demo", 0, 0);
+   cvResizeWindow("Demo", 1352, 1013);
+   }
+   }
+
+   demo_time = what_time_is_it_now();
+
+   while(!demo_done){
+buff_index = (buff_index + 1) %3;
+if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
+if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
+if(!prefix){
+    fps = 1./(what_time_is_it_now() - demo_time);
+    demo_time = what_time_is_it_now();
+    display_in_thread(0);
+}else{
+    char name[256];
+    sprintf(name, "%s_%08d", prefix, count);
+    save_image(buff[(buff_index + 1)%3], name);
+}
+pthread_join(fetch_thread, 0);
+pthread_join(detect_thread, 0);
+++count;
+}
+}
+*/
+#else
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg, float hier, int w, int h, int frames, int fullscreen)
+{
+    fprintf(stderr, "Demo needs OpenCV for webcam images.\n");
+}
+#endif
+
diff --git a/hanzi_detection/src/demo.h b/hanzi_detection/src/demo.h
new file mode 100755
index 0000000..86e4654
--- /dev/null
+++ b/hanzi_detection/src/demo.h
@@ -0,0 +1,6 @@
+#ifndef DEMO_H
+#define DEMO_H
+
+#include "image.h"
+
+#endif
diff --git a/hanzi_detection/src/detection_layer.c b/hanzi_detection/src/detection_layer.c
new file mode 100755
index 0000000..d0e0194
--- /dev/null
+++ b/hanzi_detection/src/detection_layer.c
@@ -0,0 +1,275 @@
+#include "detection_layer.h"
+#include "activations.h"
+#include "softmax_layer.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore)
+{
+    detection_layer l = {0};
+    l.type = DETECTION;
+
+    l.n = n;
+    l.batch = batch;
+    l.inputs = inputs;
+    l.classes = classes;
+    l.coords = coords;
+    l.rescore = rescore;
+    l.side = side;
+    l.w = side;
+    l.h = side;
+    assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
+    l.cost = calloc(1, sizeof(float));
+    l.outputs = l.inputs;
+    l.truths = l.side*l.side*(1+l.coords+l.classes);
+    l.output = calloc(batch*l.outputs, sizeof(float));
+    l.delta = calloc(batch*l.outputs, sizeof(float));
+
+    l.forward = forward_detection_layer;
+    l.backward = backward_detection_layer;
+#ifdef GPU
+    l.forward_gpu = forward_detection_layer_gpu;
+    l.backward_gpu = backward_detection_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+    fprintf(stderr, "Detection Layer\n");
+    srand(0);
+
+    return l;
+}
+
+void forward_detection_layer(const detection_layer l, network net)
+{
+    int locations = l.side*l.side;
+    int i,j;
+    memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+    //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
+    int b;
+    if (l.softmax){
+        for(b = 0; b < l.batch; ++b){
+            int index = b*l.inputs;
+            for (i = 0; i < locations; ++i) {
+                int offset = i*l.classes;
+                softmax(l.output + index + offset, l.classes, 1, 1,
+                        l.output + index + offset);
+            }
+        }
+    }
+    if(net.train){
+        float avg_iou = 0;
+        float avg_cat = 0;
+        float avg_allcat = 0;
+        float avg_obj = 0;
+        float avg_anyobj = 0;
+        int count = 0;
+        *(l.cost) = 0;
+        int size = l.inputs * l.batch;
+        memset(l.delta, 0, size * sizeof(float));
+        for (b = 0; b < l.batch; ++b){
+            int index = b*l.inputs;
+            for (i = 0; i < locations; ++i) {
+                int truth_index = (b*locations + i)*(1+l.coords+l.classes);
+                int is_obj = net.truth[truth_index];
+                for (j = 0; j < l.n; ++j) {
+                    int p_index = index + locations*l.classes + i*l.n + j;
+                    l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
+                    *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
+                    avg_anyobj += l.output[p_index];
+                }
+
+                int best_index = -1;
+                float best_iou = 0;
+                float best_rmse = 20;
+
+                if (!is_obj){
+                    continue;
+                }
+
+                int class_index = index + i*l.classes;
+                for(j = 0; j < l.classes; ++j) {
+                    l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]);
+                    *(l.cost) += l.class_scale * pow(net.truth[truth_index+1+j] - l.output[class_index+j], 2);
+                    if(net.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
+                    avg_allcat += l.output[class_index+j];
+                }
+
+                box truth = float_to_box(net.truth + truth_index + 1 + l.classes, 1);
+                truth.x /= l.side;
+                truth.y /= l.side;
+
+                for(j = 0; j < l.n; ++j){
+                    int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;
+                    box out = float_to_box(l.output + box_index, 1);
+                    out.x /= l.side;
+                    out.y /= l.side;
+
+                    if (l.sqrt){
+                        out.w = out.w*out.w;
+                        out.h = out.h*out.h;
+                    }
+
+                    float iou  = box_iou(out, truth);
+                    //iou = 0;
+                    float rmse = box_rmse(out, truth);
+                    if(best_iou > 0 || iou > 0){
+                        if(iou > best_iou){
+                            best_iou = iou;
+                            best_index = j;
+                        }
+                    }else{
+                        if(rmse < best_rmse){
+                            best_rmse = rmse;
+                            best_index = j;
+                        }
+                    }
+                }
+
+                if(l.forced){
+                    if(truth.w*truth.h < .1){
+                        best_index = 1;
+                    }else{
+                        best_index = 0;
+                    }
+                }
+                if(l.random && *(net.seen) < 64000){
+                    best_index = rand()%l.n;
+                }
+
+                int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
+                int tbox_index = truth_index + 1 + l.classes;
+
+                box out = float_to_box(l.output + box_index, 1);
+                out.x /= l.side;
+                out.y /= l.side;
+                if (l.sqrt) {
+                    out.w = out.w*out.w;
+                    out.h = out.h*out.h;
+                }
+                float iou  = box_iou(out, truth);
+
+                //printf("%d,", best_index);
+                int p_index = index + locations*l.classes + i*l.n + best_index;
+                *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
+                *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
+                avg_obj += l.output[p_index];
+                l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);
+
+                if(l.rescore){
+                    l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
+                }
+
+                l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]);
+                l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]);
+                l.delta[box_index+2] = l.coord_scale*(net.truth[tbox_index + 2] - l.output[box_index + 2]);
+                l.delta[box_index+3] = l.coord_scale*(net.truth[tbox_index + 3] - l.output[box_index + 3]);
+                if(l.sqrt){
+                    l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]);
+                    l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]);
+                }
+
+                *(l.cost) += pow(1-iou, 2);
+                avg_iou += iou;
+                ++count;
+            }
+        }
+
+        if(0){
+            float *costs = calloc(l.batch*locations*l.n, sizeof(float));
+            for (b = 0; b < l.batch; ++b) {
+                int index = b*l.inputs;
+                for (i = 0; i < locations; ++i) {
+                    for (j = 0; j < l.n; ++j) {
+                        int p_index = index + locations*l.classes + i*l.n + j;
+                        costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];
+                    }
+                }
+            }
+            int indexes[100];
+            top_k(costs, l.batch*locations*l.n, 100, indexes);
+            float cutoff = costs[indexes[99]];
+            for (b = 0; b < l.batch; ++b) {
+                int index = b*l.inputs;
+                for (i = 0; i < locations; ++i) {
+                    for (j = 0; j < l.n; ++j) {
+                        int p_index = index + locations*l.classes + i*l.n + j;
+                        if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;
+                    }
+                }
+            }
+            free(costs);
+        }
+
+
+        *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+
+
+        printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
+        //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
+    }
+}
+
+void backward_detection_layer(const detection_layer l, network net)
+{
+    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
+}
+
+void get_detection_detections(layer l, int w, int h, float thresh, detection *dets)
+{
+    int i,j,n;
+    float *predictions = l.output;
+    //int per_cell = 5*num+classes;
+    for (i = 0; i < l.side*l.side; ++i){
+        int row = i / l.side;
+        int col = i % l.side;
+        for(n = 0; n < l.n; ++n){
+            int index = i*l.n + n;
+            int p_index = l.side*l.side*l.classes + i*l.n + n;
+            float scale = predictions[p_index];
+            int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
+            box b;
+            b.x = (predictions[box_index + 0] + col) / l.side * w;
+            b.y = (predictions[box_index + 1] + row) / l.side * h;
+            b.w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
+            b.h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
+            dets[index].bbox = b;
+            dets[index].objectness = scale;
+            for(j = 0; j < l.classes; ++j){
+                int class_index = i*l.classes;
+                float prob = scale*predictions[class_index+j];
+                dets[index].prob[j] = (prob > thresh) ? prob : 0;
+            }
+        }
+    }
+}
+
+#ifdef GPU
+
+void forward_detection_layer_gpu(const detection_layer l, network net)
+{
+    if(!net.train){
+        copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
+        return;
+    }
+
+    cuda_pull_array(net.input_gpu, net.input, l.batch*l.inputs);
+    forward_detection_layer(l, net);
+    cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+void backward_detection_layer_gpu(detection_layer l, network net)
+{
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+    //copy_gpu(l.batch*l.inputs, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+
diff --git a/hanzi_detection/src/detection_layer.h b/hanzi_detection/src/detection_layer.h
new file mode 100755
index 0000000..1c81853
--- /dev/null
+++ b/hanzi_detection/src/detection_layer.h
@@ -0,0 +1,18 @@
+#ifndef DETECTION_LAYER_H
+#define DETECTION_LAYER_H
+
+#include "layer.h"
+#include "network.h"
+
+typedef layer detection_layer;
+
+detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore);
+void forward_detection_layer(const detection_layer l, network net);
+void backward_detection_layer(const detection_layer l, network net);
+
+#ifdef GPU
+void forward_detection_layer_gpu(const detection_layer l, network net);
+void backward_detection_layer_gpu(detection_layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/dropout_layer.c b/hanzi_detection/src/dropout_layer.c
new file mode 100755
index 0000000..780554f
--- /dev/null
+++ b/hanzi_detection/src/dropout_layer.c
@@ -0,0 +1,60 @@
+#include "dropout_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include <stdlib.h>
+#include <stdio.h>
+
+dropout_layer make_dropout_layer(int batch, int inputs, float probability)
+{
+    dropout_layer l = {0};
+    l.type = DROPOUT;
+    l.probability = probability;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.batch = batch;
+    l.rand = calloc(inputs*batch, sizeof(float));
+    l.scale = 1./(1.-probability);
+    l.forward = forward_dropout_layer;
+    l.backward = backward_dropout_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_dropout_layer_gpu;
+    l.backward_gpu = backward_dropout_layer_gpu;
+    l.rand_gpu = cuda_make_array(l.rand, inputs*batch);
+    #endif
+    fprintf(stderr, "dropout       p = %.2f               %4d  ->  %4d\n", probability, inputs, inputs);
+    return l;
+} 
+
+void resize_dropout_layer(dropout_layer *l, int inputs)
+{
+    l->rand = realloc(l->rand, l->inputs*l->batch*sizeof(float));
+    #ifdef GPU
+    cuda_free(l->rand_gpu);
+
+    l->rand_gpu = cuda_make_array(l->rand, inputs*l->batch);
+    #endif
+}
+
+void forward_dropout_layer(dropout_layer l, network net)
+{
+    int i;
+    if (!net.train) return;
+    for(i = 0; i < l.batch * l.inputs; ++i){
+        float r = rand_uniform(0, 1);
+        l.rand[i] = r;
+        if(r < l.probability) net.input[i] = 0;
+        else net.input[i] *= l.scale;
+    }
+}
+
+void backward_dropout_layer(dropout_layer l, network net)
+{
+    int i;
+    if(!net.delta) return;
+    for(i = 0; i < l.batch * l.inputs; ++i){
+        float r = l.rand[i];
+        if(r < l.probability) net.delta[i] = 0;
+        else net.delta[i] *= l.scale;
+    }
+}
+
diff --git a/hanzi_detection/src/dropout_layer.h b/hanzi_detection/src/dropout_layer.h
new file mode 100755
index 0000000..01f94d4
--- /dev/null
+++ b/hanzi_detection/src/dropout_layer.h
@@ -0,0 +1,20 @@
+#ifndef DROPOUT_LAYER_H
+#define DROPOUT_LAYER_H
+
+#include "layer.h"
+#include "network.h"
+
+typedef layer dropout_layer;
+
+dropout_layer make_dropout_layer(int batch, int inputs, float probability);
+
+void forward_dropout_layer(dropout_layer l, network net);
+void backward_dropout_layer(dropout_layer l, network net);
+void resize_dropout_layer(dropout_layer *l, int inputs);
+
+#ifdef GPU
+void forward_dropout_layer_gpu(dropout_layer l, network net);
+void backward_dropout_layer_gpu(dropout_layer l, network net);
+
+#endif
+#endif
diff --git a/hanzi_detection/src/dropout_layer_kernels.cu b/hanzi_detection/src/dropout_layer_kernels.cu
new file mode 100755
index 0000000..bd12b67
--- /dev/null
+++ b/hanzi_detection/src/dropout_layer_kernels.cu
@@ -0,0 +1,41 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "dropout_layer.h"
+#include "cuda.h"
+#include "utils.h"
+}
+
+__global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
+}
+
+void forward_dropout_layer_gpu(dropout_layer layer, network net)
+{
+    if (!net.train) return;
+    int size = layer.inputs*layer.batch;
+    cuda_random(layer.rand_gpu, size);
+    /*
+    int i;
+    for(i = 0; i < size; ++i){
+        layer.rand[i] = rand_uniform();
+    }
+    cuda_push_array(layer.rand_gpu, layer.rand, size);
+    */
+
+    yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(net.input_gpu, size, layer.rand_gpu, layer.probability, layer.scale);
+    check_error(cudaPeekAtLastError());
+}
+
+void backward_dropout_layer_gpu(dropout_layer layer, network net)
+{
+    if(!net.delta_gpu) return;
+    int size = layer.inputs*layer.batch;
+
+    yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(net.delta_gpu, size, layer.rand_gpu, layer.probability, layer.scale);
+    check_error(cudaPeekAtLastError());
+}
diff --git a/hanzi_detection/src/gemm.c b/hanzi_detection/src/gemm.c
new file mode 100755
index 0000000..648027f
--- /dev/null
+++ b/hanzi_detection/src/gemm.c
@@ -0,0 +1,324 @@
+#include "gemm.h"
+#include "utils.h"
+#include "cuda.h"
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+
+void gemm_bin(int M, int N, int K, float ALPHA, 
+        char  *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    for(i = 0; i < M; ++i){
+        for(k = 0; k < K; ++k){
+            char A_PART = A[i*lda+k];
+            if(A_PART){
+                for(j = 0; j < N; ++j){
+                    C[i*ldc+j] += B[k*ldb+j];
+                }
+            } else {
+                for(j = 0; j < N; ++j){
+                    C[i*ldc+j] -= B[k*ldb+j];
+                }
+            }
+        }
+    }
+}
+
+float *random_matrix(int rows, int cols)
+{
+    int i;
+    float *m = calloc(rows*cols, sizeof(float));
+    for(i = 0; i < rows*cols; ++i){
+        m[i] = (float)rand()/RAND_MAX;
+    }
+    return m;
+}
+
+void time_random_matrix(int TA, int TB, int m, int k, int n)
+{
+    float *a;
+    if(!TA) a = random_matrix(m,k);
+    else a = random_matrix(k,m);
+    int lda = (!TA)?k:m;
+    float *b;
+    if(!TB) b = random_matrix(k,n);
+    else b = random_matrix(n,k);
+    int ldb = (!TB)?n:k;
+
+    float *c = random_matrix(m,n);
+    int i;
+    clock_t start = clock(), end;
+    for(i = 0; i<10; ++i){
+        gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
+    }
+    end = clock();
+    printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC);
+    free(a);
+    free(b);
+    free(c);
+}
+
+
+void gemm(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float BETA,
+        float *C, int ldc)
+{
+    gemm_cpu( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
+}
+
+void gemm_nn(int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    #pragma omp parallel for
+    for(i = 0; i < M; ++i){
+        for(k = 0; k < K; ++k){
+            register float A_PART = ALPHA*A[i*lda+k];
+            for(j = 0; j < N; ++j){
+                C[i*ldc+j] += A_PART*B[k*ldb+j];
+            }
+        }
+    }
+}
+
+void gemm_nt(int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    #pragma omp parallel for
+    for(i = 0; i < M; ++i){
+        for(j = 0; j < N; ++j){
+            register float sum = 0;
+            for(k = 0; k < K; ++k){
+                sum += ALPHA*A[i*lda+k]*B[j*ldb + k];
+            }
+            C[i*ldc+j] += sum;
+        }
+    }
+}
+
+void gemm_tn(int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    #pragma omp parallel for
+    for(i = 0; i < M; ++i){
+        for(k = 0; k < K; ++k){
+            register float A_PART = ALPHA*A[k*lda+i];
+            for(j = 0; j < N; ++j){
+                C[i*ldc+j] += A_PART*B[k*ldb+j];
+            }
+        }
+    }
+}
+
+void gemm_tt(int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    #pragma omp parallel for
+    for(i = 0; i < M; ++i){
+        for(j = 0; j < N; ++j){
+            register float sum = 0;
+            for(k = 0; k < K; ++k){
+                sum += ALPHA*A[i+k*lda]*B[k+j*ldb];
+            }
+            C[i*ldc+j] += sum;
+        }
+    }
+}
+
+
+void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float BETA,
+        float *C, int ldc)
+{
+    //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc);
+    int i, j;
+    for(i = 0; i < M; ++i){
+        for(j = 0; j < N; ++j){
+            C[i*ldc + j] *= BETA;
+        }
+    }
+    if(!TA && !TB)
+        gemm_nn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
+    else if(TA && !TB)
+        gemm_tn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
+    else if(!TA && TB)
+        gemm_nt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
+    else
+        gemm_tt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc);
+}
+
+#ifdef GPU
+
+#include <math.h>
+
+void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A_gpu, int lda, 
+        float *B_gpu, int ldb,
+        float BETA,
+        float *C_gpu, int ldc)
+{
+    cublasHandle_t handle = blas_handle();
+    cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), 
+            (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc);
+    check_error(status);
+}
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <time.h>
+
+void time_gpu_random_matrix(int TA, int TB, int m, int k, int n)
+{
+    float *a;
+    if(!TA) a = random_matrix(m,k);
+    else a = random_matrix(k,m);
+    int lda = (!TA)?k:m;
+    float *b;
+    if(!TB) b = random_matrix(k,n);
+    else b = random_matrix(n,k);
+    int ldb = (!TB)?n:k;
+
+    float *c = random_matrix(m,n);
+    int i;
+    clock_t start = clock(), end;
+    for(i = 0; i<32; ++i){
+        gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
+    }
+    end = clock();
+    printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC);
+    free(a);
+    free(b);
+    free(c);
+}
+
+void time_gpu(int TA, int TB, int m, int k, int n)
+{
+    int iter = 10;
+    float *a = random_matrix(m,k);
+    float *b = random_matrix(k,n);
+
+    int lda = (!TA)?k:m;
+    int ldb = (!TB)?n:k;
+
+    float *c = random_matrix(m,n);
+
+    float *a_cl = cuda_make_array(a, m*k);
+    float *b_cl = cuda_make_array(b, k*n);
+    float *c_cl = cuda_make_array(c, m*n);
+
+    int i;
+    clock_t start = clock(), end;
+    for(i = 0; i<iter; ++i){
+        gemm_gpu(TA,TB,m,n,k,1,a_cl,lda,b_cl,ldb,1,c_cl,n);
+        cudaThreadSynchronize();
+    }
+    double flop = ((double)m)*n*(2.*k + 2.)*iter;
+    double gflop = flop/pow(10., 9);
+    end = clock();
+    double seconds = sec(end-start);
+    printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s, %lf GFLOPS\n",m,k,k,n, TA, TB, seconds, gflop/seconds);
+    cuda_free(a_cl);
+    cuda_free(b_cl);
+    cuda_free(c_cl);
+    free(a);
+    free(b);
+    free(c);
+}
+
+
+void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
+{
+    srand(0);
+    float *a;
+    if(!TA) a = random_matrix(m,k);
+    else a = random_matrix(k,m);
+    int lda = (!TA)?k:m;
+    float *b;
+    if(!TB) b = random_matrix(k,n);
+    else b = random_matrix(n,k);
+    int ldb = (!TB)?n:k;
+
+    float *c = random_matrix(m,n);
+    float *c_gpu = random_matrix(m,n);
+    memset(c, 0, m*n*sizeof(float));
+    memset(c_gpu, 0, m*n*sizeof(float));
+    int i;
+    //pm(m,k,b);
+    gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c_gpu,n);
+    //printf("GPU\n");
+    //pm(m, n, c_gpu);
+
+    gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
+    //printf("\n\nCPU\n");
+    //pm(m, n, c);
+    double sse = 0;
+    for(i = 0; i < m*n; ++i) {
+        //printf("%f %f\n", c[i], c_gpu[i]);
+        sse += pow(c[i]-c_gpu[i], 2);
+    }
+    printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %g SSE\n",m,k,k,n, TA, TB, sse/(m*n));
+    free(a);
+    free(b);
+    free(c);
+    free(c_gpu);
+}
+
+int test_gpu_blas()
+{
+    /*
+       test_gpu_accuracy(0,0,10,576,75); 
+
+       test_gpu_accuracy(0,0,17,10,10); 
+       test_gpu_accuracy(1,0,17,10,10); 
+       test_gpu_accuracy(0,1,17,10,10); 
+       test_gpu_accuracy(1,1,17,10,10); 
+
+       test_gpu_accuracy(0,0,1000,10,100); 
+       test_gpu_accuracy(1,0,1000,10,100); 
+       test_gpu_accuracy(0,1,1000,10,100); 
+       test_gpu_accuracy(1,1,1000,10,100); 
+
+       test_gpu_accuracy(0,0,10,10,10); 
+
+       time_gpu(0,0,64,2916,363); 
+       time_gpu(0,0,64,2916,363); 
+       time_gpu(0,0,64,2916,363); 
+       time_gpu(0,0,192,729,1600); 
+       time_gpu(0,0,384,196,1728); 
+       time_gpu(0,0,256,196,3456); 
+       time_gpu(0,0,256,196,2304); 
+       time_gpu(0,0,128,4096,12544); 
+       time_gpu(0,0,128,4096,4096); 
+     */
+    time_gpu(0,0,64,75,12544); 
+    time_gpu(0,0,64,75,12544); 
+    time_gpu(0,0,64,75,12544); 
+    time_gpu(0,0,64,576,12544); 
+    time_gpu(0,0,256,2304,784); 
+    time_gpu(1,1,2304,256,784); 
+    time_gpu(0,0,512,4608,196); 
+    time_gpu(1,1,4608,512,196); 
+
+    return 0;
+}
+#endif
+
diff --git a/hanzi_detection/src/gemm.h b/hanzi_detection/src/gemm.h
new file mode 100755
index 0000000..3ebb0eb
--- /dev/null
+++ b/hanzi_detection/src/gemm.h
@@ -0,0 +1,34 @@
+#ifndef GEMM_H
+#define GEMM_H
+
+void gemm_bin(int M, int N, int K, float ALPHA, 
+        char  *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc);
+        
+void gemm(int TA, int TB, int M, int N, int K, float ALPHA, 
+                    float *A, int lda, 
+                    float *B, int ldb,
+                    float BETA,
+                    float *C, int ldc);
+
+void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float BETA,
+        float *C, int ldc);
+
+#ifdef GPU
+void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A_gpu, int lda, 
+        float *B_gpu, int ldb,
+        float BETA,
+        float *C_gpu, int ldc);
+
+void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, 
+        float *A, int lda, 
+        float *B, int ldb,
+        float BETA,
+        float *C, int ldc);
+#endif
+#endif
diff --git a/hanzi_detection/src/gru_layer.c b/hanzi_detection/src/gru_layer.c
new file mode 100755
index 0000000..b6601d8
--- /dev/null
+++ b/hanzi_detection/src/gru_layer.c
@@ -0,0 +1,406 @@
+#include "gru_layer.h"
+#include "connected_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+static void increment_layer(layer *l, int steps)
+{
+    int num = l->outputs*l->batch*steps;
+    l->output += num;
+    l->delta += num;
+    l->x += num;
+    l->x_norm += num;
+
+#ifdef GPU
+    l->output_gpu += num;
+    l->delta_gpu += num;
+    l->x_gpu += num;
+    l->x_norm_gpu += num;
+#endif
+}
+
+layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam)
+{
+    fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs);
+    batch = batch / steps;
+    layer l = {0};
+    l.batch = batch;
+    l.type = GRU;
+    l.steps = steps;
+    l.inputs = inputs;
+
+    l.uz = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.uz) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.uz->batch = batch;
+
+    l.wz = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wz) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wz->batch = batch;
+
+    l.ur = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.ur) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.ur->batch = batch;
+
+    l.wr = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wr) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wr->batch = batch;
+
+
+
+    l.uh = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.uh) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.uh->batch = batch;
+
+    l.wh = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wh) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wh->batch = batch;
+
+    l.batch_normalize = batch_normalize;
+
+
+    l.outputs = outputs;
+    l.output = calloc(outputs*batch*steps, sizeof(float));
+    l.delta = calloc(outputs*batch*steps, sizeof(float));
+    l.state = calloc(outputs*batch, sizeof(float));
+    l.prev_state = calloc(outputs*batch, sizeof(float));
+    l.forgot_state = calloc(outputs*batch, sizeof(float));
+    l.forgot_delta = calloc(outputs*batch, sizeof(float));
+
+    l.r_cpu = calloc(outputs*batch, sizeof(float));
+    l.z_cpu = calloc(outputs*batch, sizeof(float));
+    l.h_cpu = calloc(outputs*batch, sizeof(float));
+
+    l.forward = forward_gru_layer;
+    l.backward = backward_gru_layer;
+    l.update = update_gru_layer;
+
+#ifdef GPU
+    l.forward_gpu = forward_gru_layer_gpu;
+    l.backward_gpu = backward_gru_layer_gpu;
+    l.update_gpu = update_gru_layer_gpu;
+
+    l.forgot_state_gpu = cuda_make_array(0, batch*outputs);
+    l.forgot_delta_gpu = cuda_make_array(0, batch*outputs);
+    l.prev_state_gpu = cuda_make_array(0, batch*outputs);
+    l.state_gpu = cuda_make_array(0, batch*outputs);
+    l.output_gpu = cuda_make_array(0, batch*outputs*steps);
+    l.delta_gpu = cuda_make_array(0, batch*outputs*steps);
+    l.r_gpu = cuda_make_array(0, batch*outputs);
+    l.z_gpu = cuda_make_array(0, batch*outputs);
+    l.h_gpu = cuda_make_array(0, batch*outputs);
+
+#ifdef CUDNN
+    cudnnSetTensor4dDescriptor(l.uz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uz->out_c, l.uz->out_h, l.uz->out_w); 
+    cudnnSetTensor4dDescriptor(l.uh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uh->out_c, l.uh->out_h, l.uh->out_w); 
+    cudnnSetTensor4dDescriptor(l.ur->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ur->out_c, l.ur->out_h, l.ur->out_w); 
+    cudnnSetTensor4dDescriptor(l.wz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wz->out_c, l.wz->out_h, l.wz->out_w); 
+    cudnnSetTensor4dDescriptor(l.wh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wh->out_c, l.wh->out_h, l.wh->out_w); 
+    cudnnSetTensor4dDescriptor(l.wr->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wr->out_c, l.wr->out_h, l.wr->out_w); 
+#endif
+#endif
+
+    return l;
+}
+
+void update_gru_layer(layer l, update_args a)
+{
+    update_connected_layer(*(l.ur), a);
+    update_connected_layer(*(l.uz), a);
+    update_connected_layer(*(l.uh), a);
+    update_connected_layer(*(l.wr), a);
+    update_connected_layer(*(l.wz), a);
+    update_connected_layer(*(l.wh), a);
+}
+
+void forward_gru_layer(layer l, network net)
+{
+    network s = net;
+    s.train = net.train;
+    int i;
+    layer uz = *(l.uz);
+    layer ur = *(l.ur);
+    layer uh = *(l.uh);
+
+    layer wz = *(l.wz);
+    layer wr = *(l.wr);
+    layer wh = *(l.wh);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, uz.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, ur.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, uh.delta, 1);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wz.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wr.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wh.delta, 1);
+    if(net.train) {
+        fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
+        copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1);
+    }
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input = l.state;
+        forward_connected_layer(wz, s);
+        forward_connected_layer(wr, s);
+
+        s.input = net.input;
+        forward_connected_layer(uz, s);
+        forward_connected_layer(ur, s);
+        forward_connected_layer(uh, s);
+
+
+        copy_cpu(l.outputs*l.batch, uz.output, 1, l.z_cpu, 1);
+        axpy_cpu(l.outputs*l.batch, 1, wz.output, 1, l.z_cpu, 1);
+
+        copy_cpu(l.outputs*l.batch, ur.output, 1, l.r_cpu, 1);
+        axpy_cpu(l.outputs*l.batch, 1, wr.output, 1, l.r_cpu, 1);
+
+        activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC);
+        activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC);
+
+        copy_cpu(l.outputs*l.batch, l.state, 1, l.forgot_state, 1);
+        mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1);
+
+        s.input = l.forgot_state;
+        forward_connected_layer(wh, s);
+
+        copy_cpu(l.outputs*l.batch, uh.output, 1, l.h_cpu, 1);
+        axpy_cpu(l.outputs*l.batch, 1, wh.output, 1, l.h_cpu, 1);
+
+        if(l.tanh){
+            activate_array(l.h_cpu, l.outputs*l.batch, TANH);
+        } else {
+            activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC);
+        }
+
+        weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output);
+
+        copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1);
+
+        net.input += l.inputs*l.batch;
+        l.output += l.outputs*l.batch;
+        increment_layer(&uz, 1);
+        increment_layer(&ur, 1);
+        increment_layer(&uh, 1);
+
+        increment_layer(&wz, 1);
+        increment_layer(&wr, 1);
+        increment_layer(&wh, 1);
+    }
+}
+
+void backward_gru_layer(layer l, network net)
+{
+}
+
+#ifdef GPU
+
+void pull_gru_layer(layer l)
+{
+}
+
+void push_gru_layer(layer l)
+{
+}
+
+void update_gru_layer_gpu(layer l, update_args a)
+{
+    update_connected_layer_gpu(*(l.ur), a);
+    update_connected_layer_gpu(*(l.uz), a);
+    update_connected_layer_gpu(*(l.uh), a);
+    update_connected_layer_gpu(*(l.wr), a);
+    update_connected_layer_gpu(*(l.wz), a);
+    update_connected_layer_gpu(*(l.wh), a);
+}
+
+void forward_gru_layer_gpu(layer l, network net)
+{
+    network s = {0};
+    s.train = net.train;
+    int i;
+    layer uz = *(l.uz);
+    layer ur = *(l.ur);
+    layer uh = *(l.uh);
+
+    layer wz = *(l.wz);
+    layer wr = *(l.wr);
+    layer wh = *(l.wh);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, uz.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, ur.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, uh.delta_gpu, 1);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wz.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wr.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wh.delta_gpu, 1);
+    if(net.train) {
+        fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
+        copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1);
+    }
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input_gpu = l.state_gpu;
+        forward_connected_layer_gpu(wz, s);
+        forward_connected_layer_gpu(wr, s);
+
+        s.input_gpu = net.input_gpu;
+        forward_connected_layer_gpu(uz, s);
+        forward_connected_layer_gpu(ur, s);
+        forward_connected_layer_gpu(uh, s);
+
+        copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1);
+
+        copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1);
+
+        activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
+        activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
+
+        copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1);
+        mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1);
+
+        s.input_gpu = l.forgot_state_gpu;
+        forward_connected_layer_gpu(wh, s);
+
+        copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1);
+
+        if(l.tanh){
+            activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH);
+        } else {
+            activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
+        }
+
+        weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu);
+        copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1);
+
+        net.input_gpu += l.inputs*l.batch;
+        l.output_gpu += l.outputs*l.batch;
+        increment_layer(&uz, 1);
+        increment_layer(&ur, 1);
+        increment_layer(&uh, 1);
+
+        increment_layer(&wz, 1);
+        increment_layer(&wr, 1);
+        increment_layer(&wh, 1);
+    }
+}
+
+void backward_gru_layer_gpu(layer l, network net)
+{
+    network s = {0};
+    s.train = net.train;
+    int i;
+    layer uz = *(l.uz);
+    layer ur = *(l.ur);
+    layer uh = *(l.uh);
+
+    layer wz = *(l.wz);
+    layer wr = *(l.wr);
+    layer wh = *(l.wh);
+
+    increment_layer(&uz, l.steps - 1);
+    increment_layer(&ur, l.steps - 1);
+    increment_layer(&uh, l.steps - 1);
+
+    increment_layer(&wz, l.steps - 1);
+    increment_layer(&wr, l.steps - 1);
+    increment_layer(&wh, l.steps - 1);
+
+    net.input_gpu += l.inputs*l.batch*(l.steps-1);
+    if(net.delta_gpu) net.delta_gpu += l.inputs*l.batch*(l.steps-1);
+    l.output_gpu += l.outputs*l.batch*(l.steps-1);
+    l.delta_gpu += l.outputs*l.batch*(l.steps-1);
+    float *end_state = l.output_gpu;
+    for (i = l.steps-1; i >= 0; --i) {
+        if(i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1);
+        else copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1);
+        float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
+
+        copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1);
+
+        copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1);
+
+        activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
+        activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
+
+        copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1);
+
+        if(l.tanh){
+            activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH);
+        } else {
+            activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
+        }
+
+        weighted_delta_gpu(l.state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, uh.delta_gpu, uz.delta_gpu, l.outputs*l.batch, l.delta_gpu);
+
+        if(l.tanh){
+            gradient_array_gpu(l.h_gpu, l.outputs*l.batch, TANH, uh.delta_gpu);
+        } else {
+            gradient_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, uh.delta_gpu);
+        }
+
+        copy_gpu(l.outputs*l.batch, uh.delta_gpu, 1, wh.delta_gpu, 1);
+
+        copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1);
+        mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1);
+        fill_gpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1);
+
+        s.input_gpu = l.forgot_state_gpu;
+        s.delta_gpu = l.forgot_delta_gpu;
+
+        backward_connected_layer_gpu(wh, s);
+        if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu);
+        mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.state_gpu, ur.delta_gpu);
+
+        gradient_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, ur.delta_gpu);
+        copy_gpu(l.outputs*l.batch, ur.delta_gpu, 1, wr.delta_gpu, 1);
+
+        gradient_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, uz.delta_gpu);
+        copy_gpu(l.outputs*l.batch, uz.delta_gpu, 1, wz.delta_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        s.delta_gpu = prev_delta_gpu;
+
+        backward_connected_layer_gpu(wr, s);
+        backward_connected_layer_gpu(wz, s);
+
+        s.input_gpu = net.input_gpu;
+        s.delta_gpu = net.delta_gpu;
+
+        backward_connected_layer_gpu(uh, s);
+        backward_connected_layer_gpu(ur, s);
+        backward_connected_layer_gpu(uz, s);
+
+
+        net.input_gpu -= l.inputs*l.batch;
+        if(net.delta_gpu) net.delta_gpu -= l.inputs*l.batch;
+        l.output_gpu -= l.outputs*l.batch;
+        l.delta_gpu -= l.outputs*l.batch;
+        increment_layer(&uz, -1);
+        increment_layer(&ur, -1);
+        increment_layer(&uh, -1);
+
+        increment_layer(&wz, -1);
+        increment_layer(&wr, -1);
+        increment_layer(&wh, -1);
+    }
+    copy_gpu(l.outputs*l.batch, end_state, 1, l.state_gpu, 1);
+}
+#endif
diff --git a/hanzi_detection/src/gru_layer.h b/hanzi_detection/src/gru_layer.h
new file mode 100755
index 0000000..9067942
--- /dev/null
+++ b/hanzi_detection/src/gru_layer.h
@@ -0,0 +1,24 @@
+
+#ifndef GRU_LAYER_H
+#define GRU_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
+
+void forward_gru_layer(layer l, network state);
+void backward_gru_layer(layer l, network state);
+void update_gru_layer(layer l, update_args a);
+
+#ifdef GPU
+void forward_gru_layer_gpu(layer l, network state);
+void backward_gru_layer_gpu(layer l, network state);
+void update_gru_layer_gpu(layer l, update_args a);
+void push_gru_layer(layer l);
+void pull_gru_layer(layer l);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/im2col.c b/hanzi_detection/src/im2col.c
new file mode 100755
index 0000000..69ec98a
--- /dev/null
+++ b/hanzi_detection/src/im2col.c
@@ -0,0 +1,40 @@
+#include "im2col.h"
+#include <stdio.h>
+float im2col_get_pixel(float *im, int height, int width, int channels,
+                        int row, int col, int channel, int pad)
+{
+    row -= pad;
+    col -= pad;
+
+    if (row < 0 || col < 0 ||
+        row >= height || col >= width) return 0;
+    return im[col + width*(row + height*channel)];
+}
+
+//From Berkeley Vision's Caffe!
+//https://github.com/BVLC/caffe/blob/master/LICENSE
+void im2col_cpu(float* data_im,
+     int channels,  int height,  int width,
+     int ksize,  int stride, int pad, float* data_col) 
+{
+    int c,h,w;
+    int height_col = (height + 2*pad - ksize) / stride + 1;
+    int width_col = (width + 2*pad - ksize) / stride + 1;
+
+    int channels_col = channels * ksize * ksize;
+    for (c = 0; c < channels_col; ++c) {
+        int w_offset = c % ksize;
+        int h_offset = (c / ksize) % ksize;
+        int c_im = c / ksize / ksize;
+        for (h = 0; h < height_col; ++h) {
+            for (w = 0; w < width_col; ++w) {
+                int im_row = h_offset + h * stride;
+                int im_col = w_offset + w * stride;
+                int col_index = (c * height_col + h) * width_col + w;
+                data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
+                        im_row, im_col, c_im, pad);
+            }
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/im2col.h b/hanzi_detection/src/im2col.h
new file mode 100755
index 0000000..02c4247
--- /dev/null
+++ b/hanzi_detection/src/im2col.h
@@ -0,0 +1,15 @@
+#ifndef IM2COL_H
+#define IM2COL_H
+
+void im2col_cpu(float* data_im,
+        int channels, int height, int width,
+        int ksize, int stride, int pad, float* data_col);
+
+#ifdef GPU
+
+void im2col_gpu(float *im,
+         int channels, int height, int width,
+         int ksize, int stride, int pad,float *data_col);
+
+#endif
+#endif
diff --git a/hanzi_detection/src/im2col_kernels.cu b/hanzi_detection/src/im2col_kernels.cu
new file mode 100755
index 0000000..07b5e67
--- /dev/null
+++ b/hanzi_detection/src/im2col_kernels.cu
@@ -0,0 +1,61 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "im2col.h"
+#include "cuda.h"
+}
+
+// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu
+// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE
+
+__global__ void im2col_gpu_kernel(const int n, const float* data_im,
+        const int height, const int width, const int ksize,
+        const int pad,
+        const int stride,
+        const int height_col, const int width_col,
+        float *data_col) {
+    int index = blockIdx.x*blockDim.x+threadIdx.x;
+    for(; index < n; index += blockDim.x*gridDim.x){
+        int w_out = index % width_col;
+        int h_index = index / width_col;
+        int h_out = h_index % height_col;
+        int channel_in = h_index / height_col;
+        int channel_out = channel_in * ksize * ksize;
+        int h_in = h_out * stride - pad;
+        int w_in = w_out * stride - pad;
+        float* data_col_ptr = data_col;
+        data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+        const float* data_im_ptr = data_im;
+        data_im_ptr += (channel_in * height + h_in) * width + w_in;
+        for (int i = 0; i < ksize; ++i) {
+            for (int j = 0; j < ksize; ++j) {
+                int h = h_in + i;
+                int w = w_in + j;
+
+                *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
+                    data_im_ptr[i * width + j] : 0;
+
+                //*data_col_ptr = data_im_ptr[ii * width + jj];
+
+                data_col_ptr += height_col * width_col;
+            }
+        }
+    }
+}
+
+void im2col_gpu(float *im,
+         int channels, int height, int width,
+         int ksize, int stride, int pad, float *data_col){
+    // We are going to launch channels * height_col * width_col kernels, each
+    // kernel responsible for copying a single-channel grid.
+    int height_col = (height + 2 * pad - ksize) / stride + 1;
+    int width_col = (width + 2 * pad - ksize) / stride + 1;
+    int num_kernels = channels * height_col * width_col;
+    im2col_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK,
+        BLOCK>>>(
+                num_kernels, im, height, width, ksize, pad,
+                stride, height_col,
+                width_col, data_col);
+}
diff --git a/hanzi_detection/src/image.c b/hanzi_detection/src/image.c
new file mode 100755
index 0000000..4a2c6ba
--- /dev/null
+++ b/hanzi_detection/src/image.c
@@ -0,0 +1,1466 @@
+#include "image.h"
+#include "utils.h"
+#include "blas.h"
+#include "cuda.h"
+#include <stdio.h>
+#include <math.h>
+
+#define STB_IMAGE_IMPLEMENTATION
+#include "stb_image.h"
+#define STB_IMAGE_WRITE_IMPLEMENTATION
+#include "stb_image_write.h"
+
+int windows = 0;
+
+float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
+
+float get_color(int c, int x, int max)
+{
+    float ratio = ((float)x/max)*5;
+    int i = floor(ratio);
+    int j = ceil(ratio);
+    ratio -= i;
+    float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
+    //printf("%f\n", r);
+    return r;
+}
+
+image mask_to_rgb(image mask)
+{
+    int n = mask.c;
+    image im = make_image(mask.w, mask.h, 3);
+    int i, j;
+    for(j = 0; j < n; ++j){
+        int offset = j*123457 % n;
+        float red = get_color(2,offset,n);
+        float green = get_color(1,offset,n);
+        float blue = get_color(0,offset,n);
+        for(i = 0; i < im.w*im.h; ++i){
+            im.data[i + 0*im.w*im.h] += mask.data[j*im.h*im.w + i]*red;
+            im.data[i + 1*im.w*im.h] += mask.data[j*im.h*im.w + i]*green;
+            im.data[i + 2*im.w*im.h] += mask.data[j*im.h*im.w + i]*blue;
+        }
+    }
+    return im;
+}
+
+static float get_pixel(image m, int x, int y, int c)
+{
+    assert(x < m.w && y < m.h && c < m.c);
+    return m.data[c*m.h*m.w + y*m.w + x];
+}
+static float get_pixel_extend(image m, int x, int y, int c)
+{
+    if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
+    /*
+    if(x < 0) x = 0;
+    if(x >= m.w) x = m.w-1;
+    if(y < 0) y = 0;
+    if(y >= m.h) y = m.h-1;
+    */
+    if(c < 0 || c >= m.c) return 0;
+    return get_pixel(m, x, y, c);
+}
+static void set_pixel(image m, int x, int y, int c, float val)
+{
+    if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
+    assert(x < m.w && y < m.h && c < m.c);
+    m.data[c*m.h*m.w + y*m.w + x] = val;
+}
+static void add_pixel(image m, int x, int y, int c, float val)
+{
+    assert(x < m.w && y < m.h && c < m.c);
+    m.data[c*m.h*m.w + y*m.w + x] += val;
+}
+
+static float bilinear_interpolate(image im, float x, float y, int c)
+{
+    int ix = (int) floorf(x);
+    int iy = (int) floorf(y);
+
+    float dx = x - ix;
+    float dy = y - iy;
+
+    float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + 
+        dy     * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + 
+        (1-dy) *   dx   * get_pixel_extend(im, ix+1, iy, c) +
+        dy     *   dx   * get_pixel_extend(im, ix+1, iy+1, c);
+    return val;
+}
+
+
+void composite_image(image source, image dest, int dx, int dy)
+{
+    int x,y,k;
+    for(k = 0; k < source.c; ++k){
+        for(y = 0; y < source.h; ++y){
+            for(x = 0; x < source.w; ++x){
+                float val = get_pixel(source, x, y, k);
+                float val2 = get_pixel_extend(dest, dx+x, dy+y, k);
+                set_pixel(dest, dx+x, dy+y, k, val * val2);
+            }
+        }
+    }
+}
+
+image border_image(image a, int border)
+{
+    image b = make_image(a.w + 2*border, a.h + 2*border, a.c);
+    int x,y,k;
+    for(k = 0; k < b.c; ++k){
+        for(y = 0; y < b.h; ++y){
+            for(x = 0; x < b.w; ++x){
+                float val = get_pixel_extend(a, x - border, y - border, k);
+                if(x - border < 0 || x - border >= a.w || y - border < 0 || y - border >= a.h) val = 1;
+                set_pixel(b, x, y, k, val);
+            }
+        }
+    }
+    return b;
+}
+
+image tile_images(image a, image b, int dx)
+{
+    if(a.w == 0) return copy_image(b);
+    image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c);
+    fill_cpu(c.w*c.h*c.c, 1, c.data, 1);
+    embed_image(a, c, 0, 0); 
+    composite_image(b, c, a.w + dx, 0);
+    return c;
+}
+
+image get_label(image **characters, char *string, int size)
+{
+    size = size/10;
+    if(size > 7) size = 7;
+    image label = make_empty_image(0,0,0);
+    while(*string){
+        image l = characters[size][(int)*string];
+        image n = tile_images(label, l, -size - 1 + (size+1)/2);
+        free_image(label);
+        label = n;
+        ++string;
+    }
+    image b = border_image(label, label.h*.25);
+    free_image(label);
+    return b;
+}
+
+void draw_label(image a, int r, int c, image label, const float *rgb)
+{
+    int w = label.w;
+    int h = label.h;
+    if (r - h >= 0) r = r - h;
+
+    int i, j, k;
+    for(j = 0; j < h && j + r < a.h; ++j){
+        for(i = 0; i < w && i + c < a.w; ++i){
+            for(k = 0; k < label.c; ++k){
+                float val = get_pixel(label, i, j, k);
+                set_pixel(a, i+c, j+r, k, rgb[k] * val);
+            }
+        }
+    }
+}
+
+void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
+{
+    //normalize_image(a);
+    int i;
+    if(x1 < 0) x1 = 0;
+    if(x1 >= a.w) x1 = a.w-1;
+    if(x2 < 0) x2 = 0;
+    if(x2 >= a.w) x2 = a.w-1;
+
+    if(y1 < 0) y1 = 0;
+    if(y1 >= a.h) y1 = a.h-1;
+    if(y2 < 0) y2 = 0;
+    if(y2 >= a.h) y2 = a.h-1;
+
+    for(i = x1; i <= x2; ++i){
+        a.data[i + y1*a.w + 0*a.w*a.h] = r;
+        a.data[i + y2*a.w + 0*a.w*a.h] = r;
+
+        a.data[i + y1*a.w + 1*a.w*a.h] = g;
+        a.data[i + y2*a.w + 1*a.w*a.h] = g;
+
+        a.data[i + y1*a.w + 2*a.w*a.h] = b;
+        a.data[i + y2*a.w + 2*a.w*a.h] = b;
+    }
+    for(i = y1; i <= y2; ++i){
+        a.data[x1 + i*a.w + 0*a.w*a.h] = r;
+        a.data[x2 + i*a.w + 0*a.w*a.h] = r;
+
+        a.data[x1 + i*a.w + 1*a.w*a.h] = g;
+        a.data[x2 + i*a.w + 1*a.w*a.h] = g;
+
+        a.data[x1 + i*a.w + 2*a.w*a.h] = b;
+        a.data[x2 + i*a.w + 2*a.w*a.h] = b;
+    }
+}
+
+void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b)
+{
+    int i;
+    for(i = 0; i < w; ++i){
+        draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b);
+    }
+}
+
+void draw_bbox(image a, box bbox, int w, float r, float g, float b)
+{
+    int left  = (bbox.x-bbox.w/2)*a.w;
+    int right = (bbox.x+bbox.w/2)*a.w;
+    int top   = (bbox.y-bbox.h/2)*a.h;
+    int bot   = (bbox.y+bbox.h/2)*a.h;
+
+    int i;
+    for(i = 0; i < w; ++i){
+        draw_box(a, left+i, top+i, right-i, bot-i, r, g, b);
+    }
+}
+
+image **load_alphabet()
+{
+    int i, j;
+    const int nsize = 8;
+    image **alphabets = calloc(nsize, sizeof(image));
+    for(j = 0; j < nsize; ++j){
+        alphabets[j] = calloc(128, sizeof(image));
+        for(i = 32; i < 127; ++i){
+            char buff[256];
+            sprintf(buff, "data/labels/%d_%d.png", i, j);
+            alphabets[j][i] = load_image_color(buff, 0, 0);
+        }
+    }
+    return alphabets;
+}
+
+void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes)
+{
+    int i,j;
+
+    for(i = 0; i < num; ++i){
+        char labelstr[4096] = {0};
+        int class = -1;
+        for(j = 0; j < classes; ++j){
+            if (dets[i].prob[j] > thresh){
+                if (class < 0) {
+                    strcat(labelstr, names[j]);
+                    class = j;
+                } else {
+                    strcat(labelstr, ", ");
+                    strcat(labelstr, names[j]);
+                }
+                printf("%s: %.0f%%\n", names[j], dets[i].prob[j]*100);
+            }
+        }
+        if(class >= 0){
+            int width = im.h * .006;
+
+            /*
+               if(0){
+               width = pow(prob, 1./2.)*10+1;
+               alphabet = 0;
+               }
+             */
+
+            //printf("%d %s: %.0f%%\n", i, names[class], prob*100);
+            int offset = class*123457 % classes;
+            float red = get_color(2,offset,classes);
+            float green = get_color(1,offset,classes);
+            float blue = get_color(0,offset,classes);
+            float rgb[3];
+
+            //width = prob*20+2;
+
+            rgb[0] = red;
+            rgb[1] = green;
+            rgb[2] = blue;
+            box b = dets[i].bbox;
+            //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
+
+            int left  = (b.x-b.w/2.)*im.w;
+            int right = (b.x+b.w/2.)*im.w;
+            int top   = (b.y-b.h/2.)*im.h;
+            int bot   = (b.y+b.h/2.)*im.h;
+
+            if(left < 0) left = 0;
+            if(right > im.w-1) right = im.w-1;
+            if(top < 0) top = 0;
+            if(bot > im.h-1) bot = im.h-1;
+
+            draw_box_width(im, left, top, right, bot, width, red, green, blue);
+            if (alphabet) {
+                image label = get_label(alphabet, labelstr, (im.h*.03));
+                draw_label(im, top + width, left, label, rgb);
+                free_image(label);
+            }
+            if (dets[i].mask){
+                image mask = float_to_image(14, 14, 1, dets[i].mask);
+                image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
+                image tmask = threshold_image(resized_mask, .5);
+                embed_image(tmask, im, left, top);
+                free_image(mask);
+                free_image(resized_mask);
+                free_image(tmask);
+            }
+        }
+    }
+}
+
+void transpose_image(image im)
+{
+    assert(im.w == im.h);
+    int n, m;
+    int c;
+    for(c = 0; c < im.c; ++c){
+        for(n = 0; n < im.w-1; ++n){
+            for(m = n + 1; m < im.w; ++m){
+                float swap = im.data[m + im.w*(n + im.h*c)];
+                im.data[m + im.w*(n + im.h*c)] = im.data[n + im.w*(m + im.h*c)];
+                im.data[n + im.w*(m + im.h*c)] = swap;
+            }
+        }
+    }
+}
+
+void rotate_image_cw(image im, int times)
+{
+    assert(im.w == im.h);
+    times = (times + 400) % 4;
+    int i, x, y, c;
+    int n = im.w;
+    for(i = 0; i < times; ++i){
+        for(c = 0; c < im.c; ++c){
+            for(x = 0; x < n/2; ++x){
+                for(y = 0; y < (n-1)/2 + 1; ++y){
+                    float temp = im.data[y + im.w*(x + im.h*c)];
+                    im.data[y + im.w*(x + im.h*c)] = im.data[n-1-x + im.w*(y + im.h*c)];
+                    im.data[n-1-x + im.w*(y + im.h*c)] = im.data[n-1-y + im.w*(n-1-x + im.h*c)];
+                    im.data[n-1-y + im.w*(n-1-x + im.h*c)] = im.data[x + im.w*(n-1-y + im.h*c)];
+                    im.data[x + im.w*(n-1-y + im.h*c)] = temp;
+                }
+            }
+        }
+    }
+}
+
+void flip_image(image a)
+{
+    int i,j,k;
+    for(k = 0; k < a.c; ++k){
+        for(i = 0; i < a.h; ++i){
+            for(j = 0; j < a.w/2; ++j){
+                int index = j + a.w*(i + a.h*(k));
+                int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
+                float swap = a.data[flip];
+                a.data[flip] = a.data[index];
+                a.data[index] = swap;
+            }
+        }
+    }
+}
+
+image image_distance(image a, image b)
+{
+    int i,j;
+    image dist = make_image(a.w, a.h, 1);
+    for(i = 0; i < a.c; ++i){
+        for(j = 0; j < a.h*a.w; ++j){
+            dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2);
+        }
+    }
+    for(j = 0; j < a.h*a.w; ++j){
+        dist.data[j] = sqrt(dist.data[j]);
+    }
+    return dist;
+}
+
+void ghost_image(image source, image dest, int dx, int dy)
+{
+    int x,y,k;
+    float max_dist = sqrt((-source.w/2. + .5)*(-source.w/2. + .5));
+    for(k = 0; k < source.c; ++k){
+        for(y = 0; y < source.h; ++y){
+            for(x = 0; x < source.w; ++x){
+                float dist = sqrt((x - source.w/2. + .5)*(x - source.w/2. + .5) + (y - source.h/2. + .5)*(y - source.h/2. + .5));
+                float alpha = (1 - dist/max_dist);
+                if(alpha < 0) alpha = 0;
+                float v1 = get_pixel(source, x,y,k);
+                float v2 = get_pixel(dest, dx+x,dy+y,k);
+                float val = alpha*v1 + (1-alpha)*v2;
+                set_pixel(dest, dx+x, dy+y, k, val);
+            }
+        }
+    }
+}
+
+void blocky_image(image im, int s)
+{
+    int i,j,k;
+    for(k = 0; k < im.c; ++k){
+        for(j = 0; j < im.h; ++j){
+            for(i = 0; i < im.w; ++i){
+                im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)];
+            }
+        }
+    }
+}
+
+void censor_image(image im, int dx, int dy, int w, int h)
+{
+    int i,j,k;
+    int s = 32;
+    if(dx < 0) dx = 0;
+    if(dy < 0) dy = 0;
+
+    for(k = 0; k < im.c; ++k){
+        for(j = dy; j < dy + h && j < im.h; ++j){
+            for(i = dx; i < dx + w && i < im.w; ++i){
+                im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)];
+                //im.data[i + j*im.w + k*im.w*im.h] = 0;
+            }
+        }
+    }
+}
+
+void embed_image(image source, image dest, int dx, int dy)
+{
+    int x,y,k;
+    for(k = 0; k < source.c; ++k){
+        for(y = 0; y < source.h; ++y){
+            for(x = 0; x < source.w; ++x){
+                float val = get_pixel(source, x,y,k);
+                set_pixel(dest, dx+x, dy+y, k, val);
+            }
+        }
+    }
+}
+
+image collapse_image_layers(image source, int border)
+{
+    int h = source.h;
+    h = (h+border)*source.c - border;
+    image dest = make_image(source.w, h, 1);
+    int i;
+    for(i = 0; i < source.c; ++i){
+        image layer = get_image_layer(source, i);
+        int h_offset = i*(source.h+border);
+        embed_image(layer, dest, 0, h_offset);
+        free_image(layer);
+    }
+    return dest;
+}
+
+void constrain_image(image im)
+{
+    int i;
+    for(i = 0; i < im.w*im.h*im.c; ++i){
+        if(im.data[i] < 0) im.data[i] = 0;
+        if(im.data[i] > 1) im.data[i] = 1;
+    }
+}
+
+void normalize_image(image p)
+{
+    int i;
+    float min = 9999999;
+    float max = -999999;
+
+    for(i = 0; i < p.h*p.w*p.c; ++i){
+        float v = p.data[i];
+        if(v < min) min = v;
+        if(v > max) max = v;
+    }
+    if(max - min < .000000001){
+        min = 0;
+        max = 1;
+    }
+    for(i = 0; i < p.c*p.w*p.h; ++i){
+        p.data[i] = (p.data[i] - min)/(max-min);
+    }
+}
+
+void normalize_image2(image p)
+{
+    float *min = calloc(p.c, sizeof(float));
+    float *max = calloc(p.c, sizeof(float));
+    int i,j;
+    for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w];
+
+    for(j = 0; j < p.c; ++j){
+        for(i = 0; i < p.h*p.w; ++i){
+            float v = p.data[i+j*p.h*p.w];
+            if(v < min[j]) min[j] = v;
+            if(v > max[j]) max[j] = v;
+        }
+    }
+    for(i = 0; i < p.c; ++i){
+        if(max[i] - min[i] < .000000001){
+            min[i] = 0;
+            max[i] = 1;
+        }
+    }
+    for(j = 0; j < p.c; ++j){
+        for(i = 0; i < p.w*p.h; ++i){
+            p.data[i+j*p.h*p.w] = (p.data[i+j*p.h*p.w] - min[j])/(max[j]-min[j]);
+        }
+    }
+    free(min);
+    free(max);
+}
+
+void copy_image_into(image src, image dest)
+{
+    memcpy(dest.data, src.data, src.h*src.w*src.c*sizeof(float));
+}
+
+image copy_image(image p)
+{
+    image copy = p;
+    copy.data = calloc(p.h*p.w*p.c, sizeof(float));
+    memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float));
+    return copy;
+}
+
+void rgbgr_image(image im)
+{
+    int i;
+    for(i = 0; i < im.w*im.h; ++i){
+        float swap = im.data[i];
+        im.data[i] = im.data[i+im.w*im.h*2];
+        im.data[i+im.w*im.h*2] = swap;
+    }
+}
+
+int show_image(image p, const char *name, int ms)
+{
+#ifdef OPENCV
+    int c = show_image_cv(p, name, ms);
+    return c;
+#else
+    fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name);
+    save_image(p, name);
+    return -1;
+#endif
+}
+
+void save_image_options(image im, const char *name, IMTYPE f, int quality)
+{
+    char buff[256];
+    //sprintf(buff, "%s (%d)", name, windows);
+    if(f == PNG)       sprintf(buff, "%s.png", name);
+    else if (f == BMP) sprintf(buff, "%s.bmp", name);
+    else if (f == TGA) sprintf(buff, "%s.tga", name);
+    else if (f == JPG) sprintf(buff, "%s.jpg", name);
+    else               sprintf(buff, "%s.png", name);
+    unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char));
+    int i,k;
+    for(k = 0; k < im.c; ++k){
+        for(i = 0; i < im.w*im.h; ++i){
+            data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]);
+        }
+    }
+    int success = 0;
+    if(f == PNG)       success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c);
+    else if (f == BMP) success = stbi_write_bmp(buff, im.w, im.h, im.c, data);
+    else if (f == TGA) success = stbi_write_tga(buff, im.w, im.h, im.c, data);
+    else if (f == JPG) success = stbi_write_jpg(buff, im.w, im.h, im.c, data, quality);
+    free(data);
+    if(!success) fprintf(stderr, "Failed to write image %s\n", buff);
+}
+
+void save_image(image im, const char *name)
+{
+    save_image_options(im, name, JPG, 80);
+}
+
+void show_image_layers(image p, char *name)
+{
+    int i;
+    char buff[256];
+    for(i = 0; i < p.c; ++i){
+        sprintf(buff, "%s - Layer %d", name, i);
+        image layer = get_image_layer(p, i);
+        show_image(layer, buff, 1);
+        free_image(layer);
+    }
+}
+
+void show_image_collapsed(image p, char *name)
+{
+    image c = collapse_image_layers(p, 1);
+    show_image(c, name, 1);
+    free_image(c);
+}
+
+image make_empty_image(int w, int h, int c)
+{
+    image out;
+    out.data = 0;
+    out.h = h;
+    out.w = w;
+    out.c = c;
+    return out;
+}
+
+image make_image(int w, int h, int c)
+{
+    image out = make_empty_image(w,h,c);
+    out.data = calloc(h*w*c, sizeof(float));
+    return out;
+}
+
+image make_random_image(int w, int h, int c)
+{
+    image out = make_empty_image(w,h,c);
+    out.data = calloc(h*w*c, sizeof(float));
+    int i;
+    for(i = 0; i < w*h*c; ++i){
+        out.data[i] = (rand_normal() * .25) + .5;
+    }
+    return out;
+}
+
+image float_to_image(int w, int h, int c, float *data)
+{
+    image out = make_empty_image(w,h,c);
+    out.data = data;
+    return out;
+}
+
+void place_image(image im, int w, int h, int dx, int dy, image canvas)
+{
+    int x, y, c;
+    for(c = 0; c < im.c; ++c){
+        for(y = 0; y < h; ++y){
+            for(x = 0; x < w; ++x){
+                float rx = ((float)x / w) * im.w;
+                float ry = ((float)y / h) * im.h;
+                float val = bilinear_interpolate(im, rx, ry, c);
+                set_pixel(canvas, x + dx, y + dy, c, val);
+            }
+        }
+    }
+}
+
+image center_crop_image(image im, int w, int h)
+{
+    int m = (im.w < im.h) ? im.w : im.h;   
+    image c = crop_image(im, (im.w - m) / 2, (im.h - m)/2, m, m);
+    image r = resize_image(c, w, h);
+    free_image(c);
+    return r;
+}
+
+image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect)
+{
+    int x, y, c;
+    float cx = im.w/2.;
+    float cy = im.h/2.;
+    image rot = make_image(w, h, im.c);
+    for(c = 0; c < im.c; ++c){
+        for(y = 0; y < h; ++y){
+            for(x = 0; x < w; ++x){
+                float rx = cos(rad)*((x - w/2.)/s*aspect + dx/s*aspect) - sin(rad)*((y - h/2.)/s + dy/s) + cx;
+                float ry = sin(rad)*((x - w/2.)/s*aspect + dx/s*aspect) + cos(rad)*((y - h/2.)/s + dy/s) + cy;
+                float val = bilinear_interpolate(im, rx, ry, c);
+                set_pixel(rot, x, y, c, val);
+            }
+        }
+    }
+    return rot;
+}
+
+image rotate_image(image im, float rad)
+{
+    int x, y, c;
+    float cx = im.w/2.;
+    float cy = im.h/2.;
+    image rot = make_image(im.w, im.h, im.c);
+    for(c = 0; c < im.c; ++c){
+        for(y = 0; y < im.h; ++y){
+            for(x = 0; x < im.w; ++x){
+                float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx;
+                float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy;
+                float val = bilinear_interpolate(im, rx, ry, c);
+                set_pixel(rot, x, y, c, val);
+            }
+        }
+    }
+    return rot;
+}
+
+void fill_image(image m, float s)
+{
+    int i;
+    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s;
+}
+
+void translate_image(image m, float s)
+{
+    int i;
+    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s;
+}
+
+void scale_image(image m, float s)
+{
+    int i;
+    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s;
+}
+
+image crop_image(image im, int dx, int dy, int w, int h)
+{
+    image cropped = make_image(w, h, im.c);
+    int i, j, k;
+    for(k = 0; k < im.c; ++k){
+        for(j = 0; j < h; ++j){
+            for(i = 0; i < w; ++i){
+                int r = j + dy;
+                int c = i + dx;
+                float val = 0;
+                r = constrain_int(r, 0, im.h-1);
+                c = constrain_int(c, 0, im.w-1);
+                val = get_pixel(im, c, r, k);
+                set_pixel(cropped, i, j, k, val);
+            }
+        }
+    }
+    return cropped;
+}
+
+int best_3d_shift_r(image a, image b, int min, int max)
+{
+    if(min == max) return min;
+    int mid = floor((min + max) / 2.);
+    image c1 = crop_image(b, 0, mid, b.w, b.h);
+    image c2 = crop_image(b, 0, mid+1, b.w, b.h);
+    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10);
+    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10);
+    free_image(c1);
+    free_image(c2);
+    if(d1 < d2) return best_3d_shift_r(a, b, min, mid);
+    else return best_3d_shift_r(a, b, mid+1, max);
+}
+
+int best_3d_shift(image a, image b, int min, int max)
+{
+    int i;
+    int best = 0;
+    float best_distance = FLT_MAX;
+    for(i = min; i <= max; i += 2){
+        image c = crop_image(b, 0, i, b.w, b.h);
+        float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100);
+        if(d < best_distance){
+            best_distance = d;
+            best = i;
+        }
+        printf("%d %f\n", i, d);
+        free_image(c);
+    }
+    return best;
+}
+
+void composite_3d(char *f1, char *f2, char *out, int delta)
+{
+    if(!out) out = "out";
+    image a = load_image(f1, 0,0,0);
+    image b = load_image(f2, 0,0,0);
+    int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100);
+
+    image c1 = crop_image(b, 10, shift, b.w, b.h);
+    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100);
+    image c2 = crop_image(b, -10, shift, b.w, b.h);
+    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100);
+
+    if(d2 < d1 && 0){
+        image swap = a;
+        a = b;
+        b = swap;
+        shift = -shift;
+        printf("swapped, %d\n", shift);
+    }
+    else{
+        printf("%d\n", shift);
+    }
+
+    image c = crop_image(b, delta, shift, a.w, a.h);
+    int i;
+    for(i = 0; i < c.w*c.h; ++i){
+        c.data[i] = a.data[i];
+    }
+    save_image(c, out);
+}
+
+void letterbox_image_into(image im, int w, int h, image boxed)
+{
+    int new_w = im.w;
+    int new_h = im.h;
+    if (((float)w/im.w) < ((float)h/im.h)) {
+        new_w = w;
+        new_h = (im.h * w)/im.w;
+    } else {
+        new_h = h;
+        new_w = (im.w * h)/im.h;
+    }
+    image resized = resize_image(im, new_w, new_h);
+    embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); 
+    free_image(resized);
+}
+
+image letterbox_image(image im, int w, int h)
+{
+    int new_w = im.w;
+    int new_h = im.h;
+    if (((float)w/im.w) < ((float)h/im.h)) {
+        new_w = w;
+        new_h = (im.h * w)/im.w;
+    } else {
+        new_h = h;
+        new_w = (im.w * h)/im.h;
+    }
+    image resized = resize_image(im, new_w, new_h);
+    image boxed = make_image(w, h, im.c);
+    fill_image(boxed, .5);
+    //int i;
+    //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0;
+    embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); 
+    free_image(resized);
+    return boxed;
+}
+
+image resize_max(image im, int max)
+{
+    int w = im.w;
+    int h = im.h;
+    if(w > h){
+        h = (h * max) / w;
+        w = max;
+    } else {
+        w = (w * max) / h;
+        h = max;
+    }
+    if(w == im.w && h == im.h) return im;
+    image resized = resize_image(im, w, h);
+    return resized;
+}
+
+image resize_min(image im, int min)
+{
+    int w = im.w;
+    int h = im.h;
+    if(w < h){
+        h = (h * min) / w;
+        w = min;
+    } else {
+        w = (w * min) / h;
+        h = min;
+    }
+    if(w == im.w && h == im.h) return im;
+    image resized = resize_image(im, w, h);
+    return resized;
+}
+
+image random_crop_image(image im, int w, int h)
+{
+    int dx = rand_int(0, im.w - w);
+    int dy = rand_int(0, im.h - h);
+    image crop = crop_image(im, dx, dy, w, h);
+    return crop;
+}
+
+augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h)
+{
+    augment_args a = {0};
+    aspect = rand_scale(aspect);
+    int r = rand_int(low, high);
+    int min = (im.h < im.w*aspect) ? im.h : im.w*aspect;
+    float scale = (float)r / min;
+
+    float rad = rand_uniform(-angle, angle) * TWO_PI / 360.;
+
+    float dx = (im.w*scale/aspect - w) / 2.;
+    float dy = (im.h*scale - w) / 2.;
+    //if(dx < 0) dx = 0;
+    //if(dy < 0) dy = 0;
+    dx = rand_uniform(-dx, dx);
+    dy = rand_uniform(-dy, dy);
+
+    a.rad = rad;
+    a.scale = scale;
+    a.w = w;
+    a.h = h;
+    a.dx = dx;
+    a.dy = dy;
+    a.aspect = aspect;
+    return a;
+}
+
+image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h)
+{
+    augment_args a = random_augment_args(im, angle, aspect, low, high, w, h);
+    image crop = rotate_crop_image(im, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
+    return crop;
+}
+
+float three_way_max(float a, float b, float c)
+{
+    return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
+}
+
+float three_way_min(float a, float b, float c)
+{
+    return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
+}
+
+void yuv_to_rgb(image im)
+{
+    assert(im.c == 3);
+    int i, j;
+    float r, g, b;
+    float y, u, v;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            y = get_pixel(im, i , j, 0);
+            u = get_pixel(im, i , j, 1);
+            v = get_pixel(im, i , j, 2);
+
+            r = y + 1.13983*v;
+            g = y + -.39465*u + -.58060*v;
+            b = y + 2.03211*u;
+
+            set_pixel(im, i, j, 0, r);
+            set_pixel(im, i, j, 1, g);
+            set_pixel(im, i, j, 2, b);
+        }
+    }
+}
+
+void rgb_to_yuv(image im)
+{
+    assert(im.c == 3);
+    int i, j;
+    float r, g, b;
+    float y, u, v;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            r = get_pixel(im, i , j, 0);
+            g = get_pixel(im, i , j, 1);
+            b = get_pixel(im, i , j, 2);
+
+            y = .299*r + .587*g + .114*b;
+            u = -.14713*r + -.28886*g + .436*b;
+            v = .615*r + -.51499*g + -.10001*b;
+
+            set_pixel(im, i, j, 0, y);
+            set_pixel(im, i, j, 1, u);
+            set_pixel(im, i, j, 2, v);
+        }
+    }
+}
+
+// http://www.cs.rit.edu/~ncs/color/t_convert.html
+void rgb_to_hsv(image im)
+{
+    assert(im.c == 3);
+    int i, j;
+    float r, g, b;
+    float h, s, v;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            r = get_pixel(im, i , j, 0);
+            g = get_pixel(im, i , j, 1);
+            b = get_pixel(im, i , j, 2);
+            float max = three_way_max(r,g,b);
+            float min = three_way_min(r,g,b);
+            float delta = max - min;
+            v = max;
+            if(max == 0){
+                s = 0;
+                h = 0;
+            }else{
+                s = delta/max;
+                if(r == max){
+                    h = (g - b) / delta;
+                } else if (g == max) {
+                    h = 2 + (b - r) / delta;
+                } else {
+                    h = 4 + (r - g) / delta;
+                }
+                if (h < 0) h += 6;
+                h = h/6.;
+            }
+            set_pixel(im, i, j, 0, h);
+            set_pixel(im, i, j, 1, s);
+            set_pixel(im, i, j, 2, v);
+        }
+    }
+}
+
+void hsv_to_rgb(image im)
+{
+    assert(im.c == 3);
+    int i, j;
+    float r, g, b;
+    float h, s, v;
+    float f, p, q, t;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            h = 6 * get_pixel(im, i , j, 0);
+            s = get_pixel(im, i , j, 1);
+            v = get_pixel(im, i , j, 2);
+            if (s == 0) {
+                r = g = b = v;
+            } else {
+                int index = floor(h);
+                f = h - index;
+                p = v*(1-s);
+                q = v*(1-s*f);
+                t = v*(1-s*(1-f));
+                if(index == 0){
+                    r = v; g = t; b = p;
+                } else if(index == 1){
+                    r = q; g = v; b = p;
+                } else if(index == 2){
+                    r = p; g = v; b = t;
+                } else if(index == 3){
+                    r = p; g = q; b = v;
+                } else if(index == 4){
+                    r = t; g = p; b = v;
+                } else {
+                    r = v; g = p; b = q;
+                }
+            }
+            set_pixel(im, i, j, 0, r);
+            set_pixel(im, i, j, 1, g);
+            set_pixel(im, i, j, 2, b);
+        }
+    }
+}
+
+void grayscale_image_3c(image im)
+{
+    assert(im.c == 3);
+    int i, j, k;
+    float scale[] = {0.299, 0.587, 0.114};
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            float val = 0;
+            for(k = 0; k < 3; ++k){
+                val += scale[k]*get_pixel(im, i, j, k);
+            }
+            im.data[0*im.h*im.w + im.w*j + i] = val;
+            im.data[1*im.h*im.w + im.w*j + i] = val;
+            im.data[2*im.h*im.w + im.w*j + i] = val;
+        }
+    }
+}
+
+image grayscale_image(image im)
+{
+    assert(im.c == 3);
+    int i, j, k;
+    image gray = make_image(im.w, im.h, 1);
+    float scale[] = {0.299, 0.587, 0.114};
+    for(k = 0; k < im.c; ++k){
+        for(j = 0; j < im.h; ++j){
+            for(i = 0; i < im.w; ++i){
+                gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
+            }
+        }
+    }
+    return gray;
+}
+
+image threshold_image(image im, float thresh)
+{
+    int i;
+    image t = make_image(im.w, im.h, im.c);
+    for(i = 0; i < im.w*im.h*im.c; ++i){
+        t.data[i] = im.data[i]>thresh ? 1 : 0;
+    }
+    return t;
+}
+
+image blend_image(image fore, image back, float alpha)
+{
+    assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
+    image blend = make_image(fore.w, fore.h, fore.c);
+    int i, j, k;
+    for(k = 0; k < fore.c; ++k){
+        for(j = 0; j < fore.h; ++j){
+            for(i = 0; i < fore.w; ++i){
+                float val = alpha * get_pixel(fore, i, j, k) + 
+                    (1 - alpha)* get_pixel(back, i, j, k);
+                set_pixel(blend, i, j, k, val);
+            }
+        }
+    }
+    return blend;
+}
+
+void scale_image_channel(image im, int c, float v)
+{
+    int i, j;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            float pix = get_pixel(im, i, j, c);
+            pix = pix*v;
+            set_pixel(im, i, j, c, pix);
+        }
+    }
+}
+
+void translate_image_channel(image im, int c, float v)
+{
+    int i, j;
+    for(j = 0; j < im.h; ++j){
+        for(i = 0; i < im.w; ++i){
+            float pix = get_pixel(im, i, j, c);
+            pix = pix+v;
+            set_pixel(im, i, j, c, pix);
+        }
+    }
+}
+
+image binarize_image(image im)
+{
+    image c = copy_image(im);
+    int i;
+    for(i = 0; i < im.w * im.h * im.c; ++i){
+        if(c.data[i] > .5) c.data[i] = 1;
+        else c.data[i] = 0;
+    }
+    return c;
+}
+
+void saturate_image(image im, float sat)
+{
+    rgb_to_hsv(im);
+    scale_image_channel(im, 1, sat);
+    hsv_to_rgb(im);
+    constrain_image(im);
+}
+
+void hue_image(image im, float hue)
+{
+    rgb_to_hsv(im);
+    int i;
+    for(i = 0; i < im.w*im.h; ++i){
+        im.data[i] = im.data[i] + hue;
+        if (im.data[i] > 1) im.data[i] -= 1;
+        if (im.data[i] < 0) im.data[i] += 1;
+    }
+    hsv_to_rgb(im);
+    constrain_image(im);
+}
+
+void exposure_image(image im, float sat)
+{
+    rgb_to_hsv(im);
+    scale_image_channel(im, 2, sat);
+    hsv_to_rgb(im);
+    constrain_image(im);
+}
+
+void distort_image(image im, float hue, float sat, float val)
+{
+    rgb_to_hsv(im);
+    scale_image_channel(im, 1, sat);
+    scale_image_channel(im, 2, val);
+    int i;
+    for(i = 0; i < im.w*im.h; ++i){
+        im.data[i] = im.data[i] + hue;
+        if (im.data[i] > 1) im.data[i] -= 1;
+        if (im.data[i] < 0) im.data[i] += 1;
+    }
+    hsv_to_rgb(im);
+    constrain_image(im);
+}
+
+void random_distort_image(image im, float hue, float saturation, float exposure)
+{
+    float dhue = rand_uniform(-hue, hue);
+    float dsat = rand_scale(saturation);
+    float dexp = rand_scale(exposure);
+    distort_image(im, dhue, dsat, dexp);
+}
+
+void saturate_exposure_image(image im, float sat, float exposure)
+{
+    rgb_to_hsv(im);
+    scale_image_channel(im, 1, sat);
+    scale_image_channel(im, 2, exposure);
+    hsv_to_rgb(im);
+    constrain_image(im);
+}
+
+image resize_image(image im, int w, int h)
+{
+    image resized = make_image(w, h, im.c);   
+    image part = make_image(w, im.h, im.c);
+    int r, c, k;
+    float w_scale = (float)(im.w - 1) / (w - 1);
+    float h_scale = (float)(im.h - 1) / (h - 1);
+    for(k = 0; k < im.c; ++k){
+        for(r = 0; r < im.h; ++r){
+            for(c = 0; c < w; ++c){
+                float val = 0;
+                if(c == w-1 || im.w == 1){
+                    val = get_pixel(im, im.w-1, r, k);
+                } else {
+                    float sx = c*w_scale;
+                    int ix = (int) sx;
+                    float dx = sx - ix;
+                    val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
+                }
+                set_pixel(part, c, r, k, val);
+            }
+        }
+    }
+    for(k = 0; k < im.c; ++k){
+        for(r = 0; r < h; ++r){
+            float sy = r*h_scale;
+            int iy = (int) sy;
+            float dy = sy - iy;
+            for(c = 0; c < w; ++c){
+                float val = (1-dy) * get_pixel(part, c, iy, k);
+                set_pixel(resized, c, r, k, val);
+            }
+            if(r == h-1 || im.h == 1) continue;
+            for(c = 0; c < w; ++c){
+                float val = dy * get_pixel(part, c, iy+1, k);
+                add_pixel(resized, c, r, k, val);
+            }
+        }
+    }
+
+    free_image(part);
+    return resized;
+}
+
+
+void test_resize(char *filename)
+{
+    image im = load_image(filename, 0,0, 3);
+    float mag = mag_array(im.data, im.w*im.h*im.c);
+    printf("L2 Norm: %f\n", mag);
+    image gray = grayscale_image(im);
+
+    image c1 = copy_image(im);
+    image c2 = copy_image(im);
+    image c3 = copy_image(im);
+    image c4 = copy_image(im);
+    distort_image(c1, .1, 1.5, 1.5);
+    distort_image(c2, -.1, .66666, .66666);
+    distort_image(c3, .1, 1.5, .66666);
+    distort_image(c4, .1, .66666, 1.5);
+
+
+    show_image(im,   "Original", 1);
+    show_image(gray, "Gray", 1);
+    show_image(c1, "C1", 1);
+    show_image(c2, "C2", 1);
+    show_image(c3, "C3", 1);
+    show_image(c4, "C4", 1);
+#ifdef OPENCV
+    while(1){
+        image aug = random_augment_image(im, 0, .75, 320, 448, 320, 320);
+        show_image(aug, "aug", 1);
+        free_image(aug);
+
+
+        float exposure = 1.15;
+        float saturation = 1.15;
+        float hue = .05;
+
+        image c = copy_image(im);
+
+        float dexp = rand_scale(exposure);
+        float dsat = rand_scale(saturation);
+        float dhue = rand_uniform(-hue, hue);
+
+        distort_image(c, dhue, dsat, dexp);
+        show_image(c, "rand", 1);
+        printf("%f %f %f\n", dhue, dsat, dexp);
+        free_image(c);
+    }
+#endif
+}
+
+
+image load_image_stb(char *filename, int channels)
+{
+    int w, h, c;
+    unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
+    if (!data) {
+        fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason());
+        exit(0);
+    }
+    if(channels) c = channels;
+    int i,j,k;
+    image im = make_image(w, h, c);
+    for(k = 0; k < c; ++k){
+        for(j = 0; j < h; ++j){
+            for(i = 0; i < w; ++i){
+                int dst_index = i + w*j + w*h*k;
+                int src_index = k + c*i + c*w*j;
+                im.data[dst_index] = (float)data[src_index]/255.;
+            }
+        }
+    }
+    free(data);
+    return im;
+}
+
+image load_image(char *filename, int w, int h, int c)
+{
+#ifdef OPENCV
+    image out = load_image_cv(filename, c);
+#else
+    image out = load_image_stb(filename, c);
+#endif
+
+    if((h && w) && (h != out.h || w != out.w)){
+        image resized = resize_image(out, w, h);
+        free_image(out);
+        out = resized;
+    }
+    return out;
+}
+
+image load_image_color(char *filename, int w, int h)
+{
+    return load_image(filename, w, h, 3);
+}
+
+image get_image_layer(image m, int l)
+{
+    image out = make_image(m.w, m.h, 1);
+    int i;
+    for(i = 0; i < m.h*m.w; ++i){
+        out.data[i] = m.data[i+l*m.h*m.w];
+    }
+    return out;
+}
+void print_image(image m)
+{
+    int i, j, k;
+    for(i =0 ; i < m.c; ++i){
+        for(j =0 ; j < m.h; ++j){
+            for(k = 0; k < m.w; ++k){
+                printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
+                if(k > 30) break;
+            }
+            printf("\n");
+            if(j > 30) break;
+        }
+        printf("\n");
+    }
+    printf("\n");
+}
+
+image collapse_images_vert(image *ims, int n)
+{
+    int color = 1;
+    int border = 1;
+    int h,w,c;
+    w = ims[0].w;
+    h = (ims[0].h + border) * n - border;
+    c = ims[0].c;
+    if(c != 3 || !color){
+        w = (w+border)*c - border;
+        c = 1;
+    }
+
+    image filters = make_image(w, h, c);
+    int i,j;
+    for(i = 0; i < n; ++i){
+        int h_offset = i*(ims[0].h+border);
+        image copy = copy_image(ims[i]);
+        //normalize_image(copy);
+        if(c == 3 && color){
+            embed_image(copy, filters, 0, h_offset);
+        }
+        else{
+            for(j = 0; j < copy.c; ++j){
+                int w_offset = j*(ims[0].w+border);
+                image layer = get_image_layer(copy, j);
+                embed_image(layer, filters, w_offset, h_offset);
+                free_image(layer);
+            }
+        }
+        free_image(copy);
+    }
+    return filters;
+} 
+
+image collapse_images_horz(image *ims, int n)
+{
+    int color = 1;
+    int border = 1;
+    int h,w,c;
+    int size = ims[0].h;
+    h = size;
+    w = (ims[0].w + border) * n - border;
+    c = ims[0].c;
+    if(c != 3 || !color){
+        h = (h+border)*c - border;
+        c = 1;
+    }
+
+    image filters = make_image(w, h, c);
+    int i,j;
+    for(i = 0; i < n; ++i){
+        int w_offset = i*(size+border);
+        image copy = copy_image(ims[i]);
+        //normalize_image(copy);
+        if(c == 3 && color){
+            embed_image(copy, filters, w_offset, 0);
+        }
+        else{
+            for(j = 0; j < copy.c; ++j){
+                int h_offset = j*(size+border);
+                image layer = get_image_layer(copy, j);
+                embed_image(layer, filters, w_offset, h_offset);
+                free_image(layer);
+            }
+        }
+        free_image(copy);
+    }
+    return filters;
+} 
+
+void show_image_normalized(image im, const char *name)
+{
+    image c = copy_image(im);
+    normalize_image(c);
+    show_image(c, name, 1);
+    free_image(c);
+}
+
+void show_images(image *ims, int n, char *window)
+{
+    image m = collapse_images_vert(ims, n);
+    /*
+       int w = 448;
+       int h = ((float)m.h/m.w) * 448;
+       if(h > 896){
+       h = 896;
+       w = ((float)m.w/m.h) * 896;
+       }
+       image sized = resize_image(m, w, h);
+     */
+    normalize_image(m);
+    save_image(m, window);
+    show_image(m, window, 1);
+    free_image(m);
+}
+
+void free_image(image m)
+{
+    if(m.data){
+        free(m.data);
+    }
+}
diff --git a/hanzi_detection/src/image.h b/hanzi_detection/src/image.h
new file mode 100755
index 0000000..3392bb9
--- /dev/null
+++ b/hanzi_detection/src/image.h
@@ -0,0 +1,69 @@
+#ifndef IMAGE_H
+#define IMAGE_H
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <float.h>
+#include <string.h>
+#include <math.h>
+#include "box.h"
+#include "darknet.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#ifdef OPENCV
+void *open_video_stream(const char *f, int c, int w, int h, int fps);
+image get_image_from_stream(void *p);
+image load_image_cv(char *filename, int channels);
+int show_image_cv(image im, const char* name, int ms);
+#endif
+
+float get_color(int c, int x, int max);
+void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);
+void draw_bbox(image a, box bbox, int w, float r, float g, float b);
+void write_label(image a, int r, int c, image *characters, char *string, float *rgb);
+image image_distance(image a, image b);
+void scale_image(image m, float s);
+image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect);
+image random_crop_image(image im, int w, int h);
+image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h);
+augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h);
+void letterbox_image_into(image im, int w, int h, image boxed);
+image resize_max(image im, int max);
+void translate_image(image m, float s);
+void embed_image(image source, image dest, int dx, int dy);
+void place_image(image im, int w, int h, int dx, int dy, image canvas);
+void saturate_image(image im, float sat);
+void exposure_image(image im, float sat);
+void distort_image(image im, float hue, float sat, float val);
+void saturate_exposure_image(image im, float sat, float exposure);
+void rgb_to_hsv(image im);
+void hsv_to_rgb(image im);
+void yuv_to_rgb(image im);
+void rgb_to_yuv(image im);
+
+
+image collapse_image_layers(image source, int border);
+image collapse_images_horz(image *ims, int n);
+image collapse_images_vert(image *ims, int n);
+
+void show_image_normalized(image im, const char *name);
+void show_images(image *ims, int n, char *window);
+void show_image_layers(image p, char *name);
+void show_image_collapsed(image p, char *name);
+
+void print_image(image m);
+
+image make_empty_image(int w, int h, int c);
+void copy_image_into(image src, image dest);
+
+image get_image_layer(image m, int l);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/image_opencv.cpp b/hanzi_detection/src/image_opencv.cpp
new file mode 100755
index 0000000..9d9e501
--- /dev/null
+++ b/hanzi_detection/src/image_opencv.cpp
@@ -0,0 +1,121 @@
+#ifdef OPENCV
+
+#include "stdio.h"
+#include "stdlib.h"
+#include "opencv2/opencv.hpp"
+#include "image.h"
+
+using namespace cv;
+
+extern "C" {
+
+Mat image_to_mat(image im)
+{
+    image copy = copy_image(im);
+    constrain_image(copy);
+    if(im.c == 3) rgbgr_image(copy);
+
+    Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
+    int x,y,c;
+
+    int step = m.step;
+    for(y = 0; y < im.h; ++y){
+        for(x = 0; x < im.w; ++x){
+            for(c= 0; c < im.c; ++c){
+                float val = im.data[c*im.h*im.w + y*im.w + x];
+                m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
+            }
+        }
+    }
+
+    free_image(copy);
+    return m;
+}
+
+image mat_to_image(Mat m)
+{
+    int h = m.rows;
+    int w = m.cols;
+    int c = m.channels();
+    image im = make_image(w, h, c);
+    unsigned char *data = (unsigned char *)m.data;
+    int step = m.step;
+    int i, j, k;
+
+    for(i = 0; i < h; ++i){
+        for(k= 0; k < c; ++k){
+            for(j = 0; j < w; ++j){
+                im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
+            }
+        }
+    }
+    rgbgr_image(im);
+    return im;
+}
+
+void *open_video_stream(const char *f, int c, int w, int h, int fps)
+{
+    VideoCapture *cap;
+    if(f) cap = new VideoCapture(f);
+    else cap = new VideoCapture(c);
+    if(!cap->isOpened()) return 0;
+    if(w) cap->set(CAP_PROP_FRAME_WIDTH, w);
+    if(h) cap->set(CAP_PROP_FRAME_HEIGHT, w);
+    if(fps) cap->set(CAP_PROP_FPS, w);
+    return (void *) cap;
+}
+
+image get_image_from_stream(void *p)
+{
+    VideoCapture *cap = (VideoCapture *)p;
+    Mat m;
+    *cap >> m;
+    if(m.empty()) return make_empty_image(0,0,0);
+    return mat_to_image(m);
+}
+
+image load_image_cv(char *filename, int channels)
+{
+    int flag = -1;
+    if (channels == 0) flag = -1;
+    else if (channels == 1) flag = 0;
+    else if (channels == 3) flag = 1;
+    else {
+        fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
+    }
+    Mat m;
+    m = imread(filename, flag);
+    if(!m.data){
+        fprintf(stderr, "Cannot load image \"%s\"\n", filename);
+        char buff[256];
+        sprintf(buff, "echo %s >> bad.list", filename);
+        system(buff);
+        return make_image(10,10,3);
+        //exit(0);
+    }
+    image im = mat_to_image(m);
+    return im;
+}
+
+int show_image_cv(image im, const char* name, int ms)
+{
+    Mat m = image_to_mat(im);
+    imshow(name, m);
+    int c = waitKey(ms);
+    if (c != -1) c = c%256;
+    return c;
+}
+
+void make_window(char *name, int w, int h, int fullscreen)
+{
+    namedWindow(name, WINDOW_NORMAL); 
+    if (fullscreen) {
+        setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
+    } else {
+        resizeWindow(name, w, h);
+        if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0);
+    }
+}
+
+}
+#endif
diff --git a/hanzi_detection/src/iseg_layer.c b/hanzi_detection/src/iseg_layer.c
new file mode 100755
index 0000000..2bf03a8
--- /dev/null
+++ b/hanzi_detection/src/iseg_layer.c
@@ -0,0 +1,225 @@
+#include "iseg_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+layer make_iseg_layer(int batch, int w, int h, int classes, int ids)
+{
+    layer l = {0};
+    l.type = ISEG;
+
+    l.h = h;
+    l.w = w;
+    l.c = classes + ids;
+    l.out_w = l.w;
+    l.out_h = l.h;
+    l.out_c = l.c;
+    l.classes = classes;
+    l.batch = batch;
+    l.extra = ids;
+    l.cost = calloc(1, sizeof(float));
+    l.outputs = h*w*l.c;
+    l.inputs = l.outputs;
+    l.truths = 90*(l.w*l.h+1);
+    l.delta = calloc(batch*l.outputs, sizeof(float));
+    l.output = calloc(batch*l.outputs, sizeof(float));
+
+    l.counts = calloc(90, sizeof(int));
+    l.sums = calloc(90, sizeof(float*));
+    if(ids){
+        int i;
+        for(i = 0; i < 90; ++i){
+            l.sums[i] = calloc(ids, sizeof(float));
+        }
+    }
+
+    l.forward = forward_iseg_layer;
+    l.backward = backward_iseg_layer;
+#ifdef GPU
+    l.forward_gpu = forward_iseg_layer_gpu;
+    l.backward_gpu = backward_iseg_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+    fprintf(stderr, "iseg\n");
+    srand(0);
+
+    return l;
+}
+
+void resize_iseg_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->outputs = h*w*l->c;
+    l->inputs = l->outputs;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =     cuda_make_array(l->delta, l->batch*l->outputs);
+    l->output_gpu =    cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
+
+void forward_iseg_layer(const layer l, network net)
+{
+
+    double time = what_time_is_it_now();
+    int i,b,j,k;
+    int ids = l.extra;
+    memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+
+#ifndef GPU
+    for (b = 0; b < l.batch; ++b){
+        int index = b*l.outputs;
+        activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC);
+    }
+#endif
+
+    for (b = 0; b < l.batch; ++b){
+        // a priori, each pixel has no class
+        for(i = 0; i < l.classes; ++i){
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + i*l.w*l.h + k;
+                l.delta[index] = 0 - l.output[index];
+            }
+        }
+
+        // a priori, embedding should be small magnitude
+        for(i = 0; i < ids; ++i){
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + (i+l.classes)*l.w*l.h + k;
+                l.delta[index] = .1 * (0 - l.output[index]);
+            }
+        }
+
+
+        memset(l.counts, 0, 90*sizeof(int));
+        for(i = 0; i < 90; ++i){
+            fill_cpu(ids, 0, l.sums[i], 1);
+            
+            int c = net.truth[b*l.truths + i*(l.w*l.h+1)];
+            if(c < 0) break;
+            // add up metric embeddings for each instance
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + c*l.w*l.h + k;
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    l.delta[index] = v - l.output[index];
+                    axpy_cpu(ids, 1, l.output + b*l.outputs + l.classes*l.w*l.h + k, l.w*l.h, l.sums[i], 1);
+                    ++l.counts[i];
+                }
+            }
+        }
+
+        float *mse = calloc(90, sizeof(float));
+        for(i = 0; i < 90; ++i){
+            int c = net.truth[b*l.truths + i*(l.w*l.h+1)];
+            if(c < 0) break;
+            for(k = 0; k < l.w*l.h; ++k){
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    int z;
+                    float sum = 0;
+                    for(z = 0; z < ids; ++z){
+                        int index = b*l.outputs + (l.classes + z)*l.w*l.h + k;
+                        sum += pow(l.sums[i][z]/l.counts[i] - l.output[index], 2);
+                    }
+                    mse[i] += sum;
+                }
+            }
+            mse[i] /= l.counts[i];
+        }
+
+        // Calculate average embedding
+        for(i = 0; i < 90; ++i){
+            if(!l.counts[i]) continue;
+            scal_cpu(ids, 1.f/l.counts[i], l.sums[i], 1);
+            if(b == 0 && net.gpu_index == 0){
+                printf("%4d, %6.3f, ", l.counts[i], mse[i]);
+                for(j = 0; j < ids; ++j){
+                    printf("%6.3f,", l.sums[i][j]);
+                }
+                printf("\n");
+            }
+        }
+        free(mse);
+
+        // Calculate embedding loss
+        for(i = 0; i < 90; ++i){
+            if(!l.counts[i]) continue;
+            for(k = 0; k < l.w*l.h; ++k){
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    for(j = 0; j < 90; ++j){
+                        if(!l.counts[j])continue;
+                        int z;
+                        for(z = 0; z < ids; ++z){
+                            int index = b*l.outputs + (l.classes + z)*l.w*l.h + k;
+                            float diff = l.sums[j][z] - l.output[index];
+                            if (j == i) l.delta[index] +=   diff < 0? -.1 : .1;
+                            else        l.delta[index] += -(diff < 0? -.1 : .1);
+                        }
+                    }
+                }
+            }
+        }
+
+        for(i = 0; i < ids; ++i){
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + (i+l.classes)*l.w*l.h + k;
+                l.delta[index] *= .01;
+            }
+        }
+    }
+
+    *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+    printf("took %lf sec\n", what_time_is_it_now() - time);
+}
+
+void backward_iseg_layer(const layer l, network net)
+{
+    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void forward_iseg_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
+    int b;
+    for (b = 0; b < l.batch; ++b){
+        activate_array_gpu(l.output_gpu + b*l.outputs, l.classes*l.w*l.h, LOGISTIC);
+        //if(l.extra) activate_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC);
+    }
+
+    cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
+    forward_iseg_layer(l, net);
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+}
+
+void backward_iseg_layer_gpu(const layer l, network net)
+{
+    int b;
+    for (b = 0; b < l.batch; ++b){
+        //if(l.extra) gradient_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC, l.delta_gpu + b*l.outputs + l.classes*l.w*l.h);
+    }
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+
diff --git a/hanzi_detection/src/iseg_layer.h b/hanzi_detection/src/iseg_layer.h
new file mode 100755
index 0000000..dd8e64e
--- /dev/null
+++ b/hanzi_detection/src/iseg_layer.h
@@ -0,0 +1,19 @@
+#ifndef ISEG_LAYER_H
+#define ISEG_LAYER_H
+
+#include "darknet.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_iseg_layer(int batch, int w, int h, int classes, int ids);
+void forward_iseg_layer(const layer l, network net);
+void backward_iseg_layer(const layer l, network net);
+void resize_iseg_layer(layer *l, int w, int h);
+int iseg_num_detections(layer l, float thresh);
+
+#ifdef GPU
+void forward_iseg_layer_gpu(const layer l, network net);
+void backward_iseg_layer_gpu(layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/l2norm_layer.c b/hanzi_detection/src/l2norm_layer.c
new file mode 100755
index 0000000..d099479
--- /dev/null
+++ b/hanzi_detection/src/l2norm_layer.c
@@ -0,0 +1,63 @@
+#include "l2norm_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "cuda.h"
+
+#include <float.h>
+#include <math.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <assert.h>
+
+layer make_l2norm_layer(int batch, int inputs)
+{
+    fprintf(stderr, "l2norm                                         %4d\n",  inputs);
+    layer l = {0};
+    l.type = L2NORM;
+    l.batch = batch;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.output = calloc(inputs*batch, sizeof(float));
+    l.scales = calloc(inputs*batch, sizeof(float));
+    l.delta = calloc(inputs*batch, sizeof(float));
+
+    l.forward = forward_l2norm_layer;
+    l.backward = backward_l2norm_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_l2norm_layer_gpu;
+    l.backward_gpu = backward_l2norm_layer_gpu;
+
+    l.output_gpu = cuda_make_array(l.output, inputs*batch); 
+    l.scales_gpu = cuda_make_array(l.output, inputs*batch); 
+    l.delta_gpu = cuda_make_array(l.delta, inputs*batch); 
+    #endif
+    return l;
+}
+
+void forward_l2norm_layer(const layer l, network net)
+{
+    copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
+    l2normalize_cpu(l.output, l.scales, l.batch, l.out_c, l.out_w*l.out_h);
+}
+
+void backward_l2norm_layer(const layer l, network net)
+{
+    //axpy_cpu(l.inputs*l.batch, 1, l.scales, 1, l.delta, 1);
+    axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void forward_l2norm_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
+    l2normalize_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+}
+
+void backward_l2norm_layer_gpu(const layer l, network net)
+{
+    axpy_gpu(l.batch*l.inputs, 1, l.scales_gpu, 1, l.delta_gpu, 1);
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+
+#endif
diff --git a/hanzi_detection/src/l2norm_layer.h b/hanzi_detection/src/l2norm_layer.h
new file mode 100755
index 0000000..1ca6f71
--- /dev/null
+++ b/hanzi_detection/src/l2norm_layer.h
@@ -0,0 +1,15 @@
+#ifndef L2NORM_LAYER_H
+#define L2NORM_LAYER_H
+#include "layer.h"
+#include "network.h"
+
+layer make_l2norm_layer(int batch, int inputs);
+void forward_l2norm_layer(const layer l, network net);
+void backward_l2norm_layer(const layer l, network net);
+
+#ifdef GPU
+void forward_l2norm_layer_gpu(const layer l, network net);
+void backward_l2norm_layer_gpu(const layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/layer.c b/hanzi_detection/src/layer.c
new file mode 100755
index 0000000..c27b477
--- /dev/null
+++ b/hanzi_detection/src/layer.c
@@ -0,0 +1,97 @@
+#include "layer.h"
+#include "cuda.h"
+
+#include <stdlib.h>
+
+void free_layer(layer l)
+{
+    if(l.type == DROPOUT){
+        if(l.rand)           free(l.rand);
+#ifdef GPU
+        if(l.rand_gpu)             cuda_free(l.rand_gpu);
+#endif
+        return;
+    }
+    if(l.cweights)           free(l.cweights);
+    if(l.indexes)            free(l.indexes);
+    if(l.input_layers)       free(l.input_layers);
+    if(l.input_sizes)        free(l.input_sizes);
+    if(l.map)                free(l.map);
+    if(l.rand)               free(l.rand);
+    if(l.cost)               free(l.cost);
+    if(l.state)              free(l.state);
+    if(l.prev_state)         free(l.prev_state);
+    if(l.forgot_state)       free(l.forgot_state);
+    if(l.forgot_delta)       free(l.forgot_delta);
+    if(l.state_delta)        free(l.state_delta);
+    if(l.concat)             free(l.concat);
+    if(l.concat_delta)       free(l.concat_delta);
+    if(l.binary_weights)     free(l.binary_weights);
+    if(l.biases)             free(l.biases);
+    if(l.bias_updates)       free(l.bias_updates);
+    if(l.scales)             free(l.scales);
+    if(l.scale_updates)      free(l.scale_updates);
+    if(l.weights)            free(l.weights);
+    if(l.weight_updates)     free(l.weight_updates);
+    if(l.delta)              free(l.delta);
+    if(l.output)             free(l.output);
+    if(l.squared)            free(l.squared);
+    if(l.norms)              free(l.norms);
+    if(l.spatial_mean)       free(l.spatial_mean);
+    if(l.mean)               free(l.mean);
+    if(l.variance)           free(l.variance);
+    if(l.mean_delta)         free(l.mean_delta);
+    if(l.variance_delta)     free(l.variance_delta);
+    if(l.rolling_mean)       free(l.rolling_mean);
+    if(l.rolling_variance)   free(l.rolling_variance);
+    if(l.x)                  free(l.x);
+    if(l.x_norm)             free(l.x_norm);
+    if(l.m)                  free(l.m);
+    if(l.v)                  free(l.v);
+    if(l.z_cpu)              free(l.z_cpu);
+    if(l.r_cpu)              free(l.r_cpu);
+    if(l.h_cpu)              free(l.h_cpu);
+    if(l.binary_input)       free(l.binary_input);
+
+#ifdef GPU
+    if(l.indexes_gpu)           cuda_free((float *)l.indexes_gpu);
+
+    if(l.z_gpu)                   cuda_free(l.z_gpu);
+    if(l.r_gpu)                   cuda_free(l.r_gpu);
+    if(l.h_gpu)                   cuda_free(l.h_gpu);
+    if(l.m_gpu)                   cuda_free(l.m_gpu);
+    if(l.v_gpu)                   cuda_free(l.v_gpu);
+    if(l.prev_state_gpu)          cuda_free(l.prev_state_gpu);
+    if(l.forgot_state_gpu)        cuda_free(l.forgot_state_gpu);
+    if(l.forgot_delta_gpu)        cuda_free(l.forgot_delta_gpu);
+    if(l.state_gpu)               cuda_free(l.state_gpu);
+    if(l.state_delta_gpu)         cuda_free(l.state_delta_gpu);
+    if(l.gate_gpu)                cuda_free(l.gate_gpu);
+    if(l.gate_delta_gpu)          cuda_free(l.gate_delta_gpu);
+    if(l.save_gpu)                cuda_free(l.save_gpu);
+    if(l.save_delta_gpu)          cuda_free(l.save_delta_gpu);
+    if(l.concat_gpu)              cuda_free(l.concat_gpu);
+    if(l.concat_delta_gpu)        cuda_free(l.concat_delta_gpu);
+    if(l.binary_input_gpu)        cuda_free(l.binary_input_gpu);
+    if(l.binary_weights_gpu)      cuda_free(l.binary_weights_gpu);
+    if(l.mean_gpu)                cuda_free(l.mean_gpu);
+    if(l.variance_gpu)            cuda_free(l.variance_gpu);
+    if(l.rolling_mean_gpu)        cuda_free(l.rolling_mean_gpu);
+    if(l.rolling_variance_gpu)    cuda_free(l.rolling_variance_gpu);
+    if(l.variance_delta_gpu)      cuda_free(l.variance_delta_gpu);
+    if(l.mean_delta_gpu)          cuda_free(l.mean_delta_gpu);
+    if(l.x_gpu)                   cuda_free(l.x_gpu);
+    if(l.x_norm_gpu)              cuda_free(l.x_norm_gpu);
+    if(l.weights_gpu)             cuda_free(l.weights_gpu);
+    if(l.weight_updates_gpu)      cuda_free(l.weight_updates_gpu);
+    if(l.biases_gpu)              cuda_free(l.biases_gpu);
+    if(l.bias_updates_gpu)        cuda_free(l.bias_updates_gpu);
+    if(l.scales_gpu)              cuda_free(l.scales_gpu);
+    if(l.scale_updates_gpu)       cuda_free(l.scale_updates_gpu);
+    if(l.output_gpu)              cuda_free(l.output_gpu);
+    if(l.delta_gpu)               cuda_free(l.delta_gpu);
+    if(l.rand_gpu)                cuda_free(l.rand_gpu);
+    if(l.squared_gpu)             cuda_free(l.squared_gpu);
+    if(l.norms_gpu)               cuda_free(l.norms_gpu);
+#endif
+}
diff --git a/hanzi_detection/src/layer.h b/hanzi_detection/src/layer.h
new file mode 100755
index 0000000..af6cd2a
--- /dev/null
+++ b/hanzi_detection/src/layer.h
@@ -0,0 +1 @@
+#include "darknet.h"
diff --git a/hanzi_detection/src/list.c b/hanzi_detection/src/list.c
new file mode 100755
index 0000000..0e4165d
--- /dev/null
+++ b/hanzi_detection/src/list.c
@@ -0,0 +1,92 @@
+#include <stdlib.h>
+#include <string.h>
+#include "list.h"
+
+list *make_list()
+{
+	list *l = malloc(sizeof(list));
+	l->size = 0;
+	l->front = 0;
+	l->back = 0;
+	return l;
+}
+
+/*
+void transfer_node(list *s, list *d, node *n)
+{
+    node *prev, *next;
+    prev = n->prev;
+    next = n->next;
+    if(prev) prev->next = next;
+    if(next) next->prev = prev;
+    --s->size;
+    if(s->front == n) s->front = next;
+    if(s->back == n) s->back = prev;
+}
+*/
+
+void *list_pop(list *l){
+    if(!l->back) return 0;
+    node *b = l->back;
+    void *val = b->val;
+    l->back = b->prev;
+    if(l->back) l->back->next = 0;
+    free(b);
+    --l->size;
+    
+    return val;
+}
+
+void list_insert(list *l, void *val)
+{
+	node *new = malloc(sizeof(node));
+	new->val = val;
+	new->next = 0;
+
+	if(!l->back){
+		l->front = new;
+		new->prev = 0;
+	}else{
+		l->back->next = new;
+		new->prev = l->back;
+	}
+	l->back = new;
+	++l->size;
+}
+
+void free_node(node *n)
+{
+	node *next;
+	while(n) {
+		next = n->next;
+		free(n);
+		n = next;
+	}
+}
+
+void free_list(list *l)
+{
+	free_node(l->front);
+	free(l);
+}
+
+void free_list_contents(list *l)
+{
+	node *n = l->front;
+	while(n){
+		free(n->val);
+		n = n->next;
+	}
+}
+
+void **list_to_array(list *l)
+{
+    void **a = calloc(l->size, sizeof(void*));
+    int count = 0;
+    node *n = l->front;
+    while(n){
+        a[count++] = n->val;
+        n = n->next;
+    }
+    return a;
+}
diff --git a/hanzi_detection/src/list.h b/hanzi_detection/src/list.h
new file mode 100755
index 0000000..6b445c7
--- /dev/null
+++ b/hanzi_detection/src/list.h
@@ -0,0 +1,13 @@
+#ifndef LIST_H
+#define LIST_H
+#include "darknet.h"
+
+list *make_list();
+int list_find(list *l, void *val);
+
+void list_insert(list *, void *);
+
+
+void free_list_contents(list *l);
+
+#endif
diff --git a/hanzi_detection/src/local_layer.c b/hanzi_detection/src/local_layer.c
new file mode 100755
index 0000000..74f6910
--- /dev/null
+++ b/hanzi_detection/src/local_layer.c
@@ -0,0 +1,293 @@
+#include "local_layer.h"
+#include "utils.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+#include <stdio.h>
+#include <time.h>
+
+int local_out_height(local_layer l)
+{
+    int h = l.h;
+    if (!l.pad) h -= l.size;
+    else h -= 1;
+    return h/l.stride + 1;
+}
+
+int local_out_width(local_layer l)
+{
+    int w = l.w;
+    if (!l.pad) w -= l.size;
+    else w -= 1;
+    return w/l.stride + 1;
+}
+
+local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
+{
+    int i;
+    local_layer l = {0};
+    l.type = LOCAL;
+
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.n = n;
+    l.batch = batch;
+    l.stride = stride;
+    l.size = size;
+    l.pad = pad;
+
+    int out_h = local_out_height(l);
+    int out_w = local_out_width(l);
+    int locations = out_h*out_w;
+    l.out_h = out_h;
+    l.out_w = out_w;
+    l.out_c = n;
+    l.outputs = l.out_h * l.out_w * l.out_c;
+    l.inputs = l.w * l.h * l.c;
+
+    l.weights = calloc(c*n*size*size*locations, sizeof(float));
+    l.weight_updates = calloc(c*n*size*size*locations, sizeof(float));
+
+    l.biases = calloc(l.outputs, sizeof(float));
+    l.bias_updates = calloc(l.outputs, sizeof(float));
+
+    // float scale = 1./sqrt(size*size*c);
+    float scale = sqrt(2./(size*size*c));
+    for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1);
+
+    l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
+    l.delta  = calloc(l.batch*out_h * out_w * n, sizeof(float));
+
+    l.workspace_size = out_h*out_w*size*size*c;
+    
+    l.forward = forward_local_layer;
+    l.backward = backward_local_layer;
+    l.update = update_local_layer;
+
+#ifdef GPU
+    l.forward_gpu = forward_local_layer_gpu;
+    l.backward_gpu = backward_local_layer_gpu;
+    l.update_gpu = update_local_layer_gpu;
+
+    l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations);
+    l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations);
+
+    l.biases_gpu = cuda_make_array(l.biases, l.outputs);
+    l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs);
+
+    l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
+    l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
+
+#endif
+    l.activation = activation;
+
+    fprintf(stderr, "Local Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
+
+    return l;
+}
+
+void forward_local_layer(const local_layer l, network net)
+{
+    int out_h = local_out_height(l);
+    int out_w = local_out_width(l);
+    int i, j;
+    int locations = out_h * out_w;
+
+    for(i = 0; i < l.batch; ++i){
+        copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
+    }
+
+    for(i = 0; i < l.batch; ++i){
+        float *input = net.input + i*l.w*l.h*l.c;
+        im2col_cpu(input, l.c, l.h, l.w, 
+                l.size, l.stride, l.pad, net.workspace);
+        float *output = l.output + i*l.outputs;
+        for(j = 0; j < locations; ++j){
+            float *a = l.weights + j*l.size*l.size*l.c*l.n;
+            float *b = net.workspace + j;
+            float *c = output + j;
+
+            int m = l.n;
+            int n = 1;
+            int k = l.size*l.size*l.c;
+
+            gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
+        }
+    }
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_local_layer(local_layer l, network net)
+{
+    int i, j;
+    int locations = l.out_w*l.out_h;
+
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+
+    for(i = 0; i < l.batch; ++i){
+        axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
+    }
+
+    for(i = 0; i < l.batch; ++i){
+        float *input = net.input + i*l.w*l.h*l.c;
+        im2col_cpu(input, l.c, l.h, l.w, 
+                l.size, l.stride, l.pad, net.workspace);
+
+        for(j = 0; j < locations; ++j){ 
+            float *a = l.delta + i*l.outputs + j;
+            float *b = net.workspace + j;
+            float *c = l.weight_updates + j*l.size*l.size*l.c*l.n;
+            int m = l.n;
+            int n = l.size*l.size*l.c;
+            int k = 1;
+
+            gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
+        }
+
+        if(net.delta){
+            for(j = 0; j < locations; ++j){ 
+                float *a = l.weights + j*l.size*l.size*l.c*l.n;
+                float *b = l.delta + i*l.outputs + j;
+                float *c = net.workspace + j;
+
+                int m = l.size*l.size*l.c;
+                int n = 1;
+                int k = l.n;
+
+                gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
+            }
+
+            col2im_cpu(net.workspace, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, net.delta+i*l.c*l.h*l.w);
+        }
+    }
+}
+
+void update_local_layer(local_layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    int locations = l.out_w*l.out_h;
+    int size = l.size*l.size*l.c*l.n*locations;
+    axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+    scal_cpu(l.outputs, momentum, l.bias_updates, 1);
+
+    axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
+    axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+    scal_cpu(size, momentum, l.weight_updates, 1);
+}
+
+#ifdef GPU
+
+void forward_local_layer_gpu(const local_layer l, network net)
+{
+    int out_h = local_out_height(l);
+    int out_w = local_out_width(l);
+    int i, j;
+    int locations = out_h * out_w;
+
+    for(i = 0; i < l.batch; ++i){
+        copy_gpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
+    }
+
+    for(i = 0; i < l.batch; ++i){
+        float *input = net.input_gpu + i*l.w*l.h*l.c;
+        im2col_gpu(input, l.c, l.h, l.w, 
+                l.size, l.stride, l.pad, net.workspace);
+        float *output = l.output_gpu + i*l.outputs;
+        for(j = 0; j < locations; ++j){
+            float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
+            float *b = net.workspace + j;
+            float *c = output + j;
+
+            int m = l.n;
+            int n = 1;
+            int k = l.size*l.size*l.c;
+
+            gemm_gpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
+        }
+    }
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_local_layer_gpu(local_layer l, network net)
+{
+    int i, j;
+    int locations = l.out_w*l.out_h;
+
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+    for(i = 0; i < l.batch; ++i){
+        axpy_gpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
+    }
+
+    for(i = 0; i < l.batch; ++i){
+        float *input = net.input_gpu + i*l.w*l.h*l.c;
+        im2col_gpu(input, l.c, l.h, l.w, 
+                l.size, l.stride, l.pad, net.workspace);
+
+        for(j = 0; j < locations; ++j){ 
+            float *a = l.delta_gpu + i*l.outputs + j;
+            float *b = net.workspace + j;
+            float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n;
+            int m = l.n;
+            int n = l.size*l.size*l.c;
+            int k = 1;
+
+            gemm_gpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
+        }
+
+        if(net.delta_gpu){
+            for(j = 0; j < locations; ++j){ 
+                float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
+                float *b = l.delta_gpu + i*l.outputs + j;
+                float *c = net.workspace + j;
+
+                int m = l.size*l.size*l.c;
+                int n = 1;
+                int k = l.n;
+
+                gemm_gpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
+            }
+
+            col2im_gpu(net.workspace, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, net.delta_gpu+i*l.c*l.h*l.w);
+        }
+    }
+}
+
+void update_local_layer_gpu(local_layer l, update_args a)
+{
+    float learning_rate = a.learning_rate*l.learning_rate_scale;
+    float momentum = a.momentum;
+    float decay = a.decay;
+    int batch = a.batch;
+
+    int locations = l.out_w*l.out_h;
+    int size = l.size*l.size*l.c*l.n*locations;
+    axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+    scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);
+
+    axpy_gpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+    axpy_gpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+    scal_gpu(size, momentum, l.weight_updates_gpu, 1);
+}
+
+void pull_local_layer(local_layer l)
+{
+    int locations = l.out_w*l.out_h;
+    int size = l.size*l.size*l.c*l.n*locations;
+    cuda_pull_array(l.weights_gpu, l.weights, size);
+    cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
+}
+
+void push_local_layer(local_layer l)
+{
+    int locations = l.out_w*l.out_h;
+    int size = l.size*l.size*l.c*l.n*locations;
+    cuda_push_array(l.weights_gpu, l.weights, size);
+    cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+}
+#endif
diff --git a/hanzi_detection/src/local_layer.h b/hanzi_detection/src/local_layer.h
new file mode 100755
index 0000000..776e572
--- /dev/null
+++ b/hanzi_detection/src/local_layer.h
@@ -0,0 +1,31 @@
+#ifndef LOCAL_LAYER_H
+#define LOCAL_LAYER_H
+
+#include "cuda.h"
+#include "image.h"
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+
+typedef layer local_layer;
+
+#ifdef GPU
+void forward_local_layer_gpu(local_layer layer, network net);
+void backward_local_layer_gpu(local_layer layer, network net);
+void update_local_layer_gpu(local_layer layer, update_args a);
+
+void push_local_layer(local_layer layer);
+void pull_local_layer(local_layer layer);
+#endif
+
+local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation);
+
+void forward_local_layer(const local_layer layer, network net);
+void backward_local_layer(local_layer layer, network net);
+void update_local_layer(local_layer layer, update_args a);
+
+void bias_output(float *output, float *biases, int batch, int n, int size);
+void backward_bias(float *bias_updates, float *delta, int batch, int n, int size);
+
+#endif
+
diff --git a/hanzi_detection/src/logistic_layer.c b/hanzi_detection/src/logistic_layer.c
new file mode 100755
index 0000000..b2b3d6b
--- /dev/null
+++ b/hanzi_detection/src/logistic_layer.c
@@ -0,0 +1,71 @@
+#include "logistic_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "cuda.h"
+
+#include <float.h>
+#include <math.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <assert.h>
+
+layer make_logistic_layer(int batch, int inputs)
+{
+    fprintf(stderr, "logistic x entropy                             %4d\n",  inputs);
+    layer l = {0};
+    l.type = LOGXENT;
+    l.batch = batch;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.loss = calloc(inputs*batch, sizeof(float));
+    l.output = calloc(inputs*batch, sizeof(float));
+    l.delta = calloc(inputs*batch, sizeof(float));
+    l.cost = calloc(1, sizeof(float));
+
+    l.forward = forward_logistic_layer;
+    l.backward = backward_logistic_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_logistic_layer_gpu;
+    l.backward_gpu = backward_logistic_layer_gpu;
+
+    l.output_gpu = cuda_make_array(l.output, inputs*batch); 
+    l.loss_gpu = cuda_make_array(l.loss, inputs*batch); 
+    l.delta_gpu = cuda_make_array(l.delta, inputs*batch); 
+    #endif
+    return l;
+}
+
+void forward_logistic_layer(const layer l, network net)
+{
+    copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
+    activate_array(l.output, l.outputs*l.batch, LOGISTIC);
+    if(net.truth){
+        logistic_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss);
+        l.cost[0] = sum_array(l.loss, l.batch*l.inputs);
+    }
+}
+
+void backward_logistic_layer(const layer l, network net)
+{
+    axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void forward_logistic_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, LOGISTIC);
+    if(net.truth){
+        logistic_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu);
+        cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs);
+        l.cost[0] = sum_array(l.loss, l.batch*l.inputs);
+    }
+}
+
+void backward_logistic_layer_gpu(const layer l, network net)
+{
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+
+#endif
diff --git a/hanzi_detection/src/logistic_layer.h b/hanzi_detection/src/logistic_layer.h
new file mode 100755
index 0000000..9c25bee
--- /dev/null
+++ b/hanzi_detection/src/logistic_layer.h
@@ -0,0 +1,15 @@
+#ifndef LOGISTIC_LAYER_H
+#define LOGISTIC_LAYER_H
+#include "layer.h"
+#include "network.h"
+
+layer make_logistic_layer(int batch, int inputs);
+void forward_logistic_layer(const layer l, network net);
+void backward_logistic_layer(const layer l, network net);
+
+#ifdef GPU
+void forward_logistic_layer_gpu(const layer l, network net);
+void backward_logistic_layer_gpu(const layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/lstm_layer.c b/hanzi_detection/src/lstm_layer.c
new file mode 100755
index 0000000..fb07de2
--- /dev/null
+++ b/hanzi_detection/src/lstm_layer.c
@@ -0,0 +1,626 @@
+#include "lstm_layer.h"
+#include "connected_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+static void increment_layer(layer *l, int steps)
+{
+    int num = l->outputs*l->batch*steps;
+    l->output += num;
+    l->delta += num;
+    l->x += num;
+    l->x_norm += num;
+
+#ifdef GPU
+    l->output_gpu += num;
+    l->delta_gpu += num;
+    l->x_gpu += num;
+    l->x_norm_gpu += num;
+#endif
+}
+
+layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam)
+{
+    fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
+    batch = batch / steps;
+    layer l = { 0 };
+    l.batch = batch;
+    l.type = LSTM;
+    l.steps = steps;
+    l.inputs = inputs;
+
+    l.uf = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.uf->batch = batch;
+
+    l.ui = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.ui->batch = batch;
+
+    l.ug = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.ug->batch = batch;
+
+    l.uo = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
+    l.uo->batch = batch;
+
+    l.wf = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wf->batch = batch;
+
+    l.wi = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wi->batch = batch;
+
+    l.wg = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wg->batch = batch;
+
+    l.wo = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
+    l.wo->batch = batch;
+
+    l.batch_normalize = batch_normalize;
+    l.outputs = outputs;
+
+    l.output = calloc(outputs*batch*steps, sizeof(float));
+    l.state = calloc(outputs*batch, sizeof(float));
+
+    l.forward = forward_lstm_layer;
+    l.update = update_lstm_layer;
+
+    l.prev_state_cpu =  calloc(batch*outputs, sizeof(float));
+    l.prev_cell_cpu =   calloc(batch*outputs, sizeof(float));
+    l.cell_cpu =        calloc(batch*outputs*steps, sizeof(float));
+
+    l.f_cpu =           calloc(batch*outputs, sizeof(float));
+    l.i_cpu =           calloc(batch*outputs, sizeof(float));
+    l.g_cpu =           calloc(batch*outputs, sizeof(float));
+    l.o_cpu =           calloc(batch*outputs, sizeof(float));
+    l.c_cpu =           calloc(batch*outputs, sizeof(float));
+    l.h_cpu =           calloc(batch*outputs, sizeof(float));
+    l.temp_cpu =        calloc(batch*outputs, sizeof(float));
+    l.temp2_cpu =       calloc(batch*outputs, sizeof(float));
+    l.temp3_cpu =       calloc(batch*outputs, sizeof(float));
+    l.dc_cpu =          calloc(batch*outputs, sizeof(float));
+    l.dh_cpu =          calloc(batch*outputs, sizeof(float));
+
+#ifdef GPU
+    l.forward_gpu = forward_lstm_layer_gpu;
+    l.backward_gpu = backward_lstm_layer_gpu;
+    l.update_gpu = update_lstm_layer_gpu;
+
+    l.output_gpu = cuda_make_array(0, batch*outputs*steps);
+    l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps);
+
+    l.prev_state_gpu = cuda_make_array(0, batch*outputs);
+    l.prev_cell_gpu = cuda_make_array(0, batch*outputs);
+    l.cell_gpu = cuda_make_array(0, batch*outputs*steps);
+
+    l.f_gpu = cuda_make_array(0, batch*outputs);
+    l.i_gpu = cuda_make_array(0, batch*outputs);
+    l.g_gpu = cuda_make_array(0, batch*outputs);
+    l.o_gpu = cuda_make_array(0, batch*outputs);
+    l.c_gpu = cuda_make_array(0, batch*outputs);
+    l.h_gpu = cuda_make_array(0, batch*outputs);
+    l.temp_gpu =  cuda_make_array(0, batch*outputs);
+    l.temp2_gpu = cuda_make_array(0, batch*outputs);
+    l.temp3_gpu = cuda_make_array(0, batch*outputs);
+    l.dc_gpu = cuda_make_array(0, batch*outputs);
+    l.dh_gpu = cuda_make_array(0, batch*outputs);
+#ifdef CUDNN
+        cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); 
+        cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); 
+        cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); 
+        cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); 
+
+        cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); 
+        cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); 
+        cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); 
+        cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); 
+#endif
+
+#endif
+
+    return l;
+}
+
+void update_lstm_layer(layer l, update_args a)
+{
+    update_connected_layer(*(l.wf), a);
+    update_connected_layer(*(l.wi), a);
+    update_connected_layer(*(l.wg), a);
+    update_connected_layer(*(l.wo), a);
+    update_connected_layer(*(l.uf), a);
+    update_connected_layer(*(l.ui), a);
+    update_connected_layer(*(l.ug), a);
+    update_connected_layer(*(l.uo), a);
+}
+
+void forward_lstm_layer(layer l, network state)
+{
+    network s = { 0 };
+    s.train = state.train;
+    int i;
+    layer wf = *(l.wf);
+    layer wi = *(l.wi);
+    layer wg = *(l.wg);
+    layer wo = *(l.wo);
+
+    layer uf = *(l.uf);
+    layer ui = *(l.ui);
+    layer ug = *(l.ug);
+    layer uo = *(l.uo);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1);
+    if (state.train) {
+        fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
+    }
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input = l.h_cpu;
+        forward_connected_layer(wf, s);							
+        forward_connected_layer(wi, s);							
+        forward_connected_layer(wg, s);							
+        forward_connected_layer(wo, s);							
+
+        s.input = state.input;
+        forward_connected_layer(uf, s);							
+        forward_connected_layer(ui, s);							
+        forward_connected_layer(ug, s);							
+        forward_connected_layer(uo, s);							
+
+        copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);
+        axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);
+
+        copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);	
+        axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);	
+
+        copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);	
+        axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);	
+
+        copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);	
+        axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);	
+
+        activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array(l.g_cpu, l.outputs*l.batch, TANH);			
+        activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);		
+
+        copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);		
+        mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);		
+        mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1);			
+        axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1);	
+
+        copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1);			
+        activate_array(l.h_cpu, l.outputs*l.batch, TANH);		
+        mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1);	
+
+        copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1);		
+        copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1);
+
+        state.input += l.inputs*l.batch;
+        l.output    += l.outputs*l.batch;
+        l.cell_cpu      += l.outputs*l.batch;
+
+        increment_layer(&wf, 1);
+        increment_layer(&wi, 1);
+        increment_layer(&wg, 1);
+        increment_layer(&wo, 1);
+
+        increment_layer(&uf, 1);
+        increment_layer(&ui, 1);
+        increment_layer(&ug, 1);
+        increment_layer(&uo, 1);
+    }
+}
+
+void backward_lstm_layer(layer l, network state)
+{
+    network s = { 0 };
+    s.train = state.train;
+    int i;
+    layer wf = *(l.wf);
+    layer wi = *(l.wi);
+    layer wg = *(l.wg);
+    layer wo = *(l.wo);
+
+    layer uf = *(l.uf);
+    layer ui = *(l.ui);
+    layer ug = *(l.ug);
+    layer uo = *(l.uo);
+
+    increment_layer(&wf, l.steps - 1);
+    increment_layer(&wi, l.steps - 1);
+    increment_layer(&wg, l.steps - 1);
+    increment_layer(&wo, l.steps - 1);
+
+    increment_layer(&uf, l.steps - 1);
+    increment_layer(&ui, l.steps - 1);
+    increment_layer(&ug, l.steps - 1);
+    increment_layer(&uo, l.steps - 1);
+
+    state.input += l.inputs*l.batch*(l.steps - 1);
+    if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
+
+    l.output += l.outputs*l.batch*(l.steps - 1);
+    l.cell_cpu += l.outputs*l.batch*(l.steps - 1);
+    l.delta += l.outputs*l.batch*(l.steps - 1);
+
+    for (i = l.steps - 1; i >= 0; --i) {
+        if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1);
+        copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1);
+        if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1);
+        copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1);
+
+        l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch;
+
+        copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);			
+        axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);			
+
+        copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);			
+        axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);			
+
+        copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);			
+        axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);			
+
+        copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);			
+        axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);			
+
+        activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);			
+        activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array(l.g_cpu, l.outputs*l.batch, TANH);			
+        activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);		
+
+        copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1);		
+
+        copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);			
+        activate_array(l.temp_cpu, l.outputs*l.batch, TANH);			
+
+        copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1);		
+        mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1);			
+
+        gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu);
+        axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1);		
+
+        copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);			
+        activate_array(l.temp_cpu, l.outputs*l.batch, TANH);			
+        mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1);		
+        gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1);
+        s.input = l.prev_state_cpu;
+        s.delta = l.dh_cpu;															
+        backward_connected_layer(wo, s);	
+
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1);
+        s.input = state.input;
+        s.delta = state.delta;
+        backward_connected_layer(uo, s);									
+
+        copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);			
+        mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);				
+        gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu);		
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1);
+        s.input = l.prev_state_cpu;
+        s.delta = l.dh_cpu;														
+        backward_connected_layer(wg, s);	
+
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1);
+        s.input = state.input;
+        s.delta = state.delta;
+        backward_connected_layer(ug, s);																
+
+        copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);			
+        mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);				
+        gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);	
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1);
+        s.input = l.prev_state_cpu;
+        s.delta = l.dh_cpu;
+        backward_connected_layer(wi, s);						
+
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1);
+        s.input = state.input;
+        s.delta = state.delta;
+        backward_connected_layer(ui, s);									
+
+        copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);		
+        mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1);
+        gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1);
+        s.input = l.prev_state_cpu;
+        s.delta = l.dh_cpu;
+        backward_connected_layer(wf, s);						
+
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1);
+        s.input = state.input;
+        s.delta = state.delta;
+        backward_connected_layer(uf, s);									
+
+        copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);			
+        mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1);				
+        copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1);				
+
+        state.input -= l.inputs*l.batch;
+        if (state.delta) state.delta -= l.inputs*l.batch;
+        l.output -= l.outputs*l.batch;
+        l.cell_cpu -= l.outputs*l.batch;
+        l.delta -= l.outputs*l.batch;
+
+        increment_layer(&wf, -1);
+        increment_layer(&wi, -1);
+        increment_layer(&wg, -1);
+        increment_layer(&wo, -1);
+
+        increment_layer(&uf, -1);
+        increment_layer(&ui, -1);
+        increment_layer(&ug, -1);
+        increment_layer(&uo, -1);
+    }
+}
+
+#ifdef GPU
+void update_lstm_layer_gpu(layer l, update_args a)
+{
+    update_connected_layer_gpu(*(l.wf), a);
+    update_connected_layer_gpu(*(l.wi), a);
+    update_connected_layer_gpu(*(l.wg), a);
+    update_connected_layer_gpu(*(l.wo), a);
+    update_connected_layer_gpu(*(l.uf), a);
+    update_connected_layer_gpu(*(l.ui), a);
+    update_connected_layer_gpu(*(l.ug), a);
+    update_connected_layer_gpu(*(l.uo), a);
+}
+
+void forward_lstm_layer_gpu(layer l, network state)
+{
+    network s = { 0 };
+    s.train = state.train;
+    int i;
+    layer wf = *(l.wf);
+    layer wi = *(l.wi);
+    layer wg = *(l.wg);
+    layer wo = *(l.wo);
+
+    layer uf = *(l.uf);
+    layer ui = *(l.ui);
+    layer ug = *(l.ug);
+    layer uo = *(l.uo);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
+    if (state.train) {
+        fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
+    }
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input_gpu = l.h_gpu;
+        forward_connected_layer_gpu(wf, s);							
+        forward_connected_layer_gpu(wi, s);							
+        forward_connected_layer_gpu(wg, s);							
+        forward_connected_layer_gpu(wo, s);							
+
+        s.input_gpu = state.input_gpu;
+        forward_connected_layer_gpu(uf, s);							
+        forward_connected_layer_gpu(ui, s);							
+        forward_connected_layer_gpu(ug, s);							
+        forward_connected_layer_gpu(uo, s);							
+
+        copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
+        axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
+
+        copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);	
+        axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);	
+
+        copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);	
+        axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);	
+
+        copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);	
+        axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);	
+
+        activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH);			
+        activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);		
+
+        copy_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);		
+        mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);		
+        mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);			
+        axpy_gpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);	
+
+        copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);			
+        activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH);		
+        mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);	
+
+        copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);		
+        copy_gpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
+
+        state.input_gpu += l.inputs*l.batch;
+        l.output_gpu    += l.outputs*l.batch;
+        l.cell_gpu      += l.outputs*l.batch;
+
+        increment_layer(&wf, 1);
+        increment_layer(&wi, 1);
+        increment_layer(&wg, 1);
+        increment_layer(&wo, 1);
+
+        increment_layer(&uf, 1);
+        increment_layer(&ui, 1);
+        increment_layer(&ug, 1);
+        increment_layer(&uo, 1);
+    }
+}
+
+void backward_lstm_layer_gpu(layer l, network state)
+{
+    network s = { 0 };
+    s.train = state.train;
+    int i;
+    layer wf = *(l.wf);
+    layer wi = *(l.wi);
+    layer wg = *(l.wg);
+    layer wo = *(l.wo);
+
+    layer uf = *(l.uf);
+    layer ui = *(l.ui);
+    layer ug = *(l.ug);
+    layer uo = *(l.uo);
+
+    increment_layer(&wf, l.steps - 1);
+    increment_layer(&wi, l.steps - 1);
+    increment_layer(&wg, l.steps - 1);
+    increment_layer(&wo, l.steps - 1);
+
+    increment_layer(&uf, l.steps - 1);
+    increment_layer(&ui, l.steps - 1);
+    increment_layer(&ug, l.steps - 1);
+    increment_layer(&uo, l.steps - 1);
+
+    state.input_gpu += l.inputs*l.batch*(l.steps - 1);
+    if (state.delta_gpu) state.delta_gpu += l.inputs*l.batch*(l.steps - 1);
+
+    l.output_gpu += l.outputs*l.batch*(l.steps - 1);
+    l.cell_gpu += l.outputs*l.batch*(l.steps - 1);
+    l.delta_gpu += l.outputs*l.batch*(l.steps - 1);
+
+    for (i = l.steps - 1; i >= 0; --i) {
+        if (i != 0) copy_gpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
+        copy_gpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
+        if (i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
+        copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1);
+
+        l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
+
+        copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);			
+        axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);			
+
+        copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);			
+        axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);			
+
+        copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);			
+        axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);			
+
+        copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);			
+        axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);			
+
+        activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);			
+        activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);		
+        activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH);			
+        activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);		
+
+        copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);		
+
+        copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);			
+        activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH);			
+
+        copy_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);		
+        mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);			
+
+        gradient_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
+        axpy_gpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);		
+
+        copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);			
+        activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH);			
+        mul_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);		
+        gradient_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
+        s.input_gpu = l.prev_state_gpu;
+        s.delta_gpu = l.dh_gpu;															
+        backward_connected_layer_gpu(wo, s);	
+
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
+        s.input_gpu = state.input_gpu;
+        s.delta_gpu = state.delta_gpu;
+        backward_connected_layer_gpu(uo, s);									
+
+        copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);			
+        mul_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);				
+        gradient_array_gpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);		
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
+        s.input_gpu = l.prev_state_gpu;
+        s.delta_gpu = l.dh_gpu;														
+        backward_connected_layer_gpu(wg, s);	
+
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
+        s.input_gpu = state.input_gpu;
+        s.delta_gpu = state.delta_gpu;
+        backward_connected_layer_gpu(ug, s);																
+
+        copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);			
+        mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);				
+        gradient_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);	
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
+        s.input_gpu = l.prev_state_gpu;
+        s.delta_gpu = l.dh_gpu;
+        backward_connected_layer_gpu(wi, s);						
+
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
+        s.input_gpu = state.input_gpu;
+        s.delta_gpu = state.delta_gpu;
+        backward_connected_layer_gpu(ui, s);									
+
+        copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);		
+        mul_gpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
+        gradient_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
+        s.input_gpu = l.prev_state_gpu;
+        s.delta_gpu = l.dh_gpu;
+        backward_connected_layer_gpu(wf, s);						
+
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
+        s.input_gpu = state.input_gpu;
+        s.delta_gpu = state.delta_gpu;
+        backward_connected_layer_gpu(uf, s);									
+
+        copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);			
+        mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);				
+        copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);				
+
+        state.input_gpu -= l.inputs*l.batch;
+        if (state.delta_gpu) state.delta_gpu -= l.inputs*l.batch;
+        l.output_gpu -= l.outputs*l.batch;
+        l.cell_gpu -= l.outputs*l.batch;
+        l.delta_gpu -= l.outputs*l.batch;
+
+        increment_layer(&wf, -1);
+        increment_layer(&wi, -1);
+        increment_layer(&wg, -1);
+        increment_layer(&wo, -1);
+
+        increment_layer(&uf, -1);
+        increment_layer(&ui, -1);
+        increment_layer(&ug, -1);
+        increment_layer(&uo, -1);
+    }
+}
+#endif
diff --git a/hanzi_detection/src/lstm_layer.h b/hanzi_detection/src/lstm_layer.h
new file mode 100755
index 0000000..b9f07e6
--- /dev/null
+++ b/hanzi_detection/src/lstm_layer.h
@@ -0,0 +1,20 @@
+#ifndef LSTM_LAYER_H
+#define LSTM_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+#define USET
+
+layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
+
+void forward_lstm_layer(layer l, network net); 
+void update_lstm_layer(layer l, update_args a);
+
+#ifdef GPU
+void forward_lstm_layer_gpu(layer l, network net);
+void backward_lstm_layer_gpu(layer l, network net);
+void update_lstm_layer_gpu(layer l, update_args a); 
+
+#endif
+#endif
diff --git a/hanzi_detection/src/matrix.c b/hanzi_detection/src/matrix.c
new file mode 100755
index 0000000..799916b
--- /dev/null
+++ b/hanzi_detection/src/matrix.c
@@ -0,0 +1,196 @@
+#include "matrix.h"
+#include "utils.h"
+#include "blas.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <assert.h>
+#include <math.h>
+
+void free_matrix(matrix m)
+{
+    int i;
+    for(i = 0; i < m.rows; ++i) free(m.vals[i]);
+    free(m.vals);
+}
+
+float matrix_topk_accuracy(matrix truth, matrix guess, int k)
+{
+    int *indexes = calloc(k, sizeof(int));
+    int n = truth.cols;
+    int i,j;
+    int correct = 0;
+    for(i = 0; i < truth.rows; ++i){
+        top_k(guess.vals[i], n, k, indexes);
+        for(j = 0; j < k; ++j){
+            int class = indexes[j];
+            if(truth.vals[i][class]){
+                ++correct;
+                break;
+            }
+        }
+    }
+    free(indexes);
+    return (float)correct/truth.rows;
+}
+
+void scale_matrix(matrix m, float scale)
+{
+    int i,j;
+    for(i = 0; i < m.rows; ++i){
+        for(j = 0; j < m.cols; ++j){
+            m.vals[i][j] *= scale;
+        }
+    }
+}
+
+matrix resize_matrix(matrix m, int size)
+{
+    int i;
+    if (m.rows == size) return m;
+    if (m.rows < size) {
+        m.vals = realloc(m.vals, size*sizeof(float*));
+        for (i = m.rows; i < size; ++i) {
+            m.vals[i] = calloc(m.cols, sizeof(float));
+        }
+    } else if (m.rows > size) {
+        for (i = size; i < m.rows; ++i) {
+            free(m.vals[i]);
+        }
+        m.vals = realloc(m.vals, size*sizeof(float*));
+    }
+    m.rows = size;
+    return m;
+}
+
+void matrix_add_matrix(matrix from, matrix to)
+{
+    assert(from.rows == to.rows && from.cols == to.cols);
+    int i,j;
+    for(i = 0; i < from.rows; ++i){
+        for(j = 0; j < from.cols; ++j){
+            to.vals[i][j] += from.vals[i][j];
+        }
+    }
+}
+
+matrix copy_matrix(matrix m)
+{
+    matrix c = {0};
+    c.rows = m.rows;
+    c.cols = m.cols;
+    c.vals = calloc(c.rows, sizeof(float *));
+    int i;
+    for(i = 0; i < c.rows; ++i){
+        c.vals[i] = calloc(c.cols, sizeof(float));
+        copy_cpu(c.cols, m.vals[i], 1, c.vals[i], 1);
+    }
+    return c;
+}
+
+matrix make_matrix(int rows, int cols)
+{
+    int i;
+    matrix m;
+    m.rows = rows;
+    m.cols = cols;
+    m.vals = calloc(m.rows, sizeof(float *));
+    for(i = 0; i < m.rows; ++i){
+        m.vals[i] = calloc(m.cols, sizeof(float));
+    }
+    return m;
+}
+
+matrix hold_out_matrix(matrix *m, int n)
+{
+    int i;
+    matrix h;
+    h.rows = n;
+    h.cols = m->cols;
+    h.vals = calloc(h.rows, sizeof(float *));
+    for(i = 0; i < n; ++i){
+        int index = rand()%m->rows;
+        h.vals[i] = m->vals[index];
+        m->vals[index] = m->vals[--(m->rows)];
+    }
+    return h;
+}
+
+float *pop_column(matrix *m, int c)
+{
+    float *col = calloc(m->rows, sizeof(float));
+    int i, j;
+    for(i = 0; i < m->rows; ++i){
+        col[i] = m->vals[i][c];
+        for(j = c; j < m->cols-1; ++j){
+            m->vals[i][j] = m->vals[i][j+1];
+        }
+    }
+    --m->cols;
+    return col;
+}
+
+matrix csv_to_matrix(char *filename)
+{
+    FILE *fp = fopen(filename, "r");
+    if(!fp) file_error(filename);
+
+    matrix m;
+    m.cols = -1;
+
+    char *line;
+
+    int n = 0;
+    int size = 1024;
+    m.vals = calloc(size, sizeof(float*));
+    while((line = fgetl(fp))){
+        if(m.cols == -1) m.cols = count_fields(line);
+        if(n == size){
+            size *= 2;
+            m.vals = realloc(m.vals, size*sizeof(float*));
+        }
+        m.vals[n] = parse_fields(line, m.cols);
+        free(line);
+        ++n;
+    }
+    m.vals = realloc(m.vals, n*sizeof(float*));
+    m.rows = n;
+    return m;
+}
+
+void matrix_to_csv(matrix m)
+{
+    int i, j;
+
+    for(i = 0; i < m.rows; ++i){
+        for(j = 0; j < m.cols; ++j){
+            if(j > 0) printf(",");
+            printf("%.17g", m.vals[i][j]);
+        }
+        printf("\n");
+    }
+}
+
+void print_matrix(matrix m)
+{
+    int i, j;
+    printf("%d X %d Matrix:\n",m.rows, m.cols);
+    printf(" __");
+    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+    printf("__ \n");
+
+    printf("|  ");
+    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+    printf("  |\n");
+
+    for(i = 0; i < m.rows; ++i){
+        printf("|  ");
+        for(j = 0; j < m.cols; ++j){
+            printf("%15.7f ", m.vals[i][j]);
+        }
+        printf(" |\n");
+    }
+    printf("|__");
+    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+    printf("__|\n");
+}
diff --git a/hanzi_detection/src/matrix.h b/hanzi_detection/src/matrix.h
new file mode 100755
index 0000000..879acd7
--- /dev/null
+++ b/hanzi_detection/src/matrix.h
@@ -0,0 +1,13 @@
+#ifndef MATRIX_H
+#define MATRIX_H
+#include "darknet.h"
+
+matrix copy_matrix(matrix m);
+void print_matrix(matrix m);
+
+matrix hold_out_matrix(matrix *m, int n);
+matrix resize_matrix(matrix m, int size);
+
+float *pop_column(matrix *m, int c);
+
+#endif
diff --git a/hanzi_detection/src/maxpool_layer.c b/hanzi_detection/src/maxpool_layer.c
new file mode 100755
index 0000000..fb05635
--- /dev/null
+++ b/hanzi_detection/src/maxpool_layer.c
@@ -0,0 +1,127 @@
+#include "maxpool_layer.h"
+#include "cuda.h"
+#include <stdio.h>
+
+image get_maxpool_image(maxpool_layer l)
+{
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.c;
+    return float_to_image(w,h,c,l.output);
+}
+
+image get_maxpool_delta(maxpool_layer l)
+{
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.c;
+    return float_to_image(w,h,c,l.delta);
+}
+
+maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding)
+{
+    maxpool_layer l = {0};
+    l.type = MAXPOOL;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.pad = padding;
+    l.out_w = (w + padding - size)/stride + 1;
+    l.out_h = (h + padding - size)/stride + 1;
+    l.out_c = c;
+    l.outputs = l.out_h * l.out_w * l.out_c;
+    l.inputs = h*w*c;
+    l.size = size;
+    l.stride = stride;
+    int output_size = l.out_h * l.out_w * l.out_c * batch;
+    l.indexes = calloc(output_size, sizeof(int));
+    l.output =  calloc(output_size, sizeof(float));
+    l.delta =   calloc(output_size, sizeof(float));
+    l.forward = forward_maxpool_layer;
+    l.backward = backward_maxpool_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_maxpool_layer_gpu;
+    l.backward_gpu = backward_maxpool_layer_gpu;
+    l.indexes_gpu = cuda_make_int_array(0, output_size);
+    l.output_gpu  = cuda_make_array(l.output, output_size);
+    l.delta_gpu   = cuda_make_array(l.delta, output_size);
+    #endif
+    fprintf(stderr, "max          %d x %d / %d  %4d x%4d x%4d   ->  %4d x%4d x%4d\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
+    return l;
+}
+
+void resize_maxpool_layer(maxpool_layer *l, int w, int h)
+{
+    l->h = h;
+    l->w = w;
+    l->inputs = h*w*l->c;
+
+    l->out_w = (w + l->pad - l->size)/l->stride + 1;
+    l->out_h = (h + l->pad - l->size)/l->stride + 1;
+    l->outputs = l->out_w * l->out_h * l->c;
+    int output_size = l->outputs * l->batch;
+
+    l->indexes = realloc(l->indexes, output_size * sizeof(int));
+    l->output = realloc(l->output, output_size * sizeof(float));
+    l->delta = realloc(l->delta, output_size * sizeof(float));
+
+    #ifdef GPU
+    cuda_free((float *)l->indexes_gpu);
+    cuda_free(l->output_gpu);
+    cuda_free(l->delta_gpu);
+    l->indexes_gpu = cuda_make_int_array(0, output_size);
+    l->output_gpu  = cuda_make_array(l->output, output_size);
+    l->delta_gpu   = cuda_make_array(l->delta,  output_size);
+    #endif
+}
+
+void forward_maxpool_layer(const maxpool_layer l, network net)
+{
+    int b,i,j,k,m,n;
+    int w_offset = -l.pad/2;
+    int h_offset = -l.pad/2;
+
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.c;
+
+    for(b = 0; b < l.batch; ++b){
+        for(k = 0; k < c; ++k){
+            for(i = 0; i < h; ++i){
+                for(j = 0; j < w; ++j){
+                    int out_index = j + w*(i + h*(k + c*b));
+                    float max = -FLT_MAX;
+                    int max_i = -1;
+                    for(n = 0; n < l.size; ++n){
+                        for(m = 0; m < l.size; ++m){
+                            int cur_h = h_offset + i*l.stride + n;
+                            int cur_w = w_offset + j*l.stride + m;
+                            int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c));
+                            int valid = (cur_h >= 0 && cur_h < l.h &&
+                                         cur_w >= 0 && cur_w < l.w);
+                            float val = (valid != 0) ? net.input[index] : -FLT_MAX;
+                            max_i = (val > max) ? index : max_i;
+                            max   = (val > max) ? val   : max;
+                        }
+                    }
+                    l.output[out_index] = max;
+                    l.indexes[out_index] = max_i;
+                }
+            }
+        }
+    }
+}
+
+void backward_maxpool_layer(const maxpool_layer l, network net)
+{
+    int i;
+    int h = l.out_h;
+    int w = l.out_w;
+    int c = l.c;
+    for(i = 0; i < h*w*c*l.batch; ++i){
+        int index = l.indexes[i];
+        net.delta[index] += l.delta[i];
+    }
+}
+
diff --git a/hanzi_detection/src/maxpool_layer.h b/hanzi_detection/src/maxpool_layer.h
new file mode 100755
index 0000000..ceb5190
--- /dev/null
+++ b/hanzi_detection/src/maxpool_layer.h
@@ -0,0 +1,23 @@
+#ifndef MAXPOOL_LAYER_H
+#define MAXPOOL_LAYER_H
+
+#include "image.h"
+#include "cuda.h"
+#include "layer.h"
+#include "network.h"
+
+typedef layer maxpool_layer;
+
+image get_maxpool_image(maxpool_layer l);
+maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding);
+void resize_maxpool_layer(maxpool_layer *l, int w, int h);
+void forward_maxpool_layer(const maxpool_layer l, network net);
+void backward_maxpool_layer(const maxpool_layer l, network net);
+
+#ifdef GPU
+void forward_maxpool_layer_gpu(maxpool_layer l, network net);
+void backward_maxpool_layer_gpu(maxpool_layer l, network net);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/maxpool_layer_kernels.cu b/hanzi_detection/src/maxpool_layer_kernels.cu
new file mode 100755
index 0000000..869ef46
--- /dev/null
+++ b/hanzi_detection/src/maxpool_layer_kernels.cu
@@ -0,0 +1,106 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
+extern "C" {
+#include "maxpool_layer.h"
+#include "cuda.h"
+}
+
+__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes)
+{
+    int h = (in_h + pad - size)/stride + 1;
+    int w = (in_w + pad - size)/stride + 1;
+    int c = in_c;
+
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int j = id % w;
+    id /= w;
+    int i = id % h;
+    id /= h;
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int w_offset = -pad/2;
+    int h_offset = -pad/2;
+
+    int out_index = j + w*(i + h*(k + c*b));
+    float max = -INFINITY;
+    int max_i = -1;
+    int l, m;
+    for(l = 0; l < size; ++l){
+        for(m = 0; m < size; ++m){
+            int cur_h = h_offset + i*stride + l;
+            int cur_w = w_offset + j*stride + m;
+            int index = cur_w + in_w*(cur_h + in_h*(k + b*in_c));
+            int valid = (cur_h >= 0 && cur_h < in_h &&
+                    cur_w >= 0 && cur_w < in_w);
+            float val = (valid != 0) ? input[index] : -INFINITY;
+            max_i = (val > max) ? index : max_i;
+            max   = (val > max) ? val   : max;
+        }
+    }
+    output[out_index] = max;
+    indexes[out_index] = max_i;
+}
+
+__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes)
+{
+    int h = (in_h + pad - size)/stride + 1;
+    int w = (in_w + pad - size)/stride + 1;
+    int c = in_c;
+    int area = (size-1)/stride;
+
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int index = id;
+    int j = id % in_w;
+    id /= in_w;
+    int i = id % in_h;
+    id /= in_h;
+    int k = id % in_c;
+    id /= in_c;
+    int b = id;
+
+    int w_offset = -pad/2;
+    int h_offset = -pad/2;
+
+    float d = 0;
+    int l, m;
+    for(l = -area; l < area+1; ++l){
+        for(m = -area; m < area+1; ++m){
+            int out_w = (j-w_offset)/stride + m;
+            int out_h = (i-h_offset)/stride + l;
+            int out_index = out_w + w*(out_h + h*(k + c*b));
+            int valid = (out_w >= 0 && out_w < w &&
+                     out_h >= 0 && out_h < h);
+            d += (valid && indexes[out_index] == index) ? delta[out_index] : 0;
+        }
+    }
+    prev_delta[index] += d;
+}
+
+extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network net)
+{
+    int h = layer.out_h;
+    int w = layer.out_w;
+    int c = layer.c;
+
+    size_t n = h*w*c*layer.batch;
+
+    forward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, net.input_gpu, layer.output_gpu, layer.indexes_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network net)
+{
+    size_t n = layer.h*layer.w*layer.c*layer.batch;
+
+    backward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, net.delta_gpu, layer.indexes_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
diff --git a/hanzi_detection/src/network.c b/hanzi_detection/src/network.c
new file mode 100755
index 0000000..aaab799
--- /dev/null
+++ b/hanzi_detection/src/network.c
@@ -0,0 +1,1129 @@
+#include <stdio.h>
+#include <time.h>
+#include <assert.h>
+#include "network.h"
+#include "image.h"
+#include "data.h"
+#include "utils.h"
+#include "blas.h"
+
+#include "crop_layer.h"
+#include "connected_layer.h"
+#include "gru_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
+#include "local_layer.h"
+#include "convolutional_layer.h"
+#include "activation_layer.h"
+#include "detection_layer.h"
+#include "region_layer.h"
+#include "yolo_layer.h"
+#include "normalization_layer.h"
+#include "batchnorm_layer.h"
+#include "maxpool_layer.h"
+#include "reorg_layer.h"
+#include "avgpool_layer.h"
+#include "cost_layer.h"
+#include "softmax_layer.h"
+#include "dropout_layer.h"
+#include "route_layer.h"
+#include "upsample_layer.h"
+#include "shortcut_layer.h"
+#include "parser.h"
+#include "data.h"
+
+load_args get_base_args(network *net)
+{
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.size = net->w;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.center = net->center;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    return args;
+}
+
+network *load_network(char *cfg, char *weights, int clear)
+{
+    network *net = parse_network_cfg(cfg);
+    if(weights && weights[0] != 0){
+        load_weights(net, weights);
+    }
+    if(clear) (*net->seen) = 0;
+    return net;
+}
+
+size_t get_current_batch(network *net)
+{
+    size_t batch_num = (*net->seen)/(net->batch*net->subdivisions);
+    return batch_num;
+}
+
+void reset_network_state(network *net, int b)
+{
+    int i;
+    for (i = 0; i < net->n; ++i) {
+        #ifdef GPU
+        layer l = net->layers[i];
+        if(l.state_gpu){
+            fill_gpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
+        }
+        if(l.h_gpu){
+            fill_gpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1);
+        }
+        #endif
+    }
+}
+
+void reset_rnn(network *net)
+{
+    reset_network_state(net, 0);
+}
+
+float get_current_rate(network *net)
+{
+    size_t batch_num = get_current_batch(net);
+    int i;
+    float rate;
+    if (batch_num < net->burn_in) return net->learning_rate * pow((float)batch_num / net->burn_in, net->power);
+    switch (net->policy) {
+        case CONSTANT:
+            return net->learning_rate;
+        case STEP:
+            return net->learning_rate * pow(net->scale, batch_num/net->step);
+        case STEPS:
+            rate = net->learning_rate;
+            for(i = 0; i < net->num_steps; ++i){
+                if(net->steps[i] > batch_num) return rate;
+                rate *= net->scales[i];
+            }
+            return rate;
+        case EXP:
+            return net->learning_rate * pow(net->gamma, batch_num);
+        case POLY:
+            return net->learning_rate * pow(1 - (float)batch_num / net->max_batches, net->power);
+        case RANDOM:
+            return net->learning_rate * pow(rand_uniform(0,1), net->power);
+        case SIG:
+            return net->learning_rate * (1./(1.+exp(net->gamma*(batch_num - net->step))));
+        default:
+            fprintf(stderr, "Policy is weird!\n");
+            return net->learning_rate;
+    }
+}
+
+char *get_layer_string(LAYER_TYPE a)
+{
+    switch(a){
+        case CONVOLUTIONAL:
+            return "convolutional";
+        case ACTIVE:
+            return "activation";
+        case LOCAL:
+            return "local";
+        case DECONVOLUTIONAL:
+            return "deconvolutional";
+        case CONNECTED:
+            return "connected";
+        case RNN:
+            return "rnn";
+        case GRU:
+            return "gru";
+        case LSTM:
+	    return "lstm";
+        case CRNN:
+            return "crnn";
+        case MAXPOOL:
+            return "maxpool";
+        case REORG:
+            return "reorg";
+        case AVGPOOL:
+            return "avgpool";
+        case SOFTMAX:
+            return "softmax";
+        case DETECTION:
+            return "detection";
+        case REGION:
+            return "region";
+        case YOLO:
+            return "yolo";
+        case DROPOUT:
+            return "dropout";
+        case CROP:
+            return "crop";
+        case COST:
+            return "cost";
+        case ROUTE:
+            return "route";
+        case SHORTCUT:
+            return "shortcut";
+        case NORMALIZATION:
+            return "normalization";
+        case BATCHNORM:
+            return "batchnorm";
+        default:
+            break;
+    }
+    return "none";
+}
+
+network *make_network(int n)
+{
+    network *net = calloc(1, sizeof(network));
+    net->n = n;
+    net->layers = calloc(net->n, sizeof(layer));
+    net->seen = calloc(1, sizeof(size_t));
+    net->t    = calloc(1, sizeof(int));
+    net->cost = calloc(1, sizeof(float));
+    return net;
+}
+
+void forward_network(network *netp)
+{
+#ifdef GPU
+    if(netp->gpu_index >= 0){
+        forward_network_gpu(netp);   
+        return;
+    }
+#endif
+    network net = *netp;
+    int i;
+    for(i = 0; i < net.n; ++i){
+        net.index = i;
+        layer l = net.layers[i];
+        if(l.delta){
+            fill_cpu(l.outputs * l.batch, 0, l.delta, 1);
+        }
+        l.forward(l, net);
+        net.input = l.output;
+        if(l.truth) {
+            net.truth = l.output;
+        }
+    }
+    calc_network_cost(netp);
+}
+
+void update_network(network *netp)
+{
+#ifdef GPU
+    if(netp->gpu_index >= 0){
+        update_network_gpu(netp);   
+        return;
+    }
+#endif
+    network net = *netp;
+    int i;
+    update_args a = {0};
+    a.batch = net.batch*net.subdivisions;
+    a.learning_rate = get_current_rate(netp);
+    a.momentum = net.momentum;
+    a.decay = net.decay;
+    a.adam = net.adam;
+    a.B1 = net.B1;
+    a.B2 = net.B2;
+    a.eps = net.eps;
+    ++*net.t;
+    a.t = *net.t;
+
+    for(i = 0; i < net.n; ++i){
+        layer l = net.layers[i];
+        if(l.update){
+            l.update(l, a);
+        }
+    }
+}
+
+void calc_network_cost(network *netp)
+{
+    network net = *netp;
+    int i;
+    float sum = 0;
+    int count = 0;
+    for(i = 0; i < net.n; ++i){
+        if(net.layers[i].cost){
+            sum += net.layers[i].cost[0];
+            ++count;
+        }
+    }
+    *net.cost = sum/count;
+}
+
+int get_predicted_class_network(network *net)
+{
+    return max_index(net->output, net->outputs);
+}
+
+void backward_network(network *netp)
+{
+#ifdef GPU
+    if(netp->gpu_index >= 0){
+        backward_network_gpu(netp);   
+        return;
+    }
+#endif
+    network net = *netp;
+    int i;
+    network orig = net;
+    for(i = net.n-1; i >= 0; --i){
+        layer l = net.layers[i];
+        if(l.stopbackward) break;
+        if(i == 0){
+            net = orig;
+        }else{
+            layer prev = net.layers[i-1];
+            net.input = prev.output;
+            net.delta = prev.delta;
+        }
+        net.index = i;
+        l.backward(l, net);
+    }
+}
+
+float train_network_datum(network *net)
+{
+    *net->seen += net->batch;
+    net->train = 1;
+    forward_network(net);
+    backward_network(net);
+    float error = *net->cost;
+    if(((*net->seen)/net->batch)%net->subdivisions == 0) update_network(net);
+    return error;
+}
+
+float train_network_sgd(network *net, data d, int n)
+{
+    int batch = net->batch;
+
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i){
+        get_random_batch(d, batch, net->input, net->truth);
+        float err = train_network_datum(net);
+        sum += err;
+    }
+    return (float)sum/(n*batch);
+}
+
+float train_network(network *net, data d)
+{
+    assert(d.X.rows % net->batch == 0);
+    int batch = net->batch;
+    int n = d.X.rows / batch;
+
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i){
+        get_next_batch(d, batch, i*batch, net->input, net->truth);
+        float err = train_network_datum(net);
+        sum += err;
+    }
+    return (float)sum/(n*batch);
+}
+
+void set_temp_network(network *net, float t)
+{
+    int i;
+    for(i = 0; i < net->n; ++i){
+        net->layers[i].temperature = t;
+    }
+}
+
+
+void set_batch_network(network *net, int b)
+{
+    net->batch = b;
+    int i;
+    for(i = 0; i < net->n; ++i){
+        net->layers[i].batch = b;
+#ifdef CUDNN
+        if(net->layers[i].type == CONVOLUTIONAL){
+            cudnn_convolutional_setup(net->layers + i);
+        }
+        if(net->layers[i].type == DECONVOLUTIONAL){
+            layer *l = net->layers + i;
+            cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, l->out_h, l->out_w);
+            cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); 
+        }
+#endif
+    }
+}
+
+int resize_network(network *net, int w, int h)
+{
+#ifdef GPU
+    cuda_set_device(net->gpu_index);
+    cuda_free(net->workspace);
+#endif
+    int i;
+    //if(w == net->w && h == net->h) return 0;
+    net->w = w;
+    net->h = h;
+    int inputs = 0;
+    size_t workspace_size = 0;
+    //fprintf(stderr, "Resizing to %d x %d...\n", w, h);
+    //fflush(stderr);
+    for (i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL){
+            resize_convolutional_layer(&l, w, h);
+        }else if(l.type == CROP){
+            resize_crop_layer(&l, w, h);
+        }else if(l.type == MAXPOOL){
+            resize_maxpool_layer(&l, w, h);
+        }else if(l.type == REGION){
+            resize_region_layer(&l, w, h);
+        }else if(l.type == YOLO){
+            resize_yolo_layer(&l, w, h);
+        }else if(l.type == ROUTE){
+            resize_route_layer(&l, net);
+        }else if(l.type == SHORTCUT){
+            resize_shortcut_layer(&l, w, h);
+        }else if(l.type == UPSAMPLE){
+            resize_upsample_layer(&l, w, h);
+        }else if(l.type == REORG){
+            resize_reorg_layer(&l, w, h);
+        }else if(l.type == AVGPOOL){
+            resize_avgpool_layer(&l, w, h);
+        }else if(l.type == NORMALIZATION){
+            resize_normalization_layer(&l, w, h);
+        }else if(l.type == COST){
+            resize_cost_layer(&l, inputs);
+        }else{
+            error("Cannot resize this type of layer");
+        }
+        if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
+        if(l.workspace_size > 2000000000) assert(0);
+        inputs = l.outputs;
+        net->layers[i] = l;
+        w = l.out_w;
+        h = l.out_h;
+        if(l.type == AVGPOOL) break;
+    }
+    layer out = get_network_output_layer(net);
+    net->inputs = net->layers[0].inputs;
+    net->outputs = out.outputs;
+    net->truths = out.outputs;
+    if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths;
+    net->output = out.output;
+    free(net->input);
+    free(net->truth);
+    net->input = calloc(net->inputs*net->batch, sizeof(float));
+    net->truth = calloc(net->truths*net->batch, sizeof(float));
+#ifdef GPU
+    if(gpu_index >= 0){
+        cuda_free(net->input_gpu);
+        cuda_free(net->truth_gpu);
+        net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch);
+        net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch);
+        if(workspace_size){
+            net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+        }
+    }else {
+        free(net->workspace);
+        net->workspace = calloc(1, workspace_size);
+    }
+#else
+    free(net->workspace);
+    net->workspace = calloc(1, workspace_size);
+#endif
+    //fprintf(stderr, " Done!\n");
+    return 0;
+}
+
+layer get_network_detection_layer(network *net)
+{
+    int i;
+    for(i = 0; i < net->n; ++i){
+        if(net->layers[i].type == DETECTION){
+            return net->layers[i];
+        }
+    }
+    fprintf(stderr, "Detection layer not found!!\n");
+    layer l = {0};
+    return l;
+}
+
+image get_network_image_layer(network *net, int i)
+{
+    layer l = net->layers[i];
+#ifdef GPU
+    //cuda_pull_array(l.output_gpu, l.output, l.outputs);
+#endif
+    if (l.out_w && l.out_h && l.out_c){
+        return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
+    }
+    image def = {0};
+    return def;
+}
+
+image get_network_image(network *net)
+{
+    int i;
+    for(i = net->n-1; i >= 0; --i){
+        image m = get_network_image_layer(net, i);
+        if(m.h != 0) return m;
+    }
+    image def = {0};
+    return def;
+}
+
+void visualize_network(network *net)
+{
+    image *prev = 0;
+    int i;
+    char buff[256];
+    for(i = 0; i < net->n; ++i){
+        sprintf(buff, "Layer %d", i);
+        layer l = net->layers[i];
+        if(l.type == CONVOLUTIONAL){
+            prev = visualize_convolutional_layer(l, buff, prev);
+        }
+    } 
+}
+
+void top_predictions(network *net, int k, int *index)
+{
+    top_k(net->output, net->outputs, k, index);
+}
+
+
+float *network_predict(network *net, float *input)
+{
+    network orig = *net;
+    net->input = input;
+    net->truth = 0;
+    net->train = 0;
+    net->delta = 0;
+    forward_network(net);
+    float *out = net->output;
+    *net = orig;
+    return out;
+}
+
+int num_detections(network *net, float thresh)
+{
+    int i;
+    int s = 0;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        if(l.type == YOLO){
+            s += yolo_num_detections(l, thresh);
+        }
+        if(l.type == DETECTION || l.type == REGION){
+            s += l.w*l.h*l.n;
+        }
+    }
+    return s;
+}
+
+detection *make_network_boxes(network *net, float thresh, int *num)
+{
+    layer l = net->layers[net->n - 1];
+    int i;
+    int nboxes = num_detections(net, thresh);
+    if(num) *num = nboxes;
+    detection *dets = calloc(nboxes, sizeof(detection));
+    for(i = 0; i < nboxes; ++i){
+        dets[i].prob = calloc(l.classes, sizeof(float));
+        if(l.coords > 4){
+            dets[i].mask = calloc(l.coords-4, sizeof(float));
+        }
+    }
+    return dets;
+}
+
+void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets)
+{
+    int j;
+    for(j = 0; j < net->n; ++j){
+        layer l = net->layers[j];
+        if(l.type == YOLO){
+            int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets);
+            dets += count;
+        }
+        if(l.type == REGION){
+            get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
+            dets += l.w*l.h*l.n;
+        }
+        if(l.type == DETECTION){
+            get_detection_detections(l, w, h, thresh, dets);
+            dets += l.w*l.h*l.n;
+        }
+    }
+}
+
+detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num)
+{
+    detection *dets = make_network_boxes(net, thresh, num);
+    fill_network_boxes(net, w, h, thresh, hier, map, relative, dets);
+    return dets;
+}
+
+void free_detections(detection *dets, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        free(dets[i].prob);
+        if(dets[i].mask) free(dets[i].mask);
+    }
+    free(dets);
+}
+
+float *network_predict_image(network *net, image im)
+{
+    image imr = letterbox_image(im, net->w, net->h);
+    set_batch_network(net, 1);
+    float *p = network_predict(net, imr.data);
+    free_image(imr);
+    return p;
+}
+
+int network_width(network *net){return net->w;}
+int network_height(network *net){return net->h;}
+
+matrix network_predict_data_multi(network *net, data test, int n)
+{
+    int i,j,b,m;
+    int k = net->outputs;
+    matrix pred = make_matrix(test.X.rows, k);
+    float *X = calloc(net->batch*test.X.rows, sizeof(float));
+    for(i = 0; i < test.X.rows; i += net->batch){
+        for(b = 0; b < net->batch; ++b){
+            if(i+b == test.X.rows) break;
+            memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+        }
+        for(m = 0; m < n; ++m){
+            float *out = network_predict(net, X);
+            for(b = 0; b < net->batch; ++b){
+                if(i+b == test.X.rows) break;
+                for(j = 0; j < k; ++j){
+                    pred.vals[i+b][j] += out[j+b*k]/n;
+                }
+            }
+        }
+    }
+    free(X);
+    return pred;   
+}
+
+matrix network_predict_data(network *net, data test)
+{
+    int i,j,b;
+    int k = net->outputs;
+    matrix pred = make_matrix(test.X.rows, k);
+    float *X = calloc(net->batch*test.X.cols, sizeof(float));
+    for(i = 0; i < test.X.rows; i += net->batch){
+        for(b = 0; b < net->batch; ++b){
+            if(i+b == test.X.rows) break;
+            memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+        }
+        float *out = network_predict(net, X);
+        for(b = 0; b < net->batch; ++b){
+            if(i+b == test.X.rows) break;
+            for(j = 0; j < k; ++j){
+                pred.vals[i+b][j] = out[j+b*k];
+            }
+        }
+    }
+    free(X);
+    return pred;   
+}
+
+void print_network(network *net)
+{
+    int i,j;
+    for(i = 0; i < net->n; ++i){
+        layer l = net->layers[i];
+        float *output = l.output;
+        int n = l.outputs;
+        float mean = mean_array(output, n);
+        float vari = variance_array(output, n);
+        fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
+        if(n > 100) n = 100;
+        for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
+        if(n == 100)fprintf(stderr,".....\n");
+        fprintf(stderr, "\n");
+    }
+}
+
+void compare_networks(network *n1, network *n2, data test)
+{
+    matrix g1 = network_predict_data(n1, test);
+    matrix g2 = network_predict_data(n2, test);
+    int i;
+    int a,b,c,d;
+    a = b = c = d = 0;
+    for(i = 0; i < g1.rows; ++i){
+        int truth = max_index(test.y.vals[i], test.y.cols);
+        int p1 = max_index(g1.vals[i], g1.cols);
+        int p2 = max_index(g2.vals[i], g2.cols);
+        if(p1 == truth){
+            if(p2 == truth) ++d;
+            else ++c;
+        }else{
+            if(p2 == truth) ++b;
+            else ++a;
+        }
+    }
+    printf("%5d %5d\n%5d %5d\n", a, b, c, d);
+    float num = pow((abs(b - c) - 1.), 2.);
+    float den = b + c;
+    printf("%f\n", num/den); 
+}
+
+float network_accuracy(network *net, data d)
+{
+    matrix guess = network_predict_data(net, d);
+    float acc = matrix_topk_accuracy(d.y, guess,1);
+    free_matrix(guess);
+    return acc;
+}
+
+float *network_accuracies(network *net, data d, int n)
+{
+    static float acc[2];
+    matrix guess = network_predict_data(net, d);
+    acc[0] = matrix_topk_accuracy(d.y, guess, 1);
+    acc[1] = matrix_topk_accuracy(d.y, guess, n);
+    free_matrix(guess);
+    return acc;
+}
+
+layer get_network_output_layer(network *net)
+{
+    int i;
+    for(i = net->n - 1; i >= 0; --i){
+        if(net->layers[i].type != COST) break;
+    }
+    return net->layers[i];
+}
+
+float network_accuracy_multi(network *net, data d, int n)
+{
+    matrix guess = network_predict_data_multi(net, d, n);
+    float acc = matrix_topk_accuracy(d.y, guess,1);
+    free_matrix(guess);
+    return acc;
+}
+
+void free_network(network *net)
+{
+    int i;
+    for(i = 0; i < net->n; ++i){
+        free_layer(net->layers[i]);
+    }
+    free(net->layers);
+    if(net->input) free(net->input);
+    if(net->truth) free(net->truth);
+#ifdef GPU
+    if(net->input_gpu) cuda_free(net->input_gpu);
+    if(net->truth_gpu) cuda_free(net->truth_gpu);
+#endif
+    free(net);
+}
+
+// Some day...
+// ^ What the hell is this comment for?
+
+
+layer network_output_layer(network *net)
+{
+    int i;
+    for(i = net->n - 1; i >= 0; --i){
+        if(net->layers[i].type != COST) break;
+    }
+    return net->layers[i];
+}
+
+int network_inputs(network *net)
+{
+    return net->layers[0].inputs;
+}
+
+int network_outputs(network *net)
+{
+    return network_output_layer(net).outputs;
+}
+
+float *network_output(network *net)
+{
+    return network_output_layer(net).output;
+}
+
+#ifdef GPU
+
+void forward_network_gpu(network *netp)
+{
+    network net = *netp;
+    cuda_set_device(net.gpu_index);
+    cuda_push_array(net.input_gpu, net.input, net.inputs*net.batch);
+    if(net.truth){
+        cuda_push_array(net.truth_gpu, net.truth, net.truths*net.batch);
+    }
+
+    int i;
+    for(i = 0; i < net.n; ++i){
+        net.index = i;
+        layer l = net.layers[i];
+        if(l.delta_gpu){
+            fill_gpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
+        }
+        l.forward_gpu(l, net);
+        net.input_gpu = l.output_gpu;
+        net.input = l.output;
+        if(l.truth) {
+            net.truth_gpu = l.output_gpu;
+            net.truth = l.output;
+        }
+    }
+    pull_network_output(netp);
+    calc_network_cost(netp);
+}
+
+void backward_network_gpu(network *netp)
+{
+    int i;
+    network net = *netp;
+    network orig = net;
+    cuda_set_device(net.gpu_index);
+    for(i = net.n-1; i >= 0; --i){
+        layer l = net.layers[i];
+        if(l.stopbackward) break;
+        if(i == 0){
+            net = orig;
+        }else{
+            layer prev = net.layers[i-1];
+            net.input = prev.output;
+            net.delta = prev.delta;
+            net.input_gpu = prev.output_gpu;
+            net.delta_gpu = prev.delta_gpu;
+        }
+        net.index = i;
+        l.backward_gpu(l, net);
+    }
+}
+
+void update_network_gpu(network *netp)
+{
+    network net = *netp;
+    cuda_set_device(net.gpu_index);
+    int i;
+    update_args a = {0};
+    a.batch = net.batch*net.subdivisions;
+    a.learning_rate = get_current_rate(netp);
+    a.momentum = net.momentum;
+    a.decay = net.decay;
+    a.adam = net.adam;
+    a.B1 = net.B1;
+    a.B2 = net.B2;
+    a.eps = net.eps;
+    ++*net.t;
+    a.t = (*net.t);
+
+    for(i = 0; i < net.n; ++i){
+        layer l = net.layers[i];
+        if(l.update_gpu){
+            l.update_gpu(l, a);
+        }
+    }
+}
+
+void harmless_update_network_gpu(network *netp)
+{
+    network net = *netp;
+    cuda_set_device(net.gpu_index);
+    int i;
+    for(i = 0; i < net.n; ++i){
+        layer l = net.layers[i];
+        if(l.weight_updates_gpu) fill_gpu(l.nweights, 0, l.weight_updates_gpu, 1);
+        if(l.bias_updates_gpu) fill_gpu(l.nbiases, 0, l.bias_updates_gpu, 1);
+        if(l.scale_updates_gpu) fill_gpu(l.nbiases, 0, l.scale_updates_gpu, 1);
+    }
+}
+
+typedef struct {
+    network *net;
+    data d;
+    float *err;
+} train_args;
+
+void *train_thread(void *ptr)
+{
+    train_args args = *(train_args*)ptr;
+    free(ptr);
+    cuda_set_device(args.net->gpu_index);
+    *args.err = train_network(args.net, args.d);
+    return 0;
+}
+
+pthread_t train_network_in_thread(network *net, data d, float *err)
+{
+    pthread_t thread;
+    train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
+    ptr->net = net;
+    ptr->d = d;
+    ptr->err = err;
+    if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
+    return thread;
+}
+
+void merge_weights(layer l, layer base)
+{
+    if (l.type == CONVOLUTIONAL) {
+        axpy_cpu(l.n, 1, l.bias_updates, 1, base.biases, 1);
+        axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weights, 1);
+        if (l.scales) {
+            axpy_cpu(l.n, 1, l.scale_updates, 1, base.scales, 1);
+        }
+    } else if(l.type == CONNECTED) {
+        axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.biases, 1);
+        axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weights, 1);
+    }
+}
+
+void scale_weights(layer l, float s)
+{
+    if (l.type == CONVOLUTIONAL) {
+        scal_cpu(l.n, s, l.biases, 1);
+        scal_cpu(l.nweights, s, l.weights, 1);
+        if (l.scales) {
+            scal_cpu(l.n, s, l.scales, 1);
+        }
+    } else if(l.type == CONNECTED) {
+        scal_cpu(l.outputs, s, l.biases, 1);
+        scal_cpu(l.outputs*l.inputs, s, l.weights, 1);
+    }
+}
+
+
+void pull_weights(layer l)
+{
+    if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
+        cuda_pull_array(l.biases_gpu, l.bias_updates, l.n);
+        cuda_pull_array(l.weights_gpu, l.weight_updates, l.nweights);
+        if(l.scales) cuda_pull_array(l.scales_gpu, l.scale_updates, l.n);
+    } else if(l.type == CONNECTED){
+        cuda_pull_array(l.biases_gpu, l.bias_updates, l.outputs);
+        cuda_pull_array(l.weights_gpu, l.weight_updates, l.outputs*l.inputs);
+    }
+}
+
+void push_weights(layer l)
+{
+    if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
+        cuda_push_array(l.biases_gpu, l.biases, l.n);
+        cuda_push_array(l.weights_gpu, l.weights, l.nweights);
+        if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n);
+    } else if(l.type == CONNECTED){
+        cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+        cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs);
+    }
+}
+
+void distribute_weights(layer l, layer base)
+{
+    if (l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL) {
+        cuda_push_array(l.biases_gpu, base.biases, l.n);
+        cuda_push_array(l.weights_gpu, base.weights, l.nweights);
+        if (base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n);
+    } else if (l.type == CONNECTED) {
+        cuda_push_array(l.biases_gpu, base.biases, l.outputs);
+        cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs);
+    }
+}
+
+
+/*
+
+   void pull_updates(layer l)
+   {
+   if(l.type == CONVOLUTIONAL){
+   cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
+   cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+   if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n);
+   } else if(l.type == CONNECTED){
+   cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+   cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
+   }
+   }
+
+   void push_updates(layer l)
+   {
+   if(l.type == CONVOLUTIONAL){
+   cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
+   cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+   if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n);
+   } else if(l.type == CONNECTED){
+   cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+   cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
+   }
+   }
+
+   void update_layer(layer l, network net)
+   {
+   int update_batch = net.batch*net.subdivisions;
+   float rate = get_current_rate(net);
+   l.t = get_current_batch(net);
+   if(l.update_gpu){
+   l.update_gpu(l, update_batch, rate*l.learning_rate_scale, net.momentum, net.decay);
+   }
+   }
+   void merge_updates(layer l, layer base)
+   {
+   if (l.type == CONVOLUTIONAL) {
+   axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1);
+   axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1);
+   if (l.scale_updates) {
+   axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1);
+   }
+   } else if(l.type == CONNECTED) {
+   axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1);
+   axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1);
+   }
+   }
+
+   void distribute_updates(layer l, layer base)
+   {
+   if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
+   cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n);
+   cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights);
+   if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n);
+   } else if(l.type == CONNECTED){
+   cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs);
+   cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs);
+   }
+   }
+ */
+
+/*
+   void sync_layer(network *nets, int n, int j)
+   {
+   int i;
+   network net = nets[0];
+   layer base = net.layers[j];
+   scale_weights(base, 0);
+   for (i = 0; i < n; ++i) {
+   cuda_set_device(nets[i].gpu_index);
+   layer l = nets[i].layers[j];
+   pull_weights(l);
+   merge_weights(l, base);
+   }
+   scale_weights(base, 1./n);
+   for (i = 0; i < n; ++i) {
+   cuda_set_device(nets[i].gpu_index);
+   layer l = nets[i].layers[j];
+   distribute_weights(l, base);
+   }
+   }
+ */
+
+void sync_layer(network **nets, int n, int j)
+{
+    int i;
+    network *net = nets[0];
+    layer base = net->layers[j];
+    scale_weights(base, 0);
+    for (i = 0; i < n; ++i) {
+        cuda_set_device(nets[i]->gpu_index);
+        layer l = nets[i]->layers[j];
+        pull_weights(l);
+        merge_weights(l, base);
+    }
+    scale_weights(base, 1./n);
+    for (i = 0; i < n; ++i) {
+        cuda_set_device(nets[i]->gpu_index);
+        layer l = nets[i]->layers[j];
+        distribute_weights(l, base);
+    }
+}
+
+typedef struct{
+    network **nets;
+    int n;
+    int j;
+} sync_args;
+
+void *sync_layer_thread(void *ptr)
+{
+    sync_args args = *(sync_args*)ptr;
+    sync_layer(args.nets, args.n, args.j);
+    free(ptr);
+    return 0;
+}
+
+pthread_t sync_layer_in_thread(network **nets, int n, int j)
+{
+    pthread_t thread;
+    sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args));
+    ptr->nets = nets;
+    ptr->n = n;
+    ptr->j = j;
+    if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed");
+    return thread;
+}
+
+void sync_nets(network **nets, int n, int interval)
+{
+    int j;
+    int layers = nets[0]->n;
+    pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t));
+
+    *(nets[0]->seen) += interval * (n-1) * nets[0]->batch * nets[0]->subdivisions;
+    for (j = 0; j < n; ++j){
+        *(nets[j]->seen) = *(nets[0]->seen);
+    }
+    for (j = 0; j < layers; ++j) {
+        threads[j] = sync_layer_in_thread(nets, n, j);
+    }
+    for (j = 0; j < layers; ++j) {
+        pthread_join(threads[j], 0);
+    }
+    free(threads);
+}
+
+float train_networks(network **nets, int n, data d, int interval)
+{
+    int i;
+    int batch = nets[0]->batch;
+    int subdivisions = nets[0]->subdivisions;
+    assert(batch * subdivisions * n == d.X.rows);
+    pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t));
+    float *errors = (float *) calloc(n, sizeof(float));
+
+    float sum = 0;
+    for(i = 0; i < n; ++i){
+        data p = get_data_part(d, i, n);
+        threads[i] = train_network_in_thread(nets[i], p, errors + i);
+    }
+    for(i = 0; i < n; ++i){
+        pthread_join(threads[i], 0);
+        //printf("%f\n", errors[i]);
+        sum += errors[i];
+    }
+    //cudaDeviceSynchronize();
+    if (get_current_batch(nets[0]) % interval == 0) {
+        printf("Syncing... ");
+        fflush(stdout);
+        sync_nets(nets, n, interval);
+        printf("Done!\n");
+    }
+    //cudaDeviceSynchronize();
+    free(threads);
+    free(errors);
+    return (float)sum/(n);
+}
+
+void pull_network_output(network *net)
+{
+    layer l = get_network_output_layer(net);
+    cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
+}
+
+#endif
diff --git a/hanzi_detection/src/network.h b/hanzi_detection/src/network.h
new file mode 100755
index 0000000..1b0dfd1
--- /dev/null
+++ b/hanzi_detection/src/network.h
@@ -0,0 +1,29 @@
+// Oh boy, why am I about to do this....
+#ifndef NETWORK_H
+#define NETWORK_H
+#include "darknet.h"
+
+#include "image.h"
+#include "layer.h"
+#include "data.h"
+#include "tree.h"
+
+
+#ifdef GPU
+void pull_network_output(network *net);
+#endif
+
+void compare_networks(network *n1, network *n2, data d);
+char *get_layer_string(LAYER_TYPE a);
+
+network *make_network(int n);
+
+
+float network_accuracy_multi(network *net, data d, int n);
+int get_predicted_class_network(network *net);
+void print_network(network *net);
+int resize_network(network *net, int w, int h);
+void calc_network_cost(network *net);
+
+#endif
+
diff --git a/hanzi_detection/src/normalization_layer.c b/hanzi_detection/src/normalization_layer.c
new file mode 100755
index 0000000..424714f
--- /dev/null
+++ b/hanzi_detection/src/normalization_layer.c
@@ -0,0 +1,151 @@
+#include "normalization_layer.h"
+#include "blas.h"
+
+#include <stdio.h>
+
+layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa)
+{
+    fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", w,h,c,size);
+    layer layer = {0};
+    layer.type = NORMALIZATION;
+    layer.batch = batch;
+    layer.h = layer.out_h = h;
+    layer.w = layer.out_w = w;
+    layer.c = layer.out_c = c;
+    layer.kappa = kappa;
+    layer.size = size;
+    layer.alpha = alpha;
+    layer.beta = beta;
+    layer.output = calloc(h * w * c * batch, sizeof(float));
+    layer.delta = calloc(h * w * c * batch, sizeof(float));
+    layer.squared = calloc(h * w * c * batch, sizeof(float));
+    layer.norms = calloc(h * w * c * batch, sizeof(float));
+    layer.inputs = w*h*c;
+    layer.outputs = layer.inputs;
+
+    layer.forward = forward_normalization_layer;
+    layer.backward = backward_normalization_layer;
+    #ifdef GPU
+    layer.forward_gpu = forward_normalization_layer_gpu;
+    layer.backward_gpu = backward_normalization_layer_gpu;
+
+    layer.output_gpu =  cuda_make_array(layer.output, h * w * c * batch);
+    layer.delta_gpu =   cuda_make_array(layer.delta, h * w * c * batch);
+    layer.squared_gpu = cuda_make_array(layer.squared, h * w * c * batch);
+    layer.norms_gpu =   cuda_make_array(layer.norms, h * w * c * batch);
+    #endif
+    return layer;
+}
+
+void resize_normalization_layer(layer *layer, int w, int h)
+{
+    int c = layer->c;
+    int batch = layer->batch;
+    layer->h = h;
+    layer->w = w;
+    layer->out_h = h;
+    layer->out_w = w;
+    layer->inputs = w*h*c;
+    layer->outputs = layer->inputs;
+    layer->output = realloc(layer->output, h * w * c * batch * sizeof(float));
+    layer->delta = realloc(layer->delta, h * w * c * batch * sizeof(float));
+    layer->squared = realloc(layer->squared, h * w * c * batch * sizeof(float));
+    layer->norms = realloc(layer->norms, h * w * c * batch * sizeof(float));
+#ifdef GPU
+    cuda_free(layer->output_gpu);
+    cuda_free(layer->delta_gpu); 
+    cuda_free(layer->squared_gpu); 
+    cuda_free(layer->norms_gpu);   
+    layer->output_gpu =  cuda_make_array(layer->output, h * w * c * batch);
+    layer->delta_gpu =   cuda_make_array(layer->delta, h * w * c * batch);
+    layer->squared_gpu = cuda_make_array(layer->squared, h * w * c * batch);
+    layer->norms_gpu =   cuda_make_array(layer->norms, h * w * c * batch);
+#endif
+}
+
+void forward_normalization_layer(const layer layer, network net)
+{
+    int k,b;
+    int w = layer.w;
+    int h = layer.h;
+    int c = layer.c;
+    scal_cpu(w*h*c*layer.batch, 0, layer.squared, 1);
+
+    for(b = 0; b < layer.batch; ++b){
+        float *squared = layer.squared + w*h*c*b;
+        float *norms   = layer.norms + w*h*c*b;
+        float *input   = net.input + w*h*c*b;
+        pow_cpu(w*h*c, 2, input, 1, squared, 1);
+
+        const_cpu(w*h, layer.kappa, norms, 1);
+        for(k = 0; k < layer.size/2; ++k){
+            axpy_cpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1);
+        }
+
+        for(k = 1; k < layer.c; ++k){
+            copy_cpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1);
+            int prev = k - ((layer.size-1)/2) - 1;
+            int next = k + (layer.size/2);
+            if(prev >= 0)      axpy_cpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1);
+            if(next < layer.c) axpy_cpu(w*h,  layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1);
+        }
+    }
+    pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1);
+    mul_cpu(w*h*c*layer.batch, net.input, 1, layer.output, 1);
+}
+
+void backward_normalization_layer(const layer layer, network net)
+{
+    // TODO This is approximate ;-)
+    // Also this should add in to delta instead of overwritting.
+
+    int w = layer.w;
+    int h = layer.h;
+    int c = layer.c;
+    pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, net.delta, 1);
+    mul_cpu(w*h*c*layer.batch, layer.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+void forward_normalization_layer_gpu(const layer layer, network net)
+{
+    int k,b;
+    int w = layer.w;
+    int h = layer.h;
+    int c = layer.c;
+    scal_gpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1);
+
+    for(b = 0; b < layer.batch; ++b){
+        float *squared = layer.squared_gpu + w*h*c*b;
+        float *norms   = layer.norms_gpu + w*h*c*b;
+        float *input   = net.input_gpu + w*h*c*b;
+        pow_gpu(w*h*c, 2, input, 1, squared, 1);
+
+        const_gpu(w*h, layer.kappa, norms, 1);
+        for(k = 0; k < layer.size/2; ++k){
+            axpy_gpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1);
+        }
+
+        for(k = 1; k < layer.c; ++k){
+            copy_gpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1);
+            int prev = k - ((layer.size-1)/2) - 1;
+            int next = k + (layer.size/2);
+            if(prev >= 0)      axpy_gpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1);
+            if(next < layer.c) axpy_gpu(w*h,  layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1);
+        }
+    }
+    pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1);
+    mul_gpu(w*h*c*layer.batch, net.input_gpu, 1, layer.output_gpu, 1);
+}
+
+void backward_normalization_layer_gpu(const layer layer, network net)
+{
+    // TODO This is approximate ;-)
+
+    int w = layer.w;
+    int h = layer.h;
+    int c = layer.c;
+    pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, net.delta_gpu, 1);
+    mul_gpu(w*h*c*layer.batch, layer.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
diff --git a/hanzi_detection/src/normalization_layer.h b/hanzi_detection/src/normalization_layer.h
new file mode 100755
index 0000000..665baa5
--- /dev/null
+++ b/hanzi_detection/src/normalization_layer.h
@@ -0,0 +1,19 @@
+#ifndef NORMALIZATION_LAYER_H
+#define NORMALIZATION_LAYER_H
+
+#include "image.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa);
+void resize_normalization_layer(layer *layer, int h, int w);
+void forward_normalization_layer(const layer layer, network net);
+void backward_normalization_layer(const layer layer, network net);
+void visualize_normalization_layer(layer layer, char *window);
+
+#ifdef GPU
+void forward_normalization_layer_gpu(const layer layer, network net);
+void backward_normalization_layer_gpu(const layer layer, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/option_list.c b/hanzi_detection/src/option_list.c
new file mode 100755
index 0000000..2f52781
--- /dev/null
+++ b/hanzi_detection/src/option_list.c
@@ -0,0 +1,140 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include "option_list.h"
+#include "utils.h"
+
+list *read_data_cfg(char *filename)
+{
+    FILE *file = fopen(filename, "r");
+    if(file == 0) file_error(filename);
+    char *line;
+    int nu = 0;
+    list *options = make_list();
+    while((line=fgetl(file)) != 0){
+        ++ nu;
+        strip(line);
+        switch(line[0]){
+            case '\0':
+            case '#':
+            case ';':
+                free(line);
+                break;
+            default:
+                if(!read_option(line, options)){
+                    fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
+                    free(line);
+                }
+                break;
+        }
+    }
+    fclose(file);
+    return options;
+}
+
+metadata get_metadata(char *file)
+{
+    metadata m = {0};
+    list *options = read_data_cfg(file);
+
+    char *name_list = option_find_str(options, "names", 0);
+    if(!name_list) name_list = option_find_str(options, "labels", 0);
+    if(!name_list) {
+        fprintf(stderr, "No names or labels found\n");
+    } else {
+        m.names = get_labels(name_list);
+    }
+    m.classes = option_find_int(options, "classes", 2);
+    free_list(options);
+    return m;
+}
+
+int read_option(char *s, list *options)
+{
+    size_t i;
+    size_t len = strlen(s);
+    char *val = 0;
+    for(i = 0; i < len; ++i){
+        if(s[i] == '='){
+            s[i] = '\0';
+            val = s+i+1;
+            break;
+        }
+    }
+    if(i == len-1) return 0;
+    char *key = s;
+    option_insert(options, key, val);
+    return 1;
+}
+
+void option_insert(list *l, char *key, char *val)
+{
+    kvp *p = malloc(sizeof(kvp));
+    p->key = key;
+    p->val = val;
+    p->used = 0;
+    list_insert(l, p);
+}
+
+void option_unused(list *l)
+{
+    node *n = l->front;
+    while(n){
+        kvp *p = (kvp *)n->val;
+        if(!p->used){
+            fprintf(stderr, "Unused field: '%s = %s'\n", p->key, p->val);
+        }
+        n = n->next;
+    }
+}
+
+char *option_find(list *l, char *key)
+{
+    node *n = l->front;
+    while(n){
+        kvp *p = (kvp *)n->val;
+        if(strcmp(p->key, key) == 0){
+            p->used = 1;
+            return p->val;
+        }
+        n = n->next;
+    }
+    return 0;
+}
+char *option_find_str(list *l, char *key, char *def)
+{
+    char *v = option_find(l, key);
+    if(v) return v;
+    if(def) fprintf(stderr, "%s: Using default '%s'\n", key, def);
+    return def;
+}
+
+int option_find_int(list *l, char *key, int def)
+{
+    char *v = option_find(l, key);
+    if(v) return atoi(v);
+    fprintf(stderr, "%s: Using default '%d'\n", key, def);
+    return def;
+}
+
+int option_find_int_quiet(list *l, char *key, int def)
+{
+    char *v = option_find(l, key);
+    if(v) return atoi(v);
+    return def;
+}
+
+float option_find_float_quiet(list *l, char *key, float def)
+{
+    char *v = option_find(l, key);
+    if(v) return atof(v);
+    return def;
+}
+
+float option_find_float(list *l, char *key, float def)
+{
+    char *v = option_find(l, key);
+    if(v) return atof(v);
+    fprintf(stderr, "%s: Using default '%lf'\n", key, def);
+    return def;
+}
diff --git a/hanzi_detection/src/option_list.h b/hanzi_detection/src/option_list.h
new file mode 100755
index 0000000..844bd87
--- /dev/null
+++ b/hanzi_detection/src/option_list.h
@@ -0,0 +1,19 @@
+#ifndef OPTION_LIST_H
+#define OPTION_LIST_H
+#include "list.h"
+
+typedef struct{
+    char *key;
+    char *val;
+    int used;
+} kvp;
+
+
+int read_option(char *s, list *options);
+void option_insert(list *l, char *key, char *val);
+char *option_find(list *l, char *key);
+float option_find_float(list *l, char *key, float def);
+float option_find_float_quiet(list *l, char *key, float def);
+void option_unused(list *l);
+
+#endif
diff --git a/hanzi_detection/src/parser.c b/hanzi_detection/src/parser.c
new file mode 100755
index 0000000..c8141c9
--- /dev/null
+++ b/hanzi_detection/src/parser.c
@@ -0,0 +1,1312 @@
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <assert.h>
+
+#include "activation_layer.h"
+#include "logistic_layer.h"
+#include "l2norm_layer.h"
+#include "activations.h"
+#include "avgpool_layer.h"
+#include "batchnorm_layer.h"
+#include "blas.h"
+#include "connected_layer.h"
+#include "deconvolutional_layer.h"
+#include "convolutional_layer.h"
+#include "cost_layer.h"
+#include "crnn_layer.h"
+#include "crop_layer.h"
+#include "detection_layer.h"
+#include "dropout_layer.h"
+#include "gru_layer.h"
+#include "list.h"
+#include "local_layer.h"
+#include "maxpool_layer.h"
+#include "normalization_layer.h"
+#include "option_list.h"
+#include "parser.h"
+#include "region_layer.h"
+#include "yolo_layer.h"
+#include "iseg_layer.h"
+#include "reorg_layer.h"
+#include "rnn_layer.h"
+#include "route_layer.h"
+#include "upsample_layer.h"
+#include "shortcut_layer.h"
+#include "softmax_layer.h"
+#include "lstm_layer.h"
+#include "utils.h"
+
+typedef struct{
+    char *type;
+    list *options;
+}section;
+
+list *read_cfg(char *filename);
+
+LAYER_TYPE string_to_layer_type(char * type)
+{
+
+    if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
+    if (strcmp(type, "[crop]")==0) return CROP;
+    if (strcmp(type, "[cost]")==0) return COST;
+    if (strcmp(type, "[detection]")==0) return DETECTION;
+    if (strcmp(type, "[region]")==0) return REGION;
+    if (strcmp(type, "[yolo]")==0) return YOLO;
+    if (strcmp(type, "[iseg]")==0) return ISEG;
+    if (strcmp(type, "[local]")==0) return LOCAL;
+    if (strcmp(type, "[conv]")==0
+            || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
+    if (strcmp(type, "[deconv]")==0
+            || strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL;
+    if (strcmp(type, "[activation]")==0) return ACTIVE;
+    if (strcmp(type, "[logistic]")==0) return LOGXENT;
+    if (strcmp(type, "[l2norm]")==0) return L2NORM;
+    if (strcmp(type, "[net]")==0
+            || strcmp(type, "[network]")==0) return NETWORK;
+    if (strcmp(type, "[crnn]")==0) return CRNN;
+    if (strcmp(type, "[gru]")==0) return GRU;
+    if (strcmp(type, "[lstm]") == 0) return LSTM;
+    if (strcmp(type, "[rnn]")==0) return RNN;
+    if (strcmp(type, "[conn]")==0
+            || strcmp(type, "[connected]")==0) return CONNECTED;
+    if (strcmp(type, "[max]")==0
+            || strcmp(type, "[maxpool]")==0) return MAXPOOL;
+    if (strcmp(type, "[reorg]")==0) return REORG;
+    if (strcmp(type, "[avg]")==0
+            || strcmp(type, "[avgpool]")==0) return AVGPOOL;
+    if (strcmp(type, "[dropout]")==0) return DROPOUT;
+    if (strcmp(type, "[lrn]")==0
+            || strcmp(type, "[normalization]")==0) return NORMALIZATION;
+    if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
+    if (strcmp(type, "[soft]")==0
+            || strcmp(type, "[softmax]")==0) return SOFTMAX;
+    if (strcmp(type, "[route]")==0) return ROUTE;
+    if (strcmp(type, "[upsample]")==0) return UPSAMPLE;
+    return BLANK;
+}
+
+void free_section(section *s)
+{
+    free(s->type);
+    node *n = s->options->front;
+    while(n){
+        kvp *pair = (kvp *)n->val;
+        free(pair->key);
+        free(pair);
+        node *next = n->next;
+        free(n);
+        n = next;
+    }
+    free(s->options);
+    free(s);
+}
+
+void parse_data(char *data, float *a, int n)
+{
+    int i;
+    if(!data) return;
+    char *curr = data;
+    char *next = data;
+    int done = 0;
+    for(i = 0; i < n && !done; ++i){
+        while(*++next !='\0' && *next != ',');
+        if(*next == '\0') done = 1;
+        *next = '\0';
+        sscanf(curr, "%g", &a[i]);
+        curr = next+1;
+    }
+}
+
+typedef struct size_params{
+    int batch;
+    int inputs;
+    int h;
+    int w;
+    int c;
+    int index;
+    int time_steps;
+    network *net;
+} size_params;
+
+local_layer parse_local(list *options, size_params params)
+{
+    int n = option_find_int(options, "filters",1);
+    int size = option_find_int(options, "size",1);
+    int stride = option_find_int(options, "stride",1);
+    int pad = option_find_int(options, "pad",0);
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before local layer must output image.");
+
+    local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
+
+    return layer;
+}
+
+layer parse_deconvolutional(list *options, size_params params)
+{
+    int n = option_find_int(options, "filters",1);
+    int size = option_find_int(options, "size",1);
+    int stride = option_find_int(options, "stride",1);
+
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+    int pad = option_find_int_quiet(options, "pad",0);
+    int padding = option_find_int_quiet(options, "padding",0);
+    if(pad) padding = size/2;
+
+    layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam);
+
+    return l;
+}
+
+
+convolutional_layer parse_convolutional(list *options, size_params params)
+{
+    int n = option_find_int(options, "filters",1);
+    int size = option_find_int(options, "size",1);
+    int stride = option_find_int(options, "stride",1);
+    int pad = option_find_int_quiet(options, "pad",0);
+    int padding = option_find_int_quiet(options, "padding",0);
+    int groups = option_find_int_quiet(options, "groups", 1);
+    if(pad) padding = size/2;
+
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before convolutional layer must output image.");
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+    int binary = option_find_int_quiet(options, "binary", 0);
+    int xnor = option_find_int_quiet(options, "xnor", 0);
+
+    convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam);
+    layer.flipped = option_find_int_quiet(options, "flipped", 0);
+    layer.dot = option_find_float_quiet(options, "dot", 0);
+
+    return layer;
+}
+
+layer parse_crnn(list *options, size_params params)
+{
+    int output_filters = option_find_int(options, "output_filters",1);
+    int hidden_filters = option_find_int(options, "hidden_filters",1);
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+    layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
+
+    l.shortcut = option_find_int_quiet(options, "shortcut", 0);
+
+    return l;
+}
+
+layer parse_rnn(list *options, size_params params)
+{
+    int output = option_find_int(options, "output",1);
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+    layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam);
+
+    l.shortcut = option_find_int_quiet(options, "shortcut", 0);
+
+    return l;
+}
+
+layer parse_gru(list *options, size_params params)
+{
+    int output = option_find_int(options, "output",1);
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+    layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
+    l.tanh = option_find_int_quiet(options, "tanh", 0);
+
+    return l;
+}
+
+layer parse_lstm(list *options, size_params params)
+{
+    int output = option_find_int(options, "output", 1);
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+    layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
+
+    return l;
+}
+
+layer parse_connected(list *options, size_params params)
+{
+    int output = option_find_int(options, "output",1);
+    char *activation_s = option_find_str(options, "activation", "logistic");
+    ACTIVATION activation = get_activation(activation_s);
+    int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+    layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam);
+    return l;
+}
+
+layer parse_softmax(list *options, size_params params)
+{
+    int groups = option_find_int_quiet(options, "groups",1);
+    layer l = make_softmax_layer(params.batch, params.inputs, groups);
+    l.temperature = option_find_float_quiet(options, "temperature", 1);
+    char *tree_file = option_find_str(options, "tree", 0);
+    if (tree_file) l.softmax_tree = read_tree(tree_file);
+    l.w = params.w;
+    l.h = params.h;
+    l.c = params.c;
+    l.spatial = option_find_float_quiet(options, "spatial", 0);
+    l.noloss =  option_find_int_quiet(options, "noloss", 0);
+    return l;
+}
+
+int *parse_yolo_mask(char *a, int *num)
+{
+    int *mask = 0;
+    if(a){
+        int len = strlen(a);
+        int n = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (a[i] == ',') ++n;
+        }
+        mask = calloc(n, sizeof(int));
+        for(i = 0; i < n; ++i){
+            int val = atoi(a);
+            mask[i] = val;
+            a = strchr(a, ',')+1;
+        }
+        *num = n;
+    }
+    return mask;
+}
+
+layer parse_yolo(list *options, size_params params)
+{
+    int classes = option_find_int(options, "classes", 20);
+    int total = option_find_int(options, "num", 1);
+    int num = total;
+
+    char *a = option_find_str(options, "mask", 0);
+    int *mask = parse_yolo_mask(a, &num);
+    layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes);
+    assert(l.outputs == params.inputs);
+
+    l.max_boxes = option_find_int_quiet(options, "max",90);
+    l.jitter = option_find_float(options, "jitter", .2);
+
+    l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
+    l.truth_thresh = option_find_float(options, "truth_thresh", 1);
+    l.random = option_find_int_quiet(options, "random", 0);
+
+    char *map_file = option_find_str(options, "map", 0);
+    if (map_file) l.map = read_map(map_file);
+
+    a = option_find_str(options, "anchors", 0);
+    if(a){
+        int len = strlen(a);
+        int n = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (a[i] == ',') ++n;
+        }
+        for(i = 0; i < n; ++i){
+            float bias = atof(a);
+            l.biases[i] = bias;
+            a = strchr(a, ',')+1;
+        }
+    }
+    return l;
+}
+
+layer parse_iseg(list *options, size_params params)
+{
+    int classes = option_find_int(options, "classes", 20);
+    int ids = option_find_int(options, "ids", 32);
+    layer l = make_iseg_layer(params.batch, params.w, params.h, classes, ids);
+    assert(l.outputs == params.inputs);
+    return l;
+}
+
+layer parse_region(list *options, size_params params)
+{
+    int coords = option_find_int(options, "coords", 4);
+    int classes = option_find_int(options, "classes", 20);
+    int num = option_find_int(options, "num", 1);
+
+    layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
+    assert(l.outputs == params.inputs);
+
+    l.log = option_find_int_quiet(options, "log", 0);
+    l.sqrt = option_find_int_quiet(options, "sqrt", 0);
+
+    l.softmax = option_find_int(options, "softmax", 0);
+    l.background = option_find_int_quiet(options, "background", 0);
+    l.max_boxes = option_find_int_quiet(options, "max",30);
+    l.jitter = option_find_float(options, "jitter", .2);
+    l.rescore = option_find_int_quiet(options, "rescore",0);
+
+    l.thresh = option_find_float(options, "thresh", .5);
+    l.classfix = option_find_int_quiet(options, "classfix", 0);
+    l.absolute = option_find_int_quiet(options, "absolute", 0);
+    l.random = option_find_int_quiet(options, "random", 0);
+
+    l.coord_scale = option_find_float(options, "coord_scale", 1);
+    l.object_scale = option_find_float(options, "object_scale", 1);
+    l.noobject_scale = option_find_float(options, "noobject_scale", 1);
+    l.mask_scale = option_find_float(options, "mask_scale", 1);
+    l.class_scale = option_find_float(options, "class_scale", 1);
+    l.bias_match = option_find_int_quiet(options, "bias_match",0);
+
+    char *tree_file = option_find_str(options, "tree", 0);
+    if (tree_file) l.softmax_tree = read_tree(tree_file);
+    char *map_file = option_find_str(options, "map", 0);
+    if (map_file) l.map = read_map(map_file);
+
+    char *a = option_find_str(options, "anchors", 0);
+    if(a){
+        int len = strlen(a);
+        int n = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (a[i] == ',') ++n;
+        }
+        for(i = 0; i < n; ++i){
+            float bias = atof(a);
+            l.biases[i] = bias;
+            a = strchr(a, ',')+1;
+        }
+    }
+    return l;
+}
+
+detection_layer parse_detection(list *options, size_params params)
+{
+    int coords = option_find_int(options, "coords", 1);
+    int classes = option_find_int(options, "classes", 1);
+    int rescore = option_find_int(options, "rescore", 0);
+    int num = option_find_int(options, "num", 1);
+    int side = option_find_int(options, "side", 7);
+    detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
+
+    layer.softmax = option_find_int(options, "softmax", 0);
+    layer.sqrt = option_find_int(options, "sqrt", 0);
+
+    layer.max_boxes = option_find_int_quiet(options, "max",90);
+    layer.coord_scale = option_find_float(options, "coord_scale", 1);
+    layer.forced = option_find_int(options, "forced", 0);
+    layer.object_scale = option_find_float(options, "object_scale", 1);
+    layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
+    layer.class_scale = option_find_float(options, "class_scale", 1);
+    layer.jitter = option_find_float(options, "jitter", .2);
+    layer.random = option_find_int_quiet(options, "random", 0);
+    layer.reorg = option_find_int_quiet(options, "reorg", 0);
+    return layer;
+}
+
+cost_layer parse_cost(list *options, size_params params)
+{
+    char *type_s = option_find_str(options, "type", "sse");
+    COST_TYPE type = get_cost_type(type_s);
+    float scale = option_find_float_quiet(options, "scale",1);
+    cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
+    layer.ratio =  option_find_float_quiet(options, "ratio",0);
+    layer.noobject_scale =  option_find_float_quiet(options, "noobj", 1);
+    layer.thresh =  option_find_float_quiet(options, "thresh",0);
+    return layer;
+}
+
+crop_layer parse_crop(list *options, size_params params)
+{
+    int crop_height = option_find_int(options, "crop_height",1);
+    int crop_width = option_find_int(options, "crop_width",1);
+    int flip = option_find_int(options, "flip",0);
+    float angle = option_find_float(options, "angle",0);
+    float saturation = option_find_float(options, "saturation",1);
+    float exposure = option_find_float(options, "exposure",1);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before crop layer must output image.");
+
+    int noadjust = option_find_int_quiet(options, "noadjust",0);
+
+    crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
+    l.shift = option_find_float(options, "shift", 0);
+    l.noadjust = noadjust;
+    return l;
+}
+
+layer parse_reorg(list *options, size_params params)
+{
+    int stride = option_find_int(options, "stride",1);
+    int reverse = option_find_int_quiet(options, "reverse",0);
+    int flatten = option_find_int_quiet(options, "flatten",0);
+    int extra = option_find_int_quiet(options, "extra",0);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before reorg layer must output image.");
+
+    layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra);
+    return layer;
+}
+
+maxpool_layer parse_maxpool(list *options, size_params params)
+{
+    int stride = option_find_int(options, "stride",1);
+    int size = option_find_int(options, "size",stride);
+    int padding = option_find_int_quiet(options, "padding", size-1);
+
+    int batch,h,w,c;
+    h = params.h;
+    w = params.w;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before maxpool layer must output image.");
+
+    maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride,padding);
+    return layer;
+}
+
+avgpool_layer parse_avgpool(list *options, size_params params)
+{
+    int batch,w,h,c;
+    w = params.w;
+    h = params.h;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before avgpool layer must output image.");
+
+    avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
+    return layer;
+}
+
+dropout_layer parse_dropout(list *options, size_params params)
+{
+    float probability = option_find_float(options, "probability", .5);
+    dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
+    layer.out_w = params.w;
+    layer.out_h = params.h;
+    layer.out_c = params.c;
+    return layer;
+}
+
+layer parse_normalization(list *options, size_params params)
+{
+    float alpha = option_find_float(options, "alpha", .0001);
+    float beta =  option_find_float(options, "beta" , .75);
+    float kappa = option_find_float(options, "kappa", 1);
+    int size = option_find_int(options, "size", 5);
+    layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa);
+    return l;
+}
+
+layer parse_batchnorm(list *options, size_params params)
+{
+    layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c);
+    return l;
+}
+
+layer parse_shortcut(list *options, size_params params, network *net)
+{
+    char *l = option_find(options, "from");
+    int index = atoi(l);
+    if(index < 0) index = params.index + index;
+
+    int batch = params.batch;
+    layer from = net->layers[index];
+
+    layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
+
+    char *activation_s = option_find_str(options, "activation", "linear");
+    ACTIVATION activation = get_activation(activation_s);
+    s.activation = activation;
+    s.alpha = option_find_float_quiet(options, "alpha", 1);
+    s.beta = option_find_float_quiet(options, "beta", 1);
+    return s;
+}
+
+
+layer parse_l2norm(list *options, size_params params)
+{
+    layer l = make_l2norm_layer(params.batch, params.inputs);
+    l.h = l.out_h = params.h;
+    l.w = l.out_w = params.w;
+    l.c = l.out_c = params.c;
+    return l;
+}
+
+
+layer parse_logistic(list *options, size_params params)
+{
+    layer l = make_logistic_layer(params.batch, params.inputs);
+    l.h = l.out_h = params.h;
+    l.w = l.out_w = params.w;
+    l.c = l.out_c = params.c;
+    return l;
+}
+
+layer parse_activation(list *options, size_params params)
+{
+    char *activation_s = option_find_str(options, "activation", "linear");
+    ACTIVATION activation = get_activation(activation_s);
+
+    layer l = make_activation_layer(params.batch, params.inputs, activation);
+
+    l.h = l.out_h = params.h;
+    l.w = l.out_w = params.w;
+    l.c = l.out_c = params.c;
+
+    return l;
+}
+
+layer parse_upsample(list *options, size_params params, network *net)
+{
+
+    int stride = option_find_int(options, "stride",2);
+    layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride);
+    l.scale = option_find_float_quiet(options, "scale", 1);
+    return l;
+}
+
+route_layer parse_route(list *options, size_params params, network *net)
+{
+    char *l = option_find(options, "layers");
+    int len = strlen(l);
+    if(!l) error("Route Layer must specify input layers");
+    int n = 1;
+    int i;
+    for(i = 0; i < len; ++i){
+        if (l[i] == ',') ++n;
+    }
+
+    int *layers = calloc(n, sizeof(int));
+    int *sizes = calloc(n, sizeof(int));
+    for(i = 0; i < n; ++i){
+        int index = atoi(l);
+        l = strchr(l, ',')+1;
+        if(index < 0) index = params.index + index;
+        layers[i] = index;
+        sizes[i] = net->layers[index].outputs;
+    }
+    int batch = params.batch;
+
+    route_layer layer = make_route_layer(batch, n, layers, sizes);
+
+    convolutional_layer first = net->layers[layers[0]];
+    layer.out_w = first.out_w;
+    layer.out_h = first.out_h;
+    layer.out_c = first.out_c;
+    for(i = 1; i < n; ++i){
+        int index = layers[i];
+        convolutional_layer next = net->layers[index];
+        if(next.out_w == first.out_w && next.out_h == first.out_h){
+            layer.out_c += next.out_c;
+        }else{
+            layer.out_h = layer.out_w = layer.out_c = 0;
+        }
+    }
+
+    return layer;
+}
+
+learning_rate_policy get_policy(char *s)
+{
+    if (strcmp(s, "random")==0) return RANDOM;
+    if (strcmp(s, "poly")==0) return POLY;
+    if (strcmp(s, "constant")==0) return CONSTANT;
+    if (strcmp(s, "step")==0) return STEP;
+    if (strcmp(s, "exp")==0) return EXP;
+    if (strcmp(s, "sigmoid")==0) return SIG;
+    if (strcmp(s, "steps")==0) return STEPS;
+    fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
+    return CONSTANT;
+}
+
+void parse_net_options(list *options, network *net)
+{
+    net->batch = option_find_int(options, "batch",1);
+    net->learning_rate = option_find_float(options, "learning_rate", .001);
+    net->momentum = option_find_float(options, "momentum", .9);
+    net->decay = option_find_float(options, "decay", .0001);
+    int subdivs = option_find_int(options, "subdivisions",1);
+    net->time_steps = option_find_int_quiet(options, "time_steps",1);
+    net->notruth = option_find_int_quiet(options, "notruth",0);
+    net->batch /= subdivs;
+    net->batch *= net->time_steps;
+    net->subdivisions = subdivs;
+    net->random = option_find_int_quiet(options, "random", 0);
+
+    net->adam = option_find_int_quiet(options, "adam", 0);
+    if(net->adam){
+        net->B1 = option_find_float(options, "B1", .9);
+        net->B2 = option_find_float(options, "B2", .999);
+        net->eps = option_find_float(options, "eps", .0000001);
+    }
+
+    net->h = option_find_int_quiet(options, "height",0);
+    net->w = option_find_int_quiet(options, "width",0);
+    net->c = option_find_int_quiet(options, "channels",0);
+    net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
+    net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
+    net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
+    net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w);
+    net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w);
+    net->center = option_find_int_quiet(options, "center",0);
+    net->clip = option_find_float_quiet(options, "clip", 0);
+
+    net->angle = option_find_float_quiet(options, "angle", 0);
+    net->aspect = option_find_float_quiet(options, "aspect", 1);
+    net->saturation = option_find_float_quiet(options, "saturation", 1);
+    net->exposure = option_find_float_quiet(options, "exposure", 1);
+    net->hue = option_find_float_quiet(options, "hue", 0);
+
+    if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
+
+    char *policy_s = option_find_str(options, "policy", "constant");
+    net->policy = get_policy(policy_s);
+    net->burn_in = option_find_int_quiet(options, "burn_in", 0);
+    net->power = option_find_float_quiet(options, "power", 4);
+    if(net->policy == STEP){
+        net->step = option_find_int(options, "step", 1);
+        net->scale = option_find_float(options, "scale", 1);
+    } else if (net->policy == STEPS){
+        char *l = option_find(options, "steps");
+        char *p = option_find(options, "scales");
+        if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
+
+        int len = strlen(l);
+        int n = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (l[i] == ',') ++n;
+        }
+        int *steps = calloc(n, sizeof(int));
+        float *scales = calloc(n, sizeof(float));
+        for(i = 0; i < n; ++i){
+            int step    = atoi(l);
+            float scale = atof(p);
+            l = strchr(l, ',')+1;
+            p = strchr(p, ',')+1;
+            steps[i] = step;
+            scales[i] = scale;
+        }
+        net->scales = scales;
+        net->steps = steps;
+        net->num_steps = n;
+    } else if (net->policy == EXP){
+        net->gamma = option_find_float(options, "gamma", 1);
+    } else if (net->policy == SIG){
+        net->gamma = option_find_float(options, "gamma", 1);
+        net->step = option_find_int(options, "step", 1);
+    } else if (net->policy == POLY || net->policy == RANDOM){
+    }
+    net->max_batches = option_find_int(options, "max_batches", 0);
+}
+
+int is_network(section *s)
+{
+    return (strcmp(s->type, "[net]")==0
+            || strcmp(s->type, "[network]")==0);
+}
+
+network *parse_network_cfg(char *filename)
+{
+    list *sections = read_cfg(filename);
+    node *n = sections->front;
+    if(!n) error("Config file has no sections");
+    network *net = make_network(sections->size - 1);
+    net->gpu_index = gpu_index;
+    size_params params;
+
+    section *s = (section *)n->val;
+    list *options = s->options;
+    if(!is_network(s)) error("First section must be [net] or [network]");
+    parse_net_options(options, net);
+
+    params.h = net->h;
+    params.w = net->w;
+    params.c = net->c;
+    params.inputs = net->inputs;
+    params.batch = net->batch;
+    params.time_steps = net->time_steps;
+    params.net = net;
+
+    size_t workspace_size = 0;
+    n = n->next;
+    int count = 0;
+    free_section(s);
+    fprintf(stderr, "layer     filters    size              input                output\n");
+    while(n){
+        params.index = count;
+        fprintf(stderr, "%5d ", count);
+        s = (section *)n->val;
+        options = s->options;
+        layer l = {0};
+        LAYER_TYPE lt = string_to_layer_type(s->type);
+        if(lt == CONVOLUTIONAL){
+            l = parse_convolutional(options, params);
+        }else if(lt == DECONVOLUTIONAL){
+            l = parse_deconvolutional(options, params);
+        }else if(lt == LOCAL){
+            l = parse_local(options, params);
+        }else if(lt == ACTIVE){
+            l = parse_activation(options, params);
+        }else if(lt == LOGXENT){
+            l = parse_logistic(options, params);
+        }else if(lt == L2NORM){
+            l = parse_l2norm(options, params);
+        }else if(lt == RNN){
+            l = parse_rnn(options, params);
+        }else if(lt == GRU){
+            l = parse_gru(options, params);
+        }else if (lt == LSTM) {
+            l = parse_lstm(options, params);
+        }else if(lt == CRNN){
+            l = parse_crnn(options, params);
+        }else if(lt == CONNECTED){
+            l = parse_connected(options, params);
+        }else if(lt == CROP){
+            l = parse_crop(options, params);
+        }else if(lt == COST){
+            l = parse_cost(options, params);
+        }else if(lt == REGION){
+            l = parse_region(options, params);
+        }else if(lt == YOLO){
+            l = parse_yolo(options, params);
+        }else if(lt == ISEG){
+            l = parse_iseg(options, params);
+        }else if(lt == DETECTION){
+            l = parse_detection(options, params);
+        }else if(lt == SOFTMAX){
+            l = parse_softmax(options, params);
+            net->hierarchy = l.softmax_tree;
+        }else if(lt == NORMALIZATION){
+            l = parse_normalization(options, params);
+        }else if(lt == BATCHNORM){
+            l = parse_batchnorm(options, params);
+        }else if(lt == MAXPOOL){
+            l = parse_maxpool(options, params);
+        }else if(lt == REORG){
+            l = parse_reorg(options, params);
+        }else if(lt == AVGPOOL){
+            l = parse_avgpool(options, params);
+        }else if(lt == ROUTE){
+            l = parse_route(options, params, net);
+        }else if(lt == UPSAMPLE){
+            l = parse_upsample(options, params, net);
+        }else if(lt == SHORTCUT){
+            l = parse_shortcut(options, params, net);
+        }else if(lt == DROPOUT){
+            l = parse_dropout(options, params);
+            l.output = net->layers[count-1].output;
+            l.delta = net->layers[count-1].delta;
+#ifdef GPU
+            l.output_gpu = net->layers[count-1].output_gpu;
+            l.delta_gpu = net->layers[count-1].delta_gpu;
+#endif
+        }else{
+            fprintf(stderr, "Type not recognized: %s\n", s->type);
+        }
+        l.clip = net->clip;
+        l.truth = option_find_int_quiet(options, "truth", 0);
+        l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
+        l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
+        l.dontsave = option_find_int_quiet(options, "dontsave", 0);
+        l.dontload = option_find_int_quiet(options, "dontload", 0);
+        l.numload = option_find_int_quiet(options, "numload", 0);
+        l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
+        l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
+        l.smooth = option_find_float_quiet(options, "smooth", 0);
+        option_unused(options);
+        net->layers[count] = l;
+        if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
+        free_section(s);
+        n = n->next;
+        ++count;
+        if(n){
+            params.h = l.out_h;
+            params.w = l.out_w;
+            params.c = l.out_c;
+            params.inputs = l.outputs;
+        }
+    }
+    free_list(sections);
+    layer out = get_network_output_layer(net);
+    net->outputs = out.outputs;
+    net->truths = out.outputs;
+    if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths;
+    net->output = out.output;
+    net->input = calloc(net->inputs*net->batch, sizeof(float));
+    net->truth = calloc(net->truths*net->batch, sizeof(float));
+#ifdef GPU
+    net->output_gpu = out.output_gpu;
+    net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch);
+    net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch);
+#endif
+    if(workspace_size){
+        //printf("%ld\n", workspace_size);
+#ifdef GPU
+        if(gpu_index >= 0){
+            net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+        }else {
+            net->workspace = calloc(1, workspace_size);
+        }
+#else
+        net->workspace = calloc(1, workspace_size);
+#endif
+    }
+    return net;
+}
+
+list *read_cfg(char *filename)
+{
+    FILE *file = fopen(filename, "r");
+    if(file == 0) file_error(filename);
+    char *line;
+    int nu = 0;
+    list *options = make_list();
+    section *current = 0;
+    while((line=fgetl(file)) != 0){
+        ++ nu;
+        strip(line);
+        switch(line[0]){
+            case '[':
+                current = malloc(sizeof(section));
+                list_insert(options, current);
+                current->options = make_list();
+                current->type = line;
+                break;
+            case '\0':
+            case '#':
+            case ';':
+                free(line);
+                break;
+            default:
+                if(!read_option(line, current->options)){
+                    fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
+                    free(line);
+                }
+                break;
+        }
+    }
+    fclose(file);
+    return options;
+}
+
+void save_convolutional_weights_binary(layer l, FILE *fp)
+{
+#ifdef GPU
+    if(gpu_index >= 0){
+        pull_convolutional_layer(l);
+    }
+#endif
+    binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
+    int size = l.c*l.size*l.size;
+    int i, j, k;
+    fwrite(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize){
+        fwrite(l.scales, sizeof(float), l.n, fp);
+        fwrite(l.rolling_mean, sizeof(float), l.n, fp);
+        fwrite(l.rolling_variance, sizeof(float), l.n, fp);
+    }
+    for(i = 0; i < l.n; ++i){
+        float mean = l.binary_weights[i*size];
+        if(mean < 0) mean = -mean;
+        fwrite(&mean, sizeof(float), 1, fp);
+        for(j = 0; j < size/8; ++j){
+            int index = i*size + j*8;
+            unsigned char c = 0;
+            for(k = 0; k < 8; ++k){
+                if (j*8 + k >= size) break;
+                if (l.binary_weights[index + k] > 0) c = (c | 1<<k);
+            }
+            fwrite(&c, sizeof(char), 1, fp);
+        }
+    }
+}
+
+void save_convolutional_weights(layer l, FILE *fp)
+{
+    if(l.binary){
+        //save_convolutional_weights_binary(l, fp);
+        //return;
+    }
+#ifdef GPU
+    if(gpu_index >= 0){
+        pull_convolutional_layer(l);
+    }
+#endif
+    int num = l.nweights;
+    fwrite(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize){
+        fwrite(l.scales, sizeof(float), l.n, fp);
+        fwrite(l.rolling_mean, sizeof(float), l.n, fp);
+        fwrite(l.rolling_variance, sizeof(float), l.n, fp);
+    }
+    fwrite(l.weights, sizeof(float), num, fp);
+}
+
+void save_batchnorm_weights(layer l, FILE *fp)
+{
+#ifdef GPU
+    if(gpu_index >= 0){
+        pull_batchnorm_layer(l);
+    }
+#endif
+    fwrite(l.scales, sizeof(float), l.c, fp);
+    fwrite(l.rolling_mean, sizeof(float), l.c, fp);
+    fwrite(l.rolling_variance, sizeof(float), l.c, fp);
+}
+
+void save_connected_weights(layer l, FILE *fp)
+{
+#ifdef GPU
+    if(gpu_index >= 0){
+        pull_connected_layer(l);
+    }
+#endif
+    fwrite(l.biases, sizeof(float), l.outputs, fp);
+    fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
+    if (l.batch_normalize){
+        fwrite(l.scales, sizeof(float), l.outputs, fp);
+        fwrite(l.rolling_mean, sizeof(float), l.outputs, fp);
+        fwrite(l.rolling_variance, sizeof(float), l.outputs, fp);
+    }
+}
+
+void save_weights_upto(network *net, char *filename, int cutoff)
+{
+#ifdef GPU
+    if(net->gpu_index >= 0){
+        cuda_set_device(net->gpu_index);
+    }
+#endif
+    fprintf(stderr, "Saving weights to %s\n", filename);
+    FILE *fp = fopen(filename, "wb");
+    if(!fp) file_error(filename);
+
+    int major = 0;
+    int minor = 2;
+    int revision = 0;
+    fwrite(&major, sizeof(int), 1, fp);
+    fwrite(&minor, sizeof(int), 1, fp);
+    fwrite(&revision, sizeof(int), 1, fp);
+    fwrite(net->seen, sizeof(size_t), 1, fp);
+
+    int i;
+    for(i = 0; i < net->n && i < cutoff; ++i){
+        layer l = net->layers[i];
+        if (l.dontsave) continue;
+        if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
+            save_convolutional_weights(l, fp);
+        } if(l.type == CONNECTED){
+            save_connected_weights(l, fp);
+        } if(l.type == BATCHNORM){
+            save_batchnorm_weights(l, fp);
+        } if(l.type == RNN){
+            save_connected_weights(*(l.input_layer), fp);
+            save_connected_weights(*(l.self_layer), fp);
+            save_connected_weights(*(l.output_layer), fp);
+        } if (l.type == LSTM) {
+            save_connected_weights(*(l.wi), fp);
+            save_connected_weights(*(l.wf), fp);
+            save_connected_weights(*(l.wo), fp);
+            save_connected_weights(*(l.wg), fp);
+            save_connected_weights(*(l.ui), fp);
+            save_connected_weights(*(l.uf), fp);
+            save_connected_weights(*(l.uo), fp);
+            save_connected_weights(*(l.ug), fp);
+        } if (l.type == GRU) {
+            if(1){
+                save_connected_weights(*(l.wz), fp);
+                save_connected_weights(*(l.wr), fp);
+                save_connected_weights(*(l.wh), fp);
+                save_connected_weights(*(l.uz), fp);
+                save_connected_weights(*(l.ur), fp);
+                save_connected_weights(*(l.uh), fp);
+            }else{
+                save_connected_weights(*(l.reset_layer), fp);
+                save_connected_weights(*(l.update_layer), fp);
+                save_connected_weights(*(l.state_layer), fp);
+            }
+        }  if(l.type == CRNN){
+            save_convolutional_weights(*(l.input_layer), fp);
+            save_convolutional_weights(*(l.self_layer), fp);
+            save_convolutional_weights(*(l.output_layer), fp);
+        } if(l.type == LOCAL){
+#ifdef GPU
+            if(gpu_index >= 0){
+                pull_local_layer(l);
+            }
+#endif
+            int locations = l.out_w*l.out_h;
+            int size = l.size*l.size*l.c*l.n*locations;
+            fwrite(l.biases, sizeof(float), l.outputs, fp);
+            fwrite(l.weights, sizeof(float), size, fp);
+        }
+    }
+    fclose(fp);
+}
+void save_weights(network *net, char *filename)
+{
+    save_weights_upto(net, filename, net->n);
+}
+
+void transpose_matrix(float *a, int rows, int cols)
+{
+    float *transpose = calloc(rows*cols, sizeof(float));
+    int x, y;
+    for(x = 0; x < rows; ++x){
+        for(y = 0; y < cols; ++y){
+            transpose[y*rows + x] = a[x*cols + y];
+        }
+    }
+    memcpy(a, transpose, rows*cols*sizeof(float));
+    free(transpose);
+}
+
+void load_connected_weights(layer l, FILE *fp, int transpose)
+{
+    fread(l.biases, sizeof(float), l.outputs, fp);
+    fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
+    if(transpose){
+        transpose_matrix(l.weights, l.inputs, l.outputs);
+    }
+    //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
+    //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
+    if (l.batch_normalize && (!l.dontloadscales)){
+        fread(l.scales, sizeof(float), l.outputs, fp);
+        fread(l.rolling_mean, sizeof(float), l.outputs, fp);
+        fread(l.rolling_variance, sizeof(float), l.outputs, fp);
+        //printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs));
+        //printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs));
+        //printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs));
+    }
+#ifdef GPU
+    if(gpu_index >= 0){
+        push_connected_layer(l);
+    }
+#endif
+}
+
+void load_batchnorm_weights(layer l, FILE *fp)
+{
+    fread(l.scales, sizeof(float), l.c, fp);
+    fread(l.rolling_mean, sizeof(float), l.c, fp);
+    fread(l.rolling_variance, sizeof(float), l.c, fp);
+#ifdef GPU
+    if(gpu_index >= 0){
+        push_batchnorm_layer(l);
+    }
+#endif
+}
+
+void load_convolutional_weights_binary(layer l, FILE *fp)
+{
+    fread(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize && (!l.dontloadscales)){
+        fread(l.scales, sizeof(float), l.n, fp);
+        fread(l.rolling_mean, sizeof(float), l.n, fp);
+        fread(l.rolling_variance, sizeof(float), l.n, fp);
+    }
+    int size = l.c*l.size*l.size;
+    int i, j, k;
+    for(i = 0; i < l.n; ++i){
+        float mean = 0;
+        fread(&mean, sizeof(float), 1, fp);
+        for(j = 0; j < size/8; ++j){
+            int index = i*size + j*8;
+            unsigned char c = 0;
+            fread(&c, sizeof(char), 1, fp);
+            for(k = 0; k < 8; ++k){
+                if (j*8 + k >= size) break;
+                l.weights[index + k] = (c & 1<<k) ? mean : -mean;
+            }
+        }
+    }
+#ifdef GPU
+    if(gpu_index >= 0){
+        push_convolutional_layer(l);
+    }
+#endif
+}
+
+void load_convolutional_weights(layer l, FILE *fp)
+{
+    if(l.binary){
+        //load_convolutional_weights_binary(l, fp);
+        //return;
+    }
+    if(l.numload) l.n = l.numload;
+    int num = l.c/l.groups*l.n*l.size*l.size;
+    fread(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize && (!l.dontloadscales)){
+        fread(l.scales, sizeof(float), l.n, fp);
+        fread(l.rolling_mean, sizeof(float), l.n, fp);
+        fread(l.rolling_variance, sizeof(float), l.n, fp);
+        if(0){
+            int i;
+            for(i = 0; i < l.n; ++i){
+                printf("%g, ", l.rolling_mean[i]);
+            }
+            printf("\n");
+            for(i = 0; i < l.n; ++i){
+                printf("%g, ", l.rolling_variance[i]);
+            }
+            printf("\n");
+        }
+        if(0){
+            fill_cpu(l.n, 0, l.rolling_mean, 1);
+            fill_cpu(l.n, 0, l.rolling_variance, 1);
+        }
+        if(0){
+            int i;
+            for(i = 0; i < l.n; ++i){
+                printf("%g, ", l.rolling_mean[i]);
+            }
+            printf("\n");
+            for(i = 0; i < l.n; ++i){
+                printf("%g, ", l.rolling_variance[i]);
+            }
+            printf("\n");
+        }
+    }
+    fread(l.weights, sizeof(float), num, fp);
+    //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
+    if (l.flipped) {
+        transpose_matrix(l.weights, l.c*l.size*l.size, l.n);
+    }
+    //if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights);
+#ifdef GPU
+    if(gpu_index >= 0){
+        push_convolutional_layer(l);
+    }
+#endif
+}
+
+
+void load_weights_upto(network *net, char *filename, int start, int cutoff)
+{
+#ifdef GPU
+    if(net->gpu_index >= 0){
+        cuda_set_device(net->gpu_index);
+    }
+#endif
+    fprintf(stderr, "Loading weights from %s...", filename);
+    fflush(stdout);
+    FILE *fp = fopen(filename, "rb");
+    if(!fp) file_error(filename);
+
+    int major;
+    int minor;
+    int revision;
+    fread(&major, sizeof(int), 1, fp);
+    fread(&minor, sizeof(int), 1, fp);
+    fread(&revision, sizeof(int), 1, fp);
+    if ((major*10 + minor) >= 2 && major < 1000 && minor < 1000){
+        fread(net->seen, sizeof(size_t), 1, fp);
+    } else {
+        int iseen = 0;
+        fread(&iseen, sizeof(int), 1, fp);
+        *net->seen = iseen;
+    }
+    int transpose = (major > 1000) || (minor > 1000);
+
+    int i;
+    for(i = start; i < net->n && i < cutoff; ++i){
+        layer l = net->layers[i];
+        if (l.dontload) continue;
+        if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
+            load_convolutional_weights(l, fp);
+        }
+        if(l.type == CONNECTED){
+            load_connected_weights(l, fp, transpose);
+        }
+        if(l.type == BATCHNORM){
+            load_batchnorm_weights(l, fp);
+        }
+        if(l.type == CRNN){
+            load_convolutional_weights(*(l.input_layer), fp);
+            load_convolutional_weights(*(l.self_layer), fp);
+            load_convolutional_weights(*(l.output_layer), fp);
+        }
+        if(l.type == RNN){
+            load_connected_weights(*(l.input_layer), fp, transpose);
+            load_connected_weights(*(l.self_layer), fp, transpose);
+            load_connected_weights(*(l.output_layer), fp, transpose);
+        }
+        if (l.type == LSTM) {
+            load_connected_weights(*(l.wi), fp, transpose);
+            load_connected_weights(*(l.wf), fp, transpose);
+            load_connected_weights(*(l.wo), fp, transpose);
+            load_connected_weights(*(l.wg), fp, transpose);
+            load_connected_weights(*(l.ui), fp, transpose);
+            load_connected_weights(*(l.uf), fp, transpose);
+            load_connected_weights(*(l.uo), fp, transpose);
+            load_connected_weights(*(l.ug), fp, transpose);
+        }
+        if (l.type == GRU) {
+            if(1){
+                load_connected_weights(*(l.wz), fp, transpose);
+                load_connected_weights(*(l.wr), fp, transpose);
+                load_connected_weights(*(l.wh), fp, transpose);
+                load_connected_weights(*(l.uz), fp, transpose);
+                load_connected_weights(*(l.ur), fp, transpose);
+                load_connected_weights(*(l.uh), fp, transpose);
+            }else{
+                load_connected_weights(*(l.reset_layer), fp, transpose);
+                load_connected_weights(*(l.update_layer), fp, transpose);
+                load_connected_weights(*(l.state_layer), fp, transpose);
+            }
+        }
+        if(l.type == LOCAL){
+            int locations = l.out_w*l.out_h;
+            int size = l.size*l.size*l.c*l.n*locations;
+            fread(l.biases, sizeof(float), l.outputs, fp);
+            fread(l.weights, sizeof(float), size, fp);
+#ifdef GPU
+            if(gpu_index >= 0){
+                push_local_layer(l);
+            }
+#endif
+        }
+    }
+    fprintf(stderr, "Done!\n");
+    fclose(fp);
+}
+
+void load_weights(network *net, char *filename)
+{
+    load_weights_upto(net, filename, 0, net->n);
+}
+
diff --git a/hanzi_detection/src/parser.h b/hanzi_detection/src/parser.h
new file mode 100755
index 0000000..81aef2c
--- /dev/null
+++ b/hanzi_detection/src/parser.h
@@ -0,0 +1,9 @@
+#ifndef PARSER_H
+#define PARSER_H
+#include "darknet.h"
+#include "network.h"
+
+void save_network(network net, char *filename);
+void save_weights_double(network net, char *filename);
+
+#endif
diff --git a/hanzi_detection/src/region_layer.c b/hanzi_detection/src/region_layer.c
new file mode 100755
index 0000000..179f5e3
--- /dev/null
+++ b/hanzi_detection/src/region_layer.c
@@ -0,0 +1,507 @@
+#include "region_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
+{
+    layer l = {0};
+    l.type = REGION;
+
+    l.n = n;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = n*(classes + coords + 1);
+    l.out_w = l.w;
+    l.out_h = l.h;
+    l.out_c = l.c;
+    l.classes = classes;
+    l.coords = coords;
+    l.cost = calloc(1, sizeof(float));
+    l.biases = calloc(n*2, sizeof(float));
+    l.bias_updates = calloc(n*2, sizeof(float));
+    l.outputs = h*w*n*(classes + coords + 1);
+    l.inputs = l.outputs;
+    l.truths = 30*(l.coords + 1);
+    l.delta = calloc(batch*l.outputs, sizeof(float));
+    l.output = calloc(batch*l.outputs, sizeof(float));
+    int i;
+    for(i = 0; i < n*2; ++i){
+        l.biases[i] = .5;
+    }
+
+    l.forward = forward_region_layer;
+    l.backward = backward_region_layer;
+#ifdef GPU
+    l.forward_gpu = forward_region_layer_gpu;
+    l.backward_gpu = backward_region_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+    fprintf(stderr, "detection\n");
+    srand(0);
+
+    return l;
+}
+
+void resize_region_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->outputs = h*w*l->n*(l->classes + l->coords + 1);
+    l->inputs = l->outputs;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =     cuda_make_array(l->delta, l->batch*l->outputs);
+    l->output_gpu =    cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
+
+box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride)
+{
+    box b;
+    b.x = (i + x[index + 0*stride]) / w;
+    b.y = (j + x[index + 1*stride]) / h;
+    b.w = exp(x[index + 2*stride]) * biases[2*n]   / w;
+    b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
+    return b;
+}
+
+float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride)
+{
+    box pred = get_region_box(x, biases, n, index, i, j, w, h, stride);
+    float iou = box_iou(pred, truth);
+
+    float tx = (truth.x*w - i);
+    float ty = (truth.y*h - j);
+    float tw = log(truth.w*w / biases[2*n]);
+    float th = log(truth.h*h / biases[2*n + 1]);
+
+    delta[index + 0*stride] = scale * (tx - x[index + 0*stride]);
+    delta[index + 1*stride] = scale * (ty - x[index + 1*stride]);
+    delta[index + 2*stride] = scale * (tw - x[index + 2*stride]);
+    delta[index + 3*stride] = scale * (th - x[index + 3*stride]);
+    return iou;
+}
+
+void delta_region_mask(float *truth, float *x, int n, int index, float *delta, int stride, int scale)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        delta[index + i*stride] = scale*(truth[i] - x[index + i*stride]);
+    }
+}
+
+
+void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, int stride, float *avg_cat, int tag)
+{
+    int i, n;
+    if(hier){
+        float pred = 1;
+        while(class >= 0){
+            pred *= output[index + stride*class];
+            int g = hier->group[class];
+            int offset = hier->group_offset[g];
+            for(i = 0; i < hier->group_size[g]; ++i){
+                delta[index + stride*(offset + i)] = scale * (0 - output[index + stride*(offset + i)]);
+            }
+            delta[index + stride*class] = scale * (1 - output[index + stride*class]);
+
+            class = hier->parent[class];
+        }
+        *avg_cat += pred;
+    } else {
+        if (delta[index] && tag){
+            delta[index + stride*class] = scale * (1 - output[index + stride*class]);
+            return;
+        }
+        for(n = 0; n < classes; ++n){
+            delta[index + stride*n] = scale * (((n == class)?1 : 0) - output[index + stride*n]);
+            if(n == class) *avg_cat += output[index + stride*n];
+        }
+    }
+}
+
+float logit(float x)
+{
+    return log(x/(1.-x));
+}
+
+float tisnan(float x)
+{
+    return (x != x);
+}
+
+int entry_index(layer l, int batch, int location, int entry)
+{
+    int n =   location / (l.w*l.h);
+    int loc = location % (l.w*l.h);
+    return batch*l.outputs + n*l.w*l.h*(l.coords+l.classes+1) + entry*l.w*l.h + loc;
+}
+
+void forward_region_layer(const layer l, network net)
+{
+    int i,j,b,t,n;
+    memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+
+#ifndef GPU
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int index = entry_index(l, b, n*l.w*l.h, 0);
+            activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
+            index = entry_index(l, b, n*l.w*l.h, l.coords);
+            if(!l.background) activate_array(l.output + index,   l.w*l.h, LOGISTIC);
+            index = entry_index(l, b, n*l.w*l.h, l.coords + 1);
+            if(!l.softmax && !l.softmax_tree) activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC);
+        }
+    }
+    if (l.softmax_tree){
+        int i;
+        int count = l.coords + 1;
+        for (i = 0; i < l.softmax_tree->groups; ++i) {
+            int group_size = l.softmax_tree->group_size[i];
+            softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count);
+            count += group_size;
+        }
+    } else if (l.softmax){
+        int index = entry_index(l, 0, 0, l.coords + !l.background);
+        softmax_cpu(net.input + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output + index);
+    }
+#endif
+
+    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+    if(!net.train) return;
+    float avg_iou = 0;
+    float recall = 0;
+    float avg_cat = 0;
+    float avg_obj = 0;
+    float avg_anyobj = 0;
+    int count = 0;
+    int class_count = 0;
+    *(l.cost) = 0;
+    for (b = 0; b < l.batch; ++b) {
+        if(l.softmax_tree){
+            int onlyclass = 0;
+            for(t = 0; t < 30; ++t){
+                box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
+                if(!truth.x) break;
+                int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords];
+                float maxp = 0;
+                int maxi = 0;
+                if(truth.x > 100000 && truth.y > 100000){
+                    for(n = 0; n < l.n*l.w*l.h; ++n){
+                        int class_index = entry_index(l, b, n, l.coords + 1);
+                        int obj_index = entry_index(l, b, n, l.coords);
+                        float scale =  l.output[obj_index];
+                        l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
+                        float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h);
+                        if(p > maxp){
+                            maxp = p;
+                            maxi = n;
+                        }
+                    }
+                    int class_index = entry_index(l, b, maxi, l.coords + 1);
+                    int obj_index = entry_index(l, b, maxi, l.coords);
+                    delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax);
+                    if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]);
+                    else  l.delta[obj_index] = 0;
+                    l.delta[obj_index] = 0;
+                    ++class_count;
+                    onlyclass = 1;
+                    break;
+                }
+            }
+            if(onlyclass) continue;
+        }
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                    box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h);
+                    float best_iou = 0;
+                    for(t = 0; t < 30; ++t){
+                        box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
+                        if(!truth.x) break;
+                        float iou = box_iou(pred, truth);
+                        if (iou > best_iou) {
+                            best_iou = iou;
+                        }
+                    }
+                    int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords);
+                    avg_anyobj += l.output[obj_index];
+                    l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
+                    if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]);
+                    if (best_iou > l.thresh) {
+                        l.delta[obj_index] = 0;
+                    }
+
+                    if(*(net.seen) < 12800){
+                        box truth = {0};
+                        truth.x = (i + .5)/l.w;
+                        truth.y = (j + .5)/l.h;
+                        truth.w = l.biases[2*n]/l.w;
+                        truth.h = l.biases[2*n+1]/l.h;
+                        delta_region_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, l.delta, .01, l.w*l.h);
+                    }
+                }
+            }
+        }
+        for(t = 0; t < 30; ++t){
+            box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
+
+            if(!truth.x) break;
+            float best_iou = 0;
+            int best_n = 0;
+            i = (truth.x * l.w);
+            j = (truth.y * l.h);
+            box truth_shift = truth;
+            truth_shift.x = 0;
+            truth_shift.y = 0;
+            for(n = 0; n < l.n; ++n){
+                int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h);
+                if(l.bias_match){
+                    pred.w = l.biases[2*n]/l.w;
+                    pred.h = l.biases[2*n+1]/l.h;
+                }
+                pred.x = 0;
+                pred.y = 0;
+                float iou = box_iou(pred, truth_shift);
+                if (iou > best_iou){
+                    best_iou = iou;
+                    best_n = n;
+                }
+            }
+
+            int box_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 0);
+            float iou = delta_region_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, l.delta, l.coord_scale *  (2 - truth.w*truth.h), l.w*l.h);
+            if(l.coords > 4){
+                int mask_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 4);
+                delta_region_mask(net.truth + t*(l.coords + 1) + b*l.truths + 5, l.output, l.coords - 4, mask_index, l.delta, l.w*l.h, l.mask_scale);
+            }
+            if(iou > .5) recall += 1;
+            avg_iou += iou;
+
+            int obj_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords);
+            avg_obj += l.output[obj_index];
+            l.delta[obj_index] = l.object_scale * (1 - l.output[obj_index]);
+            if (l.rescore) {
+                l.delta[obj_index] = l.object_scale * (iou - l.output[obj_index]);
+            }
+            if(l.background){
+                l.delta[obj_index] = l.object_scale * (0 - l.output[obj_index]);
+            }
+
+            int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords];
+            if (l.map) class = l.map[class];
+            int class_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords + 1);
+            delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax);
+            ++count;
+            ++class_count;
+        }
+    }
+    *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+    printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f,  count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
+}
+
+void backward_region_layer(const layer l, network net)
+{
+    /*
+       int b;
+       int size = l.coords + l.classes + 1;
+       for (b = 0; b < l.batch*l.n; ++b){
+       int index = (b*size + 4)*l.w*l.h;
+       gradient_array(l.output + index, l.w*l.h, LOGISTIC, l.delta + index);
+       }
+       axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
+     */
+}
+
+void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
+{
+    int i;
+    int new_w=0;
+    int new_h=0;
+    if (((float)netw/w) < ((float)neth/h)) {
+        new_w = netw;
+        new_h = (h * netw)/w;
+    } else {
+        new_h = neth;
+        new_w = (w * neth)/h;
+    }
+    for (i = 0; i < n; ++i){
+        box b = dets[i].bbox;
+        b.x =  (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); 
+        b.y =  (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); 
+        b.w *= (float)netw/new_w;
+        b.h *= (float)neth/new_h;
+        if(!relative){
+            b.x *= w;
+            b.w *= w;
+            b.y *= h;
+            b.h *= h;
+        }
+        dets[i].bbox = b;
+    }
+}
+
+void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
+{
+    int i,j,n,z;
+    float *predictions = l.output;
+    if (l.batch == 2) {
+        float *flip = l.output + l.outputs;
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w/2; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    for(z = 0; z < l.classes + l.coords + 1; ++z){
+                        int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
+                        int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
+                        float swap = flip[i1];
+                        flip[i1] = flip[i2];
+                        flip[i2] = swap;
+                        if(z == 0){
+                            flip[i1] = -flip[i1];
+                            flip[i2] = -flip[i2];
+                        }
+                    }
+                }
+            }
+        }
+        for(i = 0; i < l.outputs; ++i){
+            l.output[i] = (l.output[i] + flip[i])/2.;
+        }
+    }
+    for (i = 0; i < l.w*l.h; ++i){
+        int row = i / l.w;
+        int col = i % l.w;
+        for(n = 0; n < l.n; ++n){
+            int index = n*l.w*l.h + i;
+            for(j = 0; j < l.classes; ++j){
+                dets[index].prob[j] = 0;
+            }
+            int obj_index  = entry_index(l, 0, n*l.w*l.h + i, l.coords);
+            int box_index  = entry_index(l, 0, n*l.w*l.h + i, 0);
+            int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
+            float scale = l.background ? 1 : predictions[obj_index];
+            dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h);
+            dets[index].objectness = scale > thresh ? scale : 0;
+            if(dets[index].mask){
+                for(j = 0; j < l.coords - 4; ++j){
+                    dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
+                }
+            }
+
+            int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
+            if(l.softmax_tree){
+
+                hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h);
+                if(map){
+                    for(j = 0; j < 200; ++j){
+                        int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
+                        float prob = scale*predictions[class_index];
+                        dets[index].prob[j] = (prob > thresh) ? prob : 0;
+                    }
+                } else {
+                    int j =  hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
+                    dets[index].prob[j] = (scale > thresh) ? scale : 0;
+                }
+            } else {
+                if(dets[index].objectness){
+                    for(j = 0; j < l.classes; ++j){
+                        int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
+                        float prob = scale*predictions[class_index];
+                        dets[index].prob[j] = (prob > thresh) ? prob : 0;
+                    }
+                }
+            }
+        }
+    }
+    correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
+}
+
+#ifdef GPU
+
+void forward_region_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
+    int b, n;
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int index = entry_index(l, b, n*l.w*l.h, 0);
+            activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
+            if(l.coords > 4){
+                index = entry_index(l, b, n*l.w*l.h, 4);
+                activate_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC);
+            }
+            index = entry_index(l, b, n*l.w*l.h, l.coords);
+            if(!l.background) activate_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC);
+            index = entry_index(l, b, n*l.w*l.h, l.coords + 1);
+            if(!l.softmax && !l.softmax_tree) activate_array_gpu(l.output_gpu + index, l.classes*l.w*l.h, LOGISTIC);
+        }
+    }
+    if (l.softmax_tree){
+        int index = entry_index(l, 0, 0, l.coords + 1);
+        softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree);
+    } else if (l.softmax) {
+        int index = entry_index(l, 0, 0, l.coords + !l.background);
+        softmax_gpu(net.input_gpu + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index);
+    }
+    if(!net.train || l.onlyforward){
+        cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
+        return;
+    }
+
+    cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
+    forward_region_layer(l, net);
+    //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
+    if(!net.train) return;
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+}
+
+void backward_region_layer_gpu(const layer l, network net)
+{
+    int b, n;
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int index = entry_index(l, b, n*l.w*l.h, 0);
+            gradient_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC, l.delta_gpu + index);
+            if(l.coords > 4){
+                index = entry_index(l, b, n*l.w*l.h, 4);
+                gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index);
+            }
+            index = entry_index(l, b, n*l.w*l.h, l.coords);
+            if(!l.background) gradient_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC, l.delta_gpu + index);
+        }
+    }
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+
+void zero_objectness(layer l)
+{
+    int i, n;
+    for (i = 0; i < l.w*l.h; ++i){
+        for(n = 0; n < l.n; ++n){
+            int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
+            l.output[obj_index] = 0;
+        }
+    }
+}
+
diff --git a/hanzi_detection/src/region_layer.h b/hanzi_detection/src/region_layer.h
new file mode 100755
index 0000000..9f12fd1
--- /dev/null
+++ b/hanzi_detection/src/region_layer.h
@@ -0,0 +1,18 @@
+#ifndef REGION_LAYER_H
+#define REGION_LAYER_H
+
+#include "darknet.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_region_layer(int batch, int w, int h, int n, int classes, int coords);
+void forward_region_layer(const layer l, network net);
+void backward_region_layer(const layer l, network net);
+void resize_region_layer(layer *l, int w, int h);
+
+#ifdef GPU
+void forward_region_layer_gpu(const layer l, network net);
+void backward_region_layer_gpu(layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/reorg_layer.c b/hanzi_detection/src/reorg_layer.c
new file mode 100755
index 0000000..31d6b84
--- /dev/null
+++ b/hanzi_detection/src/reorg_layer.c
@@ -0,0 +1,173 @@
+#include "reorg_layer.h"
+#include "cuda.h"
+#include "blas.h"
+
+#include <stdio.h>
+
+
+layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra)
+{
+    layer l = {0};
+    l.type = REORG;
+    l.batch = batch;
+    l.stride = stride;
+    l.extra = extra;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.flatten = flatten;
+    if(reverse){
+        l.out_w = w*stride;
+        l.out_h = h*stride;
+        l.out_c = c/(stride*stride);
+    }else{
+        l.out_w = w/stride;
+        l.out_h = h/stride;
+        l.out_c = c*(stride*stride);
+    }
+    l.reverse = reverse;
+
+    l.outputs = l.out_h * l.out_w * l.out_c;
+    l.inputs = h*w*c;
+    if(l.extra){
+        l.out_w = l.out_h = l.out_c = 0;
+        l.outputs = l.inputs + l.extra;
+    }
+
+    if(extra){
+        fprintf(stderr, "reorg              %4d   ->  %4d\n",  l.inputs, l.outputs);
+    } else {
+        fprintf(stderr, "reorg              /%2d  %4d x%4d x%4d   ->  %4d x%4d x%4d\n",  stride, w, h, c, l.out_w, l.out_h, l.out_c);
+    }
+    int output_size = l.outputs * batch;
+    l.output =  calloc(output_size, sizeof(float));
+    l.delta =   calloc(output_size, sizeof(float));
+
+    l.forward = forward_reorg_layer;
+    l.backward = backward_reorg_layer;
+#ifdef GPU
+    l.forward_gpu = forward_reorg_layer_gpu;
+    l.backward_gpu = backward_reorg_layer_gpu;
+
+    l.output_gpu  = cuda_make_array(l.output, output_size);
+    l.delta_gpu   = cuda_make_array(l.delta, output_size);
+#endif
+    return l;
+}
+
+void resize_reorg_layer(layer *l, int w, int h)
+{
+    int stride = l->stride;
+    int c = l->c;
+
+    l->h = h;
+    l->w = w;
+
+    if(l->reverse){
+        l->out_w = w*stride;
+        l->out_h = h*stride;
+        l->out_c = c/(stride*stride);
+    }else{
+        l->out_w = w/stride;
+        l->out_h = h/stride;
+        l->out_c = c*(stride*stride);
+    }
+
+    l->outputs = l->out_h * l->out_w * l->out_c;
+    l->inputs = l->outputs;
+    int output_size = l->outputs * l->batch;
+
+    l->output = realloc(l->output, output_size * sizeof(float));
+    l->delta = realloc(l->delta, output_size * sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->output_gpu);
+    cuda_free(l->delta_gpu);
+    l->output_gpu  = cuda_make_array(l->output, output_size);
+    l->delta_gpu   = cuda_make_array(l->delta,  output_size);
+#endif
+}
+
+void forward_reorg_layer(const layer l, network net)
+{
+    int i;
+    if(l.flatten){
+        memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+        if(l.reverse){
+            flatten(l.output, l.w*l.h, l.c, l.batch, 0);
+        }else{
+            flatten(l.output, l.w*l.h, l.c, l.batch, 1);
+        }
+    } else if (l.extra) {
+        for(i = 0; i < l.batch; ++i){
+            copy_cpu(l.inputs, net.input + i*l.inputs, 1, l.output + i*l.outputs, 1);
+        }
+    } else if (l.reverse){
+        reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output);
+    } else {
+        reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output);
+    }
+}
+
+void backward_reorg_layer(const layer l, network net)
+{
+    int i;
+    if(l.flatten){
+        memcpy(net.delta, l.delta, l.outputs*l.batch*sizeof(float));
+        if(l.reverse){
+            flatten(net.delta, l.w*l.h, l.c, l.batch, 1);
+        }else{
+            flatten(net.delta, l.w*l.h, l.c, l.batch, 0);
+        }
+    } else if(l.reverse){
+        reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta);
+    } else if (l.extra) {
+        for(i = 0; i < l.batch; ++i){
+            copy_cpu(l.inputs, l.delta + i*l.outputs, 1, net.delta + i*l.inputs, 1);
+        }
+    }else{
+        reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta);
+    }
+}
+
+#ifdef GPU
+void forward_reorg_layer_gpu(layer l, network net)
+{
+    int i;
+    if(l.flatten){
+        if(l.reverse){
+            flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 0, l.output_gpu);
+        }else{
+            flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 1, l.output_gpu);
+        }
+    } else if (l.extra) {
+        for(i = 0; i < l.batch; ++i){
+            copy_gpu(l.inputs, net.input_gpu + i*l.inputs, 1, l.output_gpu + i*l.outputs, 1);
+        }
+    } else if (l.reverse) {
+        reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu);
+    }else {
+        reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu);
+    }
+}
+
+void backward_reorg_layer_gpu(layer l, network net)
+{
+    if(l.flatten){
+        if(l.reverse){
+            flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 1, net.delta_gpu);
+        }else{
+            flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 0, net.delta_gpu);
+        }
+    } else if (l.extra) {
+        int i;
+        for(i = 0; i < l.batch; ++i){
+            copy_gpu(l.inputs, l.delta_gpu + i*l.outputs, 1, net.delta_gpu + i*l.inputs, 1);
+        }
+    } else if(l.reverse){
+        reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta_gpu);
+    } else {
+        reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta_gpu);
+    }
+}
+#endif
diff --git a/hanzi_detection/src/reorg_layer.h b/hanzi_detection/src/reorg_layer.h
new file mode 100755
index 0000000..e6513a5
--- /dev/null
+++ b/hanzi_detection/src/reorg_layer.h
@@ -0,0 +1,20 @@
+#ifndef REORG_LAYER_H
+#define REORG_LAYER_H
+
+#include "image.h"
+#include "cuda.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra);
+void resize_reorg_layer(layer *l, int w, int h);
+void forward_reorg_layer(const layer l, network net);
+void backward_reorg_layer(const layer l, network net);
+
+#ifdef GPU
+void forward_reorg_layer_gpu(layer l, network net);
+void backward_reorg_layer_gpu(layer l, network net);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/rnn_layer.c b/hanzi_detection/src/rnn_layer.c
new file mode 100755
index 0000000..8c9b457
--- /dev/null
+++ b/hanzi_detection/src/rnn_layer.c
@@ -0,0 +1,292 @@
+#include "rnn_layer.h"
+#include "connected_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+static void increment_layer(layer *l, int steps)
+{
+    int num = l->outputs*l->batch*steps;
+    l->output += num;
+    l->delta += num;
+    l->x += num;
+    l->x_norm += num;
+
+#ifdef GPU
+    l->output_gpu += num;
+    l->delta_gpu += num;
+    l->x_gpu += num;
+    l->x_norm_gpu += num;
+#endif
+}
+
+layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam)
+{
+    fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs);
+    batch = batch / steps;
+    layer l = {0};
+    l.batch = batch;
+    l.type = RNN;
+    l.steps = steps;
+    l.inputs = inputs;
+
+    l.state = calloc(batch*outputs, sizeof(float));
+    l.prev_state = calloc(batch*outputs, sizeof(float));
+
+    l.input_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.input_layer) = make_connected_layer(batch*steps, inputs, outputs, activation, batch_normalize, adam);
+    l.input_layer->batch = batch;
+
+    l.self_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.self_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam);
+    l.self_layer->batch = batch;
+
+    l.output_layer = malloc(sizeof(layer));
+    fprintf(stderr, "\t\t");
+    *(l.output_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam);
+    l.output_layer->batch = batch;
+
+    l.outputs = outputs;
+    l.output = l.output_layer->output;
+    l.delta = l.output_layer->delta;
+
+    l.forward = forward_rnn_layer;
+    l.backward = backward_rnn_layer;
+    l.update = update_rnn_layer;
+#ifdef GPU
+    l.forward_gpu = forward_rnn_layer_gpu;
+    l.backward_gpu = backward_rnn_layer_gpu;
+    l.update_gpu = update_rnn_layer_gpu;
+    l.state_gpu = cuda_make_array(0, batch*outputs);
+    l.prev_state_gpu = cuda_make_array(0, batch*outputs);
+    l.output_gpu = l.output_layer->output_gpu;
+    l.delta_gpu = l.output_layer->delta_gpu;
+#ifdef CUDNN
+    cudnnSetTensor4dDescriptor(l.input_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_layer->out_c, l.input_layer->out_h, l.input_layer->out_w); 
+    cudnnSetTensor4dDescriptor(l.self_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.self_layer->out_c, l.self_layer->out_h, l.self_layer->out_w); 
+    cudnnSetTensor4dDescriptor(l.output_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.output_layer->out_c, l.output_layer->out_h, l.output_layer->out_w); 
+#endif
+#endif
+
+    return l;
+}
+
+void update_rnn_layer(layer l, update_args a)
+{
+    update_connected_layer(*(l.input_layer),  a);
+    update_connected_layer(*(l.self_layer),   a);
+    update_connected_layer(*(l.output_layer), a);
+}
+
+void forward_rnn_layer(layer l, network net)
+{
+    network s = net;
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, self_layer.delta, 1);
+    fill_cpu(l.outputs * l.batch * l.steps, 0, input_layer.delta, 1);
+    if(net.train) fill_cpu(l.outputs * l.batch, 0, l.state, 1);
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input = net.input;
+        forward_connected_layer(input_layer, s);
+
+        s.input = l.state;
+        forward_connected_layer(self_layer, s);
+
+        float *old_state = l.state;
+        if(net.train) l.state += l.outputs*l.batch;
+        if(l.shortcut){
+            copy_cpu(l.outputs * l.batch, old_state, 1, l.state, 1);
+        }else{
+            fill_cpu(l.outputs * l.batch, 0, l.state, 1);
+        }
+        axpy_cpu(l.outputs * l.batch, 1, input_layer.output, 1, l.state, 1);
+        axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+        s.input = l.state;
+        forward_connected_layer(output_layer, s);
+
+        net.input += l.inputs*l.batch;
+        increment_layer(&input_layer, 1);
+        increment_layer(&self_layer, 1);
+        increment_layer(&output_layer, 1);
+    }
+}
+
+void backward_rnn_layer(layer l, network net)
+{
+    network s = net;
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    increment_layer(&input_layer, l.steps-1);
+    increment_layer(&self_layer, l.steps-1);
+    increment_layer(&output_layer, l.steps-1);
+
+    l.state += l.outputs*l.batch*l.steps;
+    for (i = l.steps-1; i >= 0; --i) {
+        copy_cpu(l.outputs * l.batch, input_layer.output, 1, l.state, 1);
+        axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+        s.input = l.state;
+        s.delta = self_layer.delta;
+        backward_connected_layer(output_layer, s);
+
+        l.state -= l.outputs*l.batch;
+        /*
+           if(i > 0){
+           copy_cpu(l.outputs * l.batch, input_layer.output - l.outputs*l.batch, 1, l.state, 1);
+           axpy_cpu(l.outputs * l.batch, 1, self_layer.output - l.outputs*l.batch, 1, l.state, 1);
+           }else{
+           fill_cpu(l.outputs * l.batch, 0, l.state, 1);
+           }
+         */
+
+        s.input = l.state;
+        s.delta = self_layer.delta - l.outputs*l.batch;
+        if (i == 0) s.delta = 0;
+        backward_connected_layer(self_layer, s);
+
+        copy_cpu(l.outputs*l.batch, self_layer.delta, 1, input_layer.delta, 1);
+        if (i > 0 && l.shortcut) axpy_cpu(l.outputs*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.outputs*l.batch, 1);
+        s.input = net.input + i*l.inputs*l.batch;
+        if(net.delta) s.delta = net.delta + i*l.inputs*l.batch;
+        else s.delta = 0;
+        backward_connected_layer(input_layer, s);
+
+        increment_layer(&input_layer, -1);
+        increment_layer(&self_layer, -1);
+        increment_layer(&output_layer, -1);
+    }
+}
+
+#ifdef GPU
+
+void pull_rnn_layer(layer l)
+{
+    pull_connected_layer(*(l.input_layer));
+    pull_connected_layer(*(l.self_layer));
+    pull_connected_layer(*(l.output_layer));
+}
+
+void push_rnn_layer(layer l)
+{
+    push_connected_layer(*(l.input_layer));
+    push_connected_layer(*(l.self_layer));
+    push_connected_layer(*(l.output_layer));
+}
+
+void update_rnn_layer_gpu(layer l, update_args a)
+{
+    update_connected_layer_gpu(*(l.input_layer),  a);
+    update_connected_layer_gpu(*(l.self_layer),   a);
+    update_connected_layer_gpu(*(l.output_layer), a);
+}
+
+void forward_rnn_layer_gpu(layer l, network net)
+{
+    network s = {0};
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+
+    fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
+    fill_gpu(l.outputs * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
+
+    if(net.train) {
+        fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
+        copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1);
+    }
+
+    for (i = 0; i < l.steps; ++i) {
+        s.input_gpu = net.input_gpu;
+        forward_connected_layer_gpu(input_layer, s);
+
+        s.input_gpu = l.state_gpu;
+        forward_connected_layer_gpu(self_layer, s);
+
+        fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1);
+        axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
+        axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        forward_connected_layer_gpu(output_layer, s);
+
+        net.input_gpu += l.inputs*l.batch;
+        increment_layer(&input_layer, 1);
+        increment_layer(&self_layer, 1);
+        increment_layer(&output_layer, 1);
+    }
+}
+
+void backward_rnn_layer_gpu(layer l, network net)
+{
+    network s = {0};
+    s.train = net.train;
+    int i;
+    layer input_layer = *(l.input_layer);
+    layer self_layer = *(l.self_layer);
+    layer output_layer = *(l.output_layer);
+    increment_layer(&input_layer,  l.steps - 1);
+    increment_layer(&self_layer,   l.steps - 1);
+    increment_layer(&output_layer, l.steps - 1);
+    float *last_input = input_layer.output_gpu;
+    float *last_self = self_layer.output_gpu;
+    for (i = l.steps-1; i >= 0; --i) {
+        fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1);
+        axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
+        axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        s.delta_gpu = self_layer.delta_gpu;
+        backward_connected_layer_gpu(output_layer, s);
+
+        if(i != 0) {
+            fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1);
+            axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1);
+            axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1);
+        }else {
+            copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1);
+        }
+
+        copy_gpu(l.outputs*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
+
+        s.input_gpu = l.state_gpu;
+        s.delta_gpu = (i > 0) ? self_layer.delta_gpu - l.outputs*l.batch : 0;
+        if (i == 0) s.delta_gpu = 0;
+        backward_connected_layer_gpu(self_layer, s);
+
+        s.input_gpu = net.input_gpu + i*l.inputs*l.batch;
+        if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch;
+        else s.delta_gpu = 0;
+        backward_connected_layer_gpu(input_layer, s);
+
+        increment_layer(&input_layer,  -1);
+        increment_layer(&self_layer,   -1);
+        increment_layer(&output_layer, -1);
+    }
+    fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1);
+    axpy_gpu(l.outputs * l.batch, 1, last_input, 1, l.state_gpu, 1);
+    axpy_gpu(l.outputs * l.batch, 1, last_self, 1, l.state_gpu, 1);
+}
+#endif
diff --git a/hanzi_detection/src/rnn_layer.h b/hanzi_detection/src/rnn_layer.h
new file mode 100755
index 0000000..270a63f
--- /dev/null
+++ b/hanzi_detection/src/rnn_layer.h
@@ -0,0 +1,25 @@
+
+#ifndef RNN_LAYER_H
+#define RNN_LAYER_H
+
+#include "activations.h"
+#include "layer.h"
+#include "network.h"
+#define USET
+
+layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam);
+
+void forward_rnn_layer(layer l, network net);
+void backward_rnn_layer(layer l, network net);
+void update_rnn_layer(layer l, update_args a);
+
+#ifdef GPU
+void forward_rnn_layer_gpu(layer l, network net);
+void backward_rnn_layer_gpu(layer l, network net);
+void update_rnn_layer_gpu(layer l, update_args a);
+void push_rnn_layer(layer l);
+void pull_rnn_layer(layer l);
+#endif
+
+#endif
+
diff --git a/hanzi_detection/src/route_layer.c b/hanzi_detection/src/route_layer.c
new file mode 100755
index 0000000..a8970a4
--- /dev/null
+++ b/hanzi_detection/src/route_layer.c
@@ -0,0 +1,134 @@
+#include "route_layer.h"
+#include "cuda.h"
+#include "blas.h"
+
+#include <stdio.h>
+
+route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes)
+{
+    fprintf(stderr,"route ");
+    route_layer l = {0};
+    l.type = ROUTE;
+    l.batch = batch;
+    l.n = n;
+    l.input_layers = input_layers;
+    l.input_sizes = input_sizes;
+    int i;
+    int outputs = 0;
+    for(i = 0; i < n; ++i){
+        fprintf(stderr," %d", input_layers[i]);
+        outputs += input_sizes[i];
+    }
+    fprintf(stderr, "\n");
+    l.outputs = outputs;
+    l.inputs = outputs;
+    l.delta =  calloc(outputs*batch, sizeof(float));
+    l.output = calloc(outputs*batch, sizeof(float));;
+
+    l.forward = forward_route_layer;
+    l.backward = backward_route_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_route_layer_gpu;
+    l.backward_gpu = backward_route_layer_gpu;
+
+    l.delta_gpu =  cuda_make_array(l.delta, outputs*batch);
+    l.output_gpu = cuda_make_array(l.output, outputs*batch);
+    #endif
+    return l;
+}
+
+void resize_route_layer(route_layer *l, network *net)
+{
+    int i;
+    layer first = net->layers[l->input_layers[0]];
+    l->out_w = first.out_w;
+    l->out_h = first.out_h;
+    l->out_c = first.out_c;
+    l->outputs = first.outputs;
+    l->input_sizes[0] = first.outputs;
+    for(i = 1; i < l->n; ++i){
+        int index = l->input_layers[i];
+        layer next = net->layers[index];
+        l->outputs += next.outputs;
+        l->input_sizes[i] = next.outputs;
+        if(next.out_w == first.out_w && next.out_h == first.out_h){
+            l->out_c += next.out_c;
+        }else{
+            printf("%d %d, %d %d\n", next.out_w, next.out_h, first.out_w, first.out_h);
+            l->out_h = l->out_w = l->out_c = 0;
+        }
+    }
+    l->inputs = l->outputs;
+    l->delta =  realloc(l->delta, l->outputs*l->batch*sizeof(float));
+    l->output = realloc(l->output, l->outputs*l->batch*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->output_gpu);
+    cuda_free(l->delta_gpu);
+    l->output_gpu  = cuda_make_array(l->output, l->outputs*l->batch);
+    l->delta_gpu   = cuda_make_array(l->delta,  l->outputs*l->batch);
+#endif
+    
+}
+
+void forward_route_layer(const route_layer l, network net)
+{
+    int i, j;
+    int offset = 0;
+    for(i = 0; i < l.n; ++i){
+        int index = l.input_layers[i];
+        float *input = net.layers[index].output;
+        int input_size = l.input_sizes[i];
+        for(j = 0; j < l.batch; ++j){
+            copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1);
+        }
+        offset += input_size;
+    }
+}
+
+void backward_route_layer(const route_layer l, network net)
+{
+    int i, j;
+    int offset = 0;
+    for(i = 0; i < l.n; ++i){
+        int index = l.input_layers[i];
+        float *delta = net.layers[index].delta;
+        int input_size = l.input_sizes[i];
+        for(j = 0; j < l.batch; ++j){
+            axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1);
+        }
+        offset += input_size;
+    }
+}
+
+#ifdef GPU
+void forward_route_layer_gpu(const route_layer l, network net)
+{
+    int i, j;
+    int offset = 0;
+    for(i = 0; i < l.n; ++i){
+        int index = l.input_layers[i];
+        float *input = net.layers[index].output_gpu;
+        int input_size = l.input_sizes[i];
+        for(j = 0; j < l.batch; ++j){
+            copy_gpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1);
+        }
+        offset += input_size;
+    }
+}
+
+void backward_route_layer_gpu(const route_layer l, network net)
+{
+    int i, j;
+    int offset = 0;
+    for(i = 0; i < l.n; ++i){
+        int index = l.input_layers[i];
+        float *delta = net.layers[index].delta_gpu;
+        int input_size = l.input_sizes[i];
+        for(j = 0; j < l.batch; ++j){
+            axpy_gpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1);
+        }
+        offset += input_size;
+    }
+}
+#endif
diff --git a/hanzi_detection/src/route_layer.h b/hanzi_detection/src/route_layer.h
new file mode 100755
index 0000000..1d40330
--- /dev/null
+++ b/hanzi_detection/src/route_layer.h
@@ -0,0 +1,18 @@
+#ifndef ROUTE_LAYER_H
+#define ROUTE_LAYER_H
+#include "network.h"
+#include "layer.h"
+
+typedef layer route_layer;
+
+route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size);
+void forward_route_layer(const route_layer l, network net);
+void backward_route_layer(const route_layer l, network net);
+void resize_route_layer(route_layer *l, network *net);
+
+#ifdef GPU
+void forward_route_layer_gpu(const route_layer l, network net);
+void backward_route_layer_gpu(const route_layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/shortcut_layer.c b/hanzi_detection/src/shortcut_layer.c
new file mode 100755
index 0000000..49d17f5
--- /dev/null
+++ b/hanzi_detection/src/shortcut_layer.c
@@ -0,0 +1,90 @@
+#include "shortcut_layer.h"
+#include "cuda.h"
+#include "blas.h"
+#include "activations.h"
+
+#include <stdio.h>
+#include <assert.h>
+
+layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2)
+{
+    fprintf(stderr, "res  %3d                %4d x%4d x%4d   ->  %4d x%4d x%4d\n",index, w2,h2,c2, w,h,c);
+    layer l = {0};
+    l.type = SHORTCUT;
+    l.batch = batch;
+    l.w = w2;
+    l.h = h2;
+    l.c = c2;
+    l.out_w = w;
+    l.out_h = h;
+    l.out_c = c;
+    l.outputs = w*h*c;
+    l.inputs = l.outputs;
+
+    l.index = index;
+
+    l.delta =  calloc(l.outputs*batch, sizeof(float));
+    l.output = calloc(l.outputs*batch, sizeof(float));;
+
+    l.forward = forward_shortcut_layer;
+    l.backward = backward_shortcut_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_shortcut_layer_gpu;
+    l.backward_gpu = backward_shortcut_layer_gpu;
+
+    l.delta_gpu =  cuda_make_array(l.delta, l.outputs*batch);
+    l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
+    #endif
+    return l;
+}
+
+void resize_shortcut_layer(layer *l, int w, int h)
+{
+    assert(l->w == l->out_w);
+    assert(l->h == l->out_h);
+    l->w = l->out_w = w;
+    l->h = l->out_h = h;
+    l->outputs = w*h*l->out_c;
+    l->inputs = l->outputs;
+    l->delta =  realloc(l->delta, l->outputs*l->batch*sizeof(float));
+    l->output = realloc(l->output, l->outputs*l->batch*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->output_gpu);
+    cuda_free(l->delta_gpu);
+    l->output_gpu  = cuda_make_array(l->output, l->outputs*l->batch);
+    l->delta_gpu   = cuda_make_array(l->delta,  l->outputs*l->batch);
+#endif
+    
+}
+
+
+void forward_shortcut_layer(const layer l, network net)
+{
+    copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
+    shortcut_cpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output);
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_shortcut_layer(const layer l, network net)
+{
+    gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+    axpy_cpu(l.outputs*l.batch, l.alpha, l.delta, 1, net.delta, 1);
+    shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta);
+}
+
+#ifdef GPU
+void forward_shortcut_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
+    shortcut_gpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output_gpu);
+    activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_shortcut_layer_gpu(const layer l, network net)
+{
+    gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+    axpy_gpu(l.outputs*l.batch, l.alpha, l.delta_gpu, 1, net.delta_gpu, 1);
+    shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta_gpu);
+}
+#endif
diff --git a/hanzi_detection/src/shortcut_layer.h b/hanzi_detection/src/shortcut_layer.h
new file mode 100755
index 0000000..5f684fc
--- /dev/null
+++ b/hanzi_detection/src/shortcut_layer.h
@@ -0,0 +1,17 @@
+#ifndef SHORTCUT_LAYER_H
+#define SHORTCUT_LAYER_H
+
+#include "layer.h"
+#include "network.h"
+
+layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2);
+void forward_shortcut_layer(const layer l, network net);
+void backward_shortcut_layer(const layer l, network net);
+void resize_shortcut_layer(layer *l, int w, int h);
+
+#ifdef GPU
+void forward_shortcut_layer_gpu(const layer l, network net);
+void backward_shortcut_layer_gpu(const layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/softmax_layer.c b/hanzi_detection/src/softmax_layer.c
new file mode 100755
index 0000000..9cbc6be
--- /dev/null
+++ b/hanzi_detection/src/softmax_layer.c
@@ -0,0 +1,107 @@
+#include "softmax_layer.h"
+#include "blas.h"
+#include "cuda.h"
+
+#include <float.h>
+#include <math.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <assert.h>
+
+softmax_layer make_softmax_layer(int batch, int inputs, int groups)
+{
+    assert(inputs%groups == 0);
+    fprintf(stderr, "softmax                                        %4d\n",  inputs);
+    softmax_layer l = {0};
+    l.type = SOFTMAX;
+    l.batch = batch;
+    l.groups = groups;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.loss = calloc(inputs*batch, sizeof(float));
+    l.output = calloc(inputs*batch, sizeof(float));
+    l.delta = calloc(inputs*batch, sizeof(float));
+    l.cost = calloc(1, sizeof(float));
+
+    l.forward = forward_softmax_layer;
+    l.backward = backward_softmax_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_softmax_layer_gpu;
+    l.backward_gpu = backward_softmax_layer_gpu;
+
+    l.output_gpu = cuda_make_array(l.output, inputs*batch); 
+    l.loss_gpu = cuda_make_array(l.loss, inputs*batch); 
+    l.delta_gpu = cuda_make_array(l.delta, inputs*batch); 
+    #endif
+    return l;
+}
+
+void forward_softmax_layer(const softmax_layer l, network net)
+{
+    if(l.softmax_tree){
+        int i;
+        int count = 0;
+        for (i = 0; i < l.softmax_tree->groups; ++i) {
+            int group_size = l.softmax_tree->group_size[i];
+            softmax_cpu(net.input + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output + count);
+            count += group_size;
+        }
+    } else {
+        softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output);
+    }
+
+    if(net.truth && !l.noloss){
+        softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss);
+        l.cost[0] = sum_array(l.loss, l.batch*l.inputs);
+    }
+}
+
+void backward_softmax_layer(const softmax_layer l, network net)
+{
+    axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void pull_softmax_layer_output(const softmax_layer layer)
+{
+    cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
+}
+
+void forward_softmax_layer_gpu(const softmax_layer l, network net)
+{
+    if(l.softmax_tree){
+        softmax_tree(net.input_gpu, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree);
+        /*
+        int i;
+        int count = 0;
+        for (i = 0; i < l.softmax_tree->groups; ++i) {
+            int group_size = l.softmax_tree->group_size[i];
+            softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count);
+            count += group_size;
+        }
+        */
+    } else {
+        if(l.spatial){
+            softmax_gpu(net.input_gpu, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu);
+        }else{
+            softmax_gpu(net.input_gpu, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu);
+        }
+    }
+    if(net.truth && !l.noloss){
+        softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu);
+        if(l.softmax_tree){
+            mask_gpu(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth_gpu, 0);
+            mask_gpu(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth_gpu, 0);
+        }
+        cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs);
+        l.cost[0] = sum_array(l.loss, l.batch*l.inputs);
+    }
+}
+
+void backward_softmax_layer_gpu(const softmax_layer layer, network net)
+{
+    axpy_gpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta_gpu, 1);
+}
+
+#endif
diff --git a/hanzi_detection/src/softmax_layer.h b/hanzi_detection/src/softmax_layer.h
new file mode 100755
index 0000000..2e3ffe0
--- /dev/null
+++ b/hanzi_detection/src/softmax_layer.h
@@ -0,0 +1,19 @@
+#ifndef SOFTMAX_LAYER_H
+#define SOFTMAX_LAYER_H
+#include "layer.h"
+#include "network.h"
+
+typedef layer softmax_layer;
+
+void softmax_array(float *input, int n, float temp, float *output);
+softmax_layer make_softmax_layer(int batch, int inputs, int groups);
+void forward_softmax_layer(const softmax_layer l, network net);
+void backward_softmax_layer(const softmax_layer l, network net);
+
+#ifdef GPU
+void pull_softmax_layer_output(const softmax_layer l);
+void forward_softmax_layer_gpu(const softmax_layer l, network net);
+void backward_softmax_layer_gpu(const softmax_layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/stb_image.h b/hanzi_detection/src/stb_image.h
new file mode 100755
index 0000000..d9c21bc
--- /dev/null
+++ b/hanzi_detection/src/stb_image.h
@@ -0,0 +1,7462 @@
+/* stb_image - v2.19 - public domain image loader - http://nothings.org/stb
+                                  no warranty implied; use at your own risk
+
+   Do this:
+      #define STB_IMAGE_IMPLEMENTATION
+   before you include this file in *one* C or C++ file to create the implementation.
+
+   // i.e. it should look like this:
+   #include ...
+   #include ...
+   #include ...
+   #define STB_IMAGE_IMPLEMENTATION
+   #include "stb_image.h"
+
+   You can #define STBI_ASSERT(x) before the #include to avoid using assert.h.
+   And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free
+
+
+   QUICK NOTES:
+      Primarily of interest to game developers and other people who can
+          avoid problematic images and only need the trivial interface
+
+      JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib)
+      PNG 1/2/4/8/16-bit-per-channel
+
+      TGA (not sure what subset, if a subset)
+      BMP non-1bpp, non-RLE
+      PSD (composited view only, no extra channels, 8/16 bit-per-channel)
+
+      GIF (*comp always reports as 4-channel)
+      HDR (radiance rgbE format)
+      PIC (Softimage PIC)
+      PNM (PPM and PGM binary only)
+
+      Animated GIF still needs a proper API, but here's one way to do it:
+          http://gist.github.com/urraka/685d9a6340b26b830d49
+
+      - decode from memory or through FILE (define STBI_NO_STDIO to remove code)
+      - decode from arbitrary I/O callbacks
+      - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON)
+
+   Full documentation under "DOCUMENTATION" below.
+
+
+LICENSE
+
+  See end of file for license information.
+
+RECENT REVISION HISTORY:
+
+      2.19  (2018-02-11) fix warning
+      2.18  (2018-01-30) fix warnings
+      2.17  (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings
+      2.16  (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes
+      2.15  (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC
+      2.14  (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs
+      2.13  (2016-12-04) experimental 16-bit API, only for PNG so far; fixes
+      2.12  (2016-04-02) fix typo in 2.11 PSD fix that caused crashes
+      2.11  (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64
+                         RGB-format JPEG; remove white matting in PSD;
+                         allocate large structures on the stack;
+                         correct channel count for PNG & BMP
+      2.10  (2016-01-22) avoid warning introduced in 2.09
+      2.09  (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED
+
+   See end of file for full revision history.
+
+
+ ============================    Contributors    =========================
+
+ Image formats                          Extensions, features
+    Sean Barrett (jpeg, png, bmp)          Jetro Lauha (stbi_info)
+    Nicolas Schulz (hdr, psd)              Martin "SpartanJ" Golini (stbi_info)
+    Jonathan Dummer (tga)                  James "moose2000" Brown (iPhone PNG)
+    Jean-Marc Lienher (gif)                Ben "Disch" Wenger (io callbacks)
+    Tom Seddon (pic)                       Omar Cornut (1/2/4-bit PNG)
+    Thatcher Ulrich (psd)                  Nicolas Guillemot (vertical flip)
+    Ken Miller (pgm, ppm)                  Richard Mitton (16-bit PSD)
+    github:urraka (animated gif)           Junggon Kim (PNM comments)
+    Christopher Forseth (animated gif)     Daniel Gibson (16-bit TGA)
+                                           socks-the-fox (16-bit PNG)
+                                           Jeremy Sawicki (handle all ImageNet JPGs)
+ Optimizations & bugfixes                  Mikhail Morozov (1-bit BMP)
+    Fabian "ryg" Giesen                    Anael Seghezzi (is-16-bit query)
+    Arseny Kapoulkine
+    John-Mark Allen
+
+ Bug & warning fixes
+    Marc LeBlanc            David Woo          Guillaume George   Martins Mozeiko
+    Christpher Lloyd        Jerry Jansson      Joseph Thomson     Phil Jordan
+    Dave Moore              Roy Eltham         Hayaki Saito       Nathan Reed
+    Won Chun                Luke Graham        Johan Duparc       Nick Verigakis
+    the Horde3D community   Thomas Ruf         Ronny Chevalier    github:rlyeh
+    Janez Zemva             John Bartholomew   Michal Cichon      github:romigrou
+    Jonathan Blow           Ken Hamada         Tero Hanninen      github:svdijk
+    Laurent Gomila          Cort Stratton      Sergio Gonzalez    github:snagar
+    Aruelien Pocheville     Thibault Reuille   Cass Everitt       github:Zelex
+    Ryamond Barbiero        Paul Du Bois       Engin Manap        github:grim210
+    Aldo Culquicondor       Philipp Wiesemann  Dale Weiler        github:sammyhw
+    Oriol Ferrer Mesia      Josh Tobin         Matthew Gregan     github:phprus
+    Julian Raschke          Gregory Mullen     Baldur Karlsson    github:poppolopoppo
+    Christian Floisand      Kevin Schmidt                         github:darealshinji
+    Blazej Dariusz Roszkowski                                     github:Michaelangel007
+*/
+
+#ifndef STBI_INCLUDE_STB_IMAGE_H
+#define STBI_INCLUDE_STB_IMAGE_H
+
+// DOCUMENTATION
+//
+// Limitations:
+//    - no 12-bit-per-channel JPEG
+//    - no JPEGs with arithmetic coding
+//    - GIF always returns *comp=4
+//
+// Basic usage (see HDR discussion below for HDR usage):
+//    int x,y,n;
+//    unsigned char *data = stbi_load(filename, &x, &y, &n, 0);
+//    // ... process data if not NULL ...
+//    // ... x = width, y = height, n = # 8-bit components per pixel ...
+//    // ... replace '0' with '1'..'4' to force that many components per pixel
+//    // ... but 'n' will always be the number that it would have been if you said 0
+//    stbi_image_free(data)
+//
+// Standard parameters:
+//    int *x                 -- outputs image width in pixels
+//    int *y                 -- outputs image height in pixels
+//    int *channels_in_file  -- outputs # of image components in image file
+//    int desired_channels   -- if non-zero, # of image components requested in result
+//
+// The return value from an image loader is an 'unsigned char *' which points
+// to the pixel data, or NULL on an allocation failure or if the image is
+// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels,
+// with each pixel consisting of N interleaved 8-bit components; the first
+// pixel pointed to is top-left-most in the image. There is no padding between
+// image scanlines or between pixels, regardless of format. The number of
+// components N is 'desired_channels' if desired_channels is non-zero, or
+// *channels_in_file otherwise. If desired_channels is non-zero,
+// *channels_in_file has the number of components that _would_ have been
+// output otherwise. E.g. if you set desired_channels to 4, you will always
+// get RGBA output, but you can check *channels_in_file to see if it's trivially
+// opaque because e.g. there were only 3 channels in the source image.
+//
+// An output image with N components has the following components interleaved
+// in this order in each pixel:
+//
+//     N=#comp     components
+//       1           grey
+//       2           grey, alpha
+//       3           red, green, blue
+//       4           red, green, blue, alpha
+//
+// If image loading fails for any reason, the return value will be NULL,
+// and *x, *y, *channels_in_file will be unchanged. The function
+// stbi_failure_reason() can be queried for an extremely brief, end-user
+// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS
+// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly
+// more user-friendly ones.
+//
+// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized.
+//
+// ===========================================================================
+//
+// Philosophy
+//
+// stb libraries are designed with the following priorities:
+//
+//    1. easy to use
+//    2. easy to maintain
+//    3. good performance
+//
+// Sometimes I let "good performance" creep up in priority over "easy to maintain",
+// and for best performance I may provide less-easy-to-use APIs that give higher
+// performance, in addition to the easy to use ones. Nevertheless, it's important
+// to keep in mind that from the standpoint of you, a client of this library,
+// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all.
+//
+// Some secondary priorities arise directly from the first two, some of which
+// make more explicit reasons why performance can't be emphasized.
+//
+//    - Portable ("ease of use")
+//    - Small source code footprint ("easy to maintain")
+//    - No dependencies ("ease of use")
+//
+// ===========================================================================
+//
+// I/O callbacks
+//
+// I/O callbacks allow you to read from arbitrary sources, like packaged
+// files or some other source. Data read from callbacks are processed
+// through a small internal buffer (currently 128 bytes) to try to reduce
+// overhead.
+//
+// The three functions you must define are "read" (reads some bytes of data),
+// "skip" (skips some bytes of data), "eof" (reports if the stream is at the end).
+//
+// ===========================================================================
+//
+// SIMD support
+//
+// The JPEG decoder will try to automatically use SIMD kernels on x86 when
+// supported by the compiler. For ARM Neon support, you must explicitly
+// request it.
+//
+// (The old do-it-yourself SIMD API is no longer supported in the current
+// code.)
+//
+// On x86, SSE2 will automatically be used when available based on a run-time
+// test; if not, the generic C versions are used as a fall-back. On ARM targets,
+// the typical path is to have separate builds for NEON and non-NEON devices
+// (at least this is true for iOS and Android). Therefore, the NEON support is
+// toggled by a build flag: define STBI_NEON to get NEON loops.
+//
+// If for some reason you do not want to use any of SIMD code, or if
+// you have issues compiling it, you can disable it entirely by
+// defining STBI_NO_SIMD.
+//
+// ===========================================================================
+//
+// HDR image support   (disable by defining STBI_NO_HDR)
+//
+// stb_image now supports loading HDR images in general, and currently
+// the Radiance .HDR file format, although the support is provided
+// generically. You can still load any file through the existing interface;
+// if you attempt to load an HDR file, it will be automatically remapped to
+// LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1;
+// both of these constants can be reconfigured through this interface:
+//
+//     stbi_hdr_to_ldr_gamma(2.2f);
+//     stbi_hdr_to_ldr_scale(1.0f);
+//
+// (note, do not use _inverse_ constants; stbi_image will invert them
+// appropriately).
+//
+// Additionally, there is a new, parallel interface for loading files as
+// (linear) floats to preserve the full dynamic range:
+//
+//    float *data = stbi_loadf(filename, &x, &y, &n, 0);
+//
+// If you load LDR images through this interface, those images will
+// be promoted to floating point values, run through the inverse of
+// constants corresponding to the above:
+//
+//     stbi_ldr_to_hdr_scale(1.0f);
+//     stbi_ldr_to_hdr_gamma(2.2f);
+//
+// Finally, given a filename (or an open file or memory block--see header
+// file for details) containing image data, you can query for the "most
+// appropriate" interface to use (that is, whether the image is HDR or
+// not), using:
+//
+//     stbi_is_hdr(char *filename);
+//
+// ===========================================================================
+//
+// iPhone PNG support:
+//
+// By default we convert iphone-formatted PNGs back to RGB, even though
+// they are internally encoded differently. You can disable this conversion
+// by by calling stbi_convert_iphone_png_to_rgb(0), in which case
+// you will always just get the native iphone "format" through (which
+// is BGR stored in RGB).
+//
+// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per
+// pixel to remove any premultiplied alpha *only* if the image file explicitly
+// says there's premultiplied data (currently only happens in iPhone images,
+// and only if iPhone convert-to-rgb processing is on).
+//
+// ===========================================================================
+//
+// ADDITIONAL CONFIGURATION
+//
+//  - You can suppress implementation of any of the decoders to reduce
+//    your code footprint by #defining one or more of the following
+//    symbols before creating the implementation.
+//
+//        STBI_NO_JPEG
+//        STBI_NO_PNG
+//        STBI_NO_BMP
+//        STBI_NO_PSD
+//        STBI_NO_TGA
+//        STBI_NO_GIF
+//        STBI_NO_HDR
+//        STBI_NO_PIC
+//        STBI_NO_PNM   (.ppm and .pgm)
+//
+//  - You can request *only* certain decoders and suppress all other ones
+//    (this will be more forward-compatible, as addition of new decoders
+//    doesn't require you to disable them explicitly):
+//
+//        STBI_ONLY_JPEG
+//        STBI_ONLY_PNG
+//        STBI_ONLY_BMP
+//        STBI_ONLY_PSD
+//        STBI_ONLY_TGA
+//        STBI_ONLY_GIF
+//        STBI_ONLY_HDR
+//        STBI_ONLY_PIC
+//        STBI_ONLY_PNM   (.ppm and .pgm)
+//
+//   - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still
+//     want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB
+//
+
+
+#ifndef STBI_NO_STDIO
+#include <stdio.h>
+#endif // STBI_NO_STDIO
+
+#define STBI_VERSION 1
+
+enum
+{
+   STBI_default = 0, // only used for desired_channels
+
+   STBI_grey       = 1,
+   STBI_grey_alpha = 2,
+   STBI_rgb        = 3,
+   STBI_rgb_alpha  = 4
+};
+
+typedef unsigned char stbi_uc;
+typedef unsigned short stbi_us;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#ifdef STB_IMAGE_STATIC
+#define STBIDEF static
+#else
+#define STBIDEF extern
+#endif
+
+//////////////////////////////////////////////////////////////////////////////
+//
+// PRIMARY API - works on images of any type
+//
+
+//
+// load image by filename, open file, or memory buffer
+//
+
+typedef struct
+{
+   int      (*read)  (void *user,char *data,int size);   // fill 'data' with 'size' bytes.  return number of bytes actually read
+   void     (*skip)  (void *user,int n);                 // skip the next 'n' bytes, or 'unget' the last -n bytes if negative
+   int      (*eof)   (void *user);                       // returns nonzero if we are at end of file/data
+} stbi_io_callbacks;
+
+////////////////////////////////////
+//
+// 8-bits-per-channel interface
+//
+
+STBIDEF stbi_uc *stbi_load_from_memory   (stbi_uc           const *buffer, int len   , int *x, int *y, int *channels_in_file, int desired_channels);
+STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk  , void *user, int *x, int *y, int *channels_in_file, int desired_channels);
+#ifndef STBI_NO_GIF
+STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp);
+#endif
+
+
+#ifndef STBI_NO_STDIO
+STBIDEF stbi_uc *stbi_load            (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels);
+STBIDEF stbi_uc *stbi_load_from_file  (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels);
+// for stbi_load_from_file, file pointer is left pointing immediately after image
+#endif
+
+////////////////////////////////////
+//
+// 16-bits-per-channel interface
+//
+
+STBIDEF stbi_us *stbi_load_16_from_memory   (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels);
+STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels);
+
+#ifndef STBI_NO_STDIO
+STBIDEF stbi_us *stbi_load_16          (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels);
+STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels);
+#endif
+
+////////////////////////////////////
+//
+// float-per-channel interface
+//
+#ifndef STBI_NO_LINEAR
+   STBIDEF float *stbi_loadf_from_memory     (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels);
+   STBIDEF float *stbi_loadf_from_callbacks  (stbi_io_callbacks const *clbk, void *user, int *x, int *y,  int *channels_in_file, int desired_channels);
+
+   #ifndef STBI_NO_STDIO
+   STBIDEF float *stbi_loadf            (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels);
+   STBIDEF float *stbi_loadf_from_file  (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels);
+   #endif
+#endif
+
+#ifndef STBI_NO_HDR
+   STBIDEF void   stbi_hdr_to_ldr_gamma(float gamma);
+   STBIDEF void   stbi_hdr_to_ldr_scale(float scale);
+#endif // STBI_NO_HDR
+
+#ifndef STBI_NO_LINEAR
+   STBIDEF void   stbi_ldr_to_hdr_gamma(float gamma);
+   STBIDEF void   stbi_ldr_to_hdr_scale(float scale);
+#endif // STBI_NO_LINEAR
+
+// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR
+STBIDEF int    stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user);
+STBIDEF int    stbi_is_hdr_from_memory(stbi_uc const *buffer, int len);
+#ifndef STBI_NO_STDIO
+STBIDEF int      stbi_is_hdr          (char const *filename);
+STBIDEF int      stbi_is_hdr_from_file(FILE *f);
+#endif // STBI_NO_STDIO
+
+
+// get a VERY brief reason for failure
+// NOT THREADSAFE
+STBIDEF const char *stbi_failure_reason  (void);
+
+// free the loaded image -- this is just free()
+STBIDEF void     stbi_image_free      (void *retval_from_stbi_load);
+
+// get image dimensions & components without fully decoding
+STBIDEF int      stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp);
+STBIDEF int      stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp);
+STBIDEF int      stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len);
+STBIDEF int      stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user);
+
+#ifndef STBI_NO_STDIO
+STBIDEF int      stbi_info               (char const *filename,     int *x, int *y, int *comp);
+STBIDEF int      stbi_info_from_file     (FILE *f,                  int *x, int *y, int *comp);
+STBIDEF int      stbi_is_16_bit          (char const *filename);
+STBIDEF int      stbi_is_16_bit_from_file(FILE *f);
+#endif
+
+
+
+// for image formats that explicitly notate that they have premultiplied alpha,
+// we just return the colors as stored in the file. set this flag to force
+// unpremultiplication. results are undefined if the unpremultiply overflow.
+STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply);
+
+// indicate whether we should process iphone images back to canonical format,
+// or just pass them through "as-is"
+STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert);
+
+// flip the image vertically, so the first pixel in the output array is the bottom left
+STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip);
+
+// ZLIB client - used by PNG, available for other purposes
+
+STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen);
+STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header);
+STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen);
+STBIDEF int   stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen);
+
+STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen);
+STBIDEF int   stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen);
+
+
+#ifdef __cplusplus
+}
+#endif
+
+//
+//
+////   end header file   /////////////////////////////////////////////////////
+#endif // STBI_INCLUDE_STB_IMAGE_H
+
+#ifdef STB_IMAGE_IMPLEMENTATION
+
+#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \
+  || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \
+  || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \
+  || defined(STBI_ONLY_ZLIB)
+   #ifndef STBI_ONLY_JPEG
+   #define STBI_NO_JPEG
+   #endif
+   #ifndef STBI_ONLY_PNG
+   #define STBI_NO_PNG
+   #endif
+   #ifndef STBI_ONLY_BMP
+   #define STBI_NO_BMP
+   #endif
+   #ifndef STBI_ONLY_PSD
+   #define STBI_NO_PSD
+   #endif
+   #ifndef STBI_ONLY_TGA
+   #define STBI_NO_TGA
+   #endif
+   #ifndef STBI_ONLY_GIF
+   #define STBI_NO_GIF
+   #endif
+   #ifndef STBI_ONLY_HDR
+   #define STBI_NO_HDR
+   #endif
+   #ifndef STBI_ONLY_PIC
+   #define STBI_NO_PIC
+   #endif
+   #ifndef STBI_ONLY_PNM
+   #define STBI_NO_PNM
+   #endif
+#endif
+
+#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB)
+#define STBI_NO_ZLIB
+#endif
+
+
+#include <stdarg.h>
+#include <stddef.h> // ptrdiff_t on osx
+#include <stdlib.h>
+#include <string.h>
+#include <limits.h>
+
+#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR)
+#include <math.h>  // ldexp, pow
+#endif
+
+#ifndef STBI_NO_STDIO
+#include <stdio.h>
+#endif
+
+#ifndef STBI_ASSERT
+#include <assert.h>
+#define STBI_ASSERT(x) assert(x)
+#endif
+
+
+#ifndef _MSC_VER
+   #ifdef __cplusplus
+   #define stbi_inline inline
+   #else
+   #define stbi_inline
+   #endif
+#else
+   #define stbi_inline __forceinline
+#endif
+
+
+#ifdef _MSC_VER
+typedef unsigned short stbi__uint16;
+typedef   signed short stbi__int16;
+typedef unsigned int   stbi__uint32;
+typedef   signed int   stbi__int32;
+#else
+#include <stdint.h>
+typedef uint16_t stbi__uint16;
+typedef int16_t  stbi__int16;
+typedef uint32_t stbi__uint32;
+typedef int32_t  stbi__int32;
+#endif
+
+// should produce compiler error if size is wrong
+typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1];
+
+#ifdef _MSC_VER
+#define STBI_NOTUSED(v)  (void)(v)
+#else
+#define STBI_NOTUSED(v)  (void)sizeof(v)
+#endif
+
+#ifdef _MSC_VER
+#define STBI_HAS_LROTL
+#endif
+
+#ifdef STBI_HAS_LROTL
+   #define stbi_lrot(x,y)  _lrotl(x,y)
+#else
+   #define stbi_lrot(x,y)  (((x) << (y)) | ((x) >> (32 - (y))))
+#endif
+
+#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED))
+// ok
+#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED)
+// ok
+#else
+#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)."
+#endif
+
+#ifndef STBI_MALLOC
+#define STBI_MALLOC(sz)           malloc(sz)
+#define STBI_REALLOC(p,newsz)     realloc(p,newsz)
+#define STBI_FREE(p)              free(p)
+#endif
+
+#ifndef STBI_REALLOC_SIZED
+#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz)
+#endif
+
+// x86/x64 detection
+#if defined(__x86_64__) || defined(_M_X64)
+#define STBI__X64_TARGET
+#elif defined(__i386) || defined(_M_IX86)
+#define STBI__X86_TARGET
+#endif
+
+#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD)
+// gcc doesn't support sse2 intrinsics unless you compile with -msse2,
+// which in turn means it gets to use SSE2 everywhere. This is unfortunate,
+// but previous attempts to provide the SSE2 functions with runtime
+// detection caused numerous issues. The way architecture extensions are
+// exposed in GCC/Clang is, sadly, not really suited for one-file libs.
+// New behavior: if compiled with -msse2, we use SSE2 without any
+// detection; if not, we don't use it at all.
+#define STBI_NO_SIMD
+#endif
+
+#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD)
+// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET
+//
+// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the
+// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant.
+// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not
+// simultaneously enabling "-mstackrealign".
+//
+// See https://github.com/nothings/stb/issues/81 for more information.
+//
+// So default to no SSE2 on 32-bit MinGW. If you've read this far and added
+// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2.
+#define STBI_NO_SIMD
+#endif
+
+#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET))
+#define STBI_SSE2
+#include <emmintrin.h>
+
+#ifdef _MSC_VER
+
+#if _MSC_VER >= 1400  // not VC6
+#include <intrin.h> // __cpuid
+static int stbi__cpuid3(void)
+{
+   int info[4];
+   __cpuid(info,1);
+   return info[3];
+}
+#else
+static int stbi__cpuid3(void)
+{
+   int res;
+   __asm {
+      mov  eax,1
+      cpuid
+      mov  res,edx
+   }
+   return res;
+}
+#endif
+
+#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name
+
+static int stbi__sse2_available(void)
+{
+   int info3 = stbi__cpuid3();
+   return ((info3 >> 26) & 1) != 0;
+}
+#else // assume GCC-style if not VC++
+#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16)))
+
+static int stbi__sse2_available(void)
+{
+   // If we're even attempting to compile this on GCC/Clang, that means
+   // -msse2 is on, which means the compiler is allowed to use SSE2
+   // instructions at will, and so are we.
+   return 1;
+}
+#endif
+#endif
+
+// ARM NEON
+#if defined(STBI_NO_SIMD) && defined(STBI_NEON)
+#undef STBI_NEON
+#endif
+
+#ifdef STBI_NEON
+#include <arm_neon.h>
+// assume GCC or Clang on ARM targets
+#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16)))
+#endif
+
+#ifndef STBI_SIMD_ALIGN
+#define STBI_SIMD_ALIGN(type, name) type name
+#endif
+
+///////////////////////////////////////////////
+//
+//  stbi__context struct and start_xxx functions
+
+// stbi__context structure is our basic context used by all images, so it
+// contains all the IO context, plus some basic image information
+typedef struct
+{
+   stbi__uint32 img_x, img_y;
+   int img_n, img_out_n;
+
+   stbi_io_callbacks io;
+   void *io_user_data;
+
+   int read_from_callbacks;
+   int buflen;
+   stbi_uc buffer_start[128];
+
+   stbi_uc *img_buffer, *img_buffer_end;
+   stbi_uc *img_buffer_original, *img_buffer_original_end;
+} stbi__context;
+
+
+static void stbi__refill_buffer(stbi__context *s);
+
+// initialize a memory-decode context
+static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len)
+{
+   s->io.read = NULL;
+   s->read_from_callbacks = 0;
+   s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer;
+   s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len;
+}
+
+// initialize a callback-based context
+static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user)
+{
+   s->io = *c;
+   s->io_user_data = user;
+   s->buflen = sizeof(s->buffer_start);
+   s->read_from_callbacks = 1;
+   s->img_buffer_original = s->buffer_start;
+   stbi__refill_buffer(s);
+   s->img_buffer_original_end = s->img_buffer_end;
+}
+
+#ifndef STBI_NO_STDIO
+
+static int stbi__stdio_read(void *user, char *data, int size)
+{
+   return (int) fread(data,1,size,(FILE*) user);
+}
+
+static void stbi__stdio_skip(void *user, int n)
+{
+   fseek((FILE*) user, n, SEEK_CUR);
+}
+
+static int stbi__stdio_eof(void *user)
+{
+   return feof((FILE*) user);
+}
+
+static stbi_io_callbacks stbi__stdio_callbacks =
+{
+   stbi__stdio_read,
+   stbi__stdio_skip,
+   stbi__stdio_eof,
+};
+
+static void stbi__start_file(stbi__context *s, FILE *f)
+{
+   stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f);
+}
+
+//static void stop_file(stbi__context *s) { }
+
+#endif // !STBI_NO_STDIO
+
+static void stbi__rewind(stbi__context *s)
+{
+   // conceptually rewind SHOULD rewind to the beginning of the stream,
+   // but we just rewind to the beginning of the initial buffer, because
+   // we only use it after doing 'test', which only ever looks at at most 92 bytes
+   s->img_buffer = s->img_buffer_original;
+   s->img_buffer_end = s->img_buffer_original_end;
+}
+
+enum
+{
+   STBI_ORDER_RGB,
+   STBI_ORDER_BGR
+};
+
+typedef struct
+{
+   int bits_per_channel;
+   int num_channels;
+   int channel_order;
+} stbi__result_info;
+
+#ifndef STBI_NO_JPEG
+static int      stbi__jpeg_test(stbi__context *s);
+static void    *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_PNG
+static int      stbi__png_test(stbi__context *s);
+static void    *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__png_info(stbi__context *s, int *x, int *y, int *comp);
+static int      stbi__png_is16(stbi__context *s);
+#endif
+
+#ifndef STBI_NO_BMP
+static int      stbi__bmp_test(stbi__context *s);
+static void    *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_TGA
+static int      stbi__tga_test(stbi__context *s);
+static void    *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__tga_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_PSD
+static int      stbi__psd_test(stbi__context *s);
+static void    *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc);
+static int      stbi__psd_info(stbi__context *s, int *x, int *y, int *comp);
+static int      stbi__psd_is16(stbi__context *s);
+#endif
+
+#ifndef STBI_NO_HDR
+static int      stbi__hdr_test(stbi__context *s);
+static float   *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_PIC
+static int      stbi__pic_test(stbi__context *s);
+static void    *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__pic_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_GIF
+static int      stbi__gif_test(stbi__context *s);
+static void    *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static void    *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp);
+static int      stbi__gif_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+#ifndef STBI_NO_PNM
+static int      stbi__pnm_test(stbi__context *s);
+static void    *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri);
+static int      stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp);
+#endif
+
+// this is not threadsafe
+static const char *stbi__g_failure_reason;
+
+STBIDEF const char *stbi_failure_reason(void)
+{
+   return stbi__g_failure_reason;
+}
+
+static int stbi__err(const char *str)
+{
+   stbi__g_failure_reason = str;
+   return 0;
+}
+
+static void *stbi__malloc(size_t size)
+{
+    return STBI_MALLOC(size);
+}
+
+// stb_image uses ints pervasively, including for offset calculations.
+// therefore the largest decoded image size we can support with the
+// current code, even on 64-bit targets, is INT_MAX. this is not a
+// significant limitation for the intended use case.
+//
+// we do, however, need to make sure our size calculations don't
+// overflow. hence a few helper functions for size calculations that
+// multiply integers together, making sure that they're non-negative
+// and no overflow occurs.
+
+// return 1 if the sum is valid, 0 on overflow.
+// negative terms are considered invalid.
+static int stbi__addsizes_valid(int a, int b)
+{
+   if (b < 0) return 0;
+   // now 0 <= b <= INT_MAX, hence also
+   // 0 <= INT_MAX - b <= INTMAX.
+   // And "a + b <= INT_MAX" (which might overflow) is the
+   // same as a <= INT_MAX - b (no overflow)
+   return a <= INT_MAX - b;
+}
+
+// returns 1 if the product is valid, 0 on overflow.
+// negative factors are considered invalid.
+static int stbi__mul2sizes_valid(int a, int b)
+{
+   if (a < 0 || b < 0) return 0;
+   if (b == 0) return 1; // mul-by-0 is always safe
+   // portable way to check for no overflows in a*b
+   return a <= INT_MAX/b;
+}
+
+// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow
+static int stbi__mad2sizes_valid(int a, int b, int add)
+{
+   return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add);
+}
+
+// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow
+static int stbi__mad3sizes_valid(int a, int b, int c, int add)
+{
+   return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) &&
+      stbi__addsizes_valid(a*b*c, add);
+}
+
+// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow
+#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR)
+static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add)
+{
+   return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) &&
+      stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add);
+}
+#endif
+
+// mallocs with size overflow checking
+static void *stbi__malloc_mad2(int a, int b, int add)
+{
+   if (!stbi__mad2sizes_valid(a, b, add)) return NULL;
+   return stbi__malloc(a*b + add);
+}
+
+static void *stbi__malloc_mad3(int a, int b, int c, int add)
+{
+   if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL;
+   return stbi__malloc(a*b*c + add);
+}
+
+#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR)
+static void *stbi__malloc_mad4(int a, int b, int c, int d, int add)
+{
+   if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL;
+   return stbi__malloc(a*b*c*d + add);
+}
+#endif
+
+// stbi__err - error
+// stbi__errpf - error returning pointer to float
+// stbi__errpuc - error returning pointer to unsigned char
+
+#ifdef STBI_NO_FAILURE_STRINGS
+   #define stbi__err(x,y)  0
+#elif defined(STBI_FAILURE_USERMSG)
+   #define stbi__err(x,y)  stbi__err(y)
+#else
+   #define stbi__err(x,y)  stbi__err(x)
+#endif
+
+#define stbi__errpf(x,y)   ((float *)(size_t) (stbi__err(x,y)?NULL:NULL))
+#define stbi__errpuc(x,y)  ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL))
+
+STBIDEF void stbi_image_free(void *retval_from_stbi_load)
+{
+   STBI_FREE(retval_from_stbi_load);
+}
+
+#ifndef STBI_NO_LINEAR
+static float   *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp);
+#endif
+
+#ifndef STBI_NO_HDR
+static stbi_uc *stbi__hdr_to_ldr(float   *data, int x, int y, int comp);
+#endif
+
+static int stbi__vertically_flip_on_load = 0;
+
+STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip)
+{
+    stbi__vertically_flip_on_load = flag_true_if_should_flip;
+}
+
+static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc)
+{
+   memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields
+   ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed
+   ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order
+   ri->num_channels = 0;
+
+   #ifndef STBI_NO_JPEG
+   if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri);
+   #endif
+   #ifndef STBI_NO_PNG
+   if (stbi__png_test(s))  return stbi__png_load(s,x,y,comp,req_comp, ri);
+   #endif
+   #ifndef STBI_NO_BMP
+   if (stbi__bmp_test(s))  return stbi__bmp_load(s,x,y,comp,req_comp, ri);
+   #endif
+   #ifndef STBI_NO_GIF
+   if (stbi__gif_test(s))  return stbi__gif_load(s,x,y,comp,req_comp, ri);
+   #endif
+   #ifndef STBI_NO_PSD
+   if (stbi__psd_test(s))  return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc);
+   #endif
+   #ifndef STBI_NO_PIC
+   if (stbi__pic_test(s))  return stbi__pic_load(s,x,y,comp,req_comp, ri);
+   #endif
+   #ifndef STBI_NO_PNM
+   if (stbi__pnm_test(s))  return stbi__pnm_load(s,x,y,comp,req_comp, ri);
+   #endif
+
+   #ifndef STBI_NO_HDR
+   if (stbi__hdr_test(s)) {
+      float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri);
+      return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp);
+   }
+   #endif
+
+   #ifndef STBI_NO_TGA
+   // test tga last because it's a crappy test!
+   if (stbi__tga_test(s))
+      return stbi__tga_load(s,x,y,comp,req_comp, ri);
+   #endif
+
+   return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt");
+}
+
+static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels)
+{
+   int i;
+   int img_len = w * h * channels;
+   stbi_uc *reduced;
+
+   reduced = (stbi_uc *) stbi__malloc(img_len);
+   if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory");
+
+   for (i = 0; i < img_len; ++i)
+      reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling
+
+   STBI_FREE(orig);
+   return reduced;
+}
+
+static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels)
+{
+   int i;
+   int img_len = w * h * channels;
+   stbi__uint16 *enlarged;
+
+   enlarged = (stbi__uint16 *) stbi__malloc(img_len*2);
+   if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory");
+
+   for (i = 0; i < img_len; ++i)
+      enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff
+
+   STBI_FREE(orig);
+   return enlarged;
+}
+
+static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel)
+{
+   int row;
+   size_t bytes_per_row = (size_t)w * bytes_per_pixel;
+   stbi_uc temp[2048];
+   stbi_uc *bytes = (stbi_uc *)image;
+
+   for (row = 0; row < (h>>1); row++) {
+      stbi_uc *row0 = bytes + row*bytes_per_row;
+      stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row;
+      // swap row0 with row1
+      size_t bytes_left = bytes_per_row;
+      while (bytes_left) {
+         size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp);
+         memcpy(temp, row0, bytes_copy);
+         memcpy(row0, row1, bytes_copy);
+         memcpy(row1, temp, bytes_copy);
+         row0 += bytes_copy;
+         row1 += bytes_copy;
+         bytes_left -= bytes_copy;
+      }
+   }
+}
+
+static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel)
+{
+   int slice;
+   int slice_size = w * h * bytes_per_pixel;
+
+   stbi_uc *bytes = (stbi_uc *)image;
+   for (slice = 0; slice < z; ++slice) {
+      stbi__vertical_flip(bytes, w, h, bytes_per_pixel); 
+      bytes += slice_size; 
+   }
+}
+
+static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__result_info ri;
+   void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8);
+
+   if (result == NULL)
+      return NULL;
+
+   if (ri.bits_per_channel != 8) {
+      STBI_ASSERT(ri.bits_per_channel == 16);
+      result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp);
+      ri.bits_per_channel = 8;
+   }
+
+   // @TODO: move stbi__convert_format to here
+
+   if (stbi__vertically_flip_on_load) {
+      int channels = req_comp ? req_comp : *comp;
+      stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc));
+   }
+
+   return (unsigned char *) result;
+}
+
+static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__result_info ri;
+   void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16);
+
+   if (result == NULL)
+      return NULL;
+
+   if (ri.bits_per_channel != 16) {
+      STBI_ASSERT(ri.bits_per_channel == 8);
+      result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp);
+      ri.bits_per_channel = 16;
+   }
+
+   // @TODO: move stbi__convert_format16 to here
+   // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision
+
+   if (stbi__vertically_flip_on_load) {
+      int channels = req_comp ? req_comp : *comp;
+      stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16));
+   }
+
+   return (stbi__uint16 *) result;
+}
+
+#if !defined(STBI_NO_HDR) || !defined(STBI_NO_LINEAR)
+static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp)
+{
+   if (stbi__vertically_flip_on_load && result != NULL) {
+      int channels = req_comp ? req_comp : *comp;
+      stbi__vertical_flip(result, *x, *y, channels * sizeof(float));
+   }
+}
+#endif
+
+#ifndef STBI_NO_STDIO
+
+static FILE *stbi__fopen(char const *filename, char const *mode)
+{
+   FILE *f;
+#if defined(_MSC_VER) && _MSC_VER >= 1400
+   if (0 != fopen_s(&f, filename, mode))
+      f=0;
+#else
+   f = fopen(filename, mode);
+#endif
+   return f;
+}
+
+
+STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp)
+{
+   FILE *f = stbi__fopen(filename, "rb");
+   unsigned char *result;
+   if (!f) return stbi__errpuc("can't fopen", "Unable to open file");
+   result = stbi_load_from_file(f,x,y,comp,req_comp);
+   fclose(f);
+   return result;
+}
+
+STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp)
+{
+   unsigned char *result;
+   stbi__context s;
+   stbi__start_file(&s,f);
+   result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp);
+   if (result) {
+      // need to 'unget' all the characters in the IO buffer
+      fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR);
+   }
+   return result;
+}
+
+STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__uint16 *result;
+   stbi__context s;
+   stbi__start_file(&s,f);
+   result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp);
+   if (result) {
+      // need to 'unget' all the characters in the IO buffer
+      fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR);
+   }
+   return result;
+}
+
+STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp)
+{
+   FILE *f = stbi__fopen(filename, "rb");
+   stbi__uint16 *result;
+   if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file");
+   result = stbi_load_from_file_16(f,x,y,comp,req_comp);
+   fclose(f);
+   return result;
+}
+
+
+#endif //!STBI_NO_STDIO
+
+STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels)
+{
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels);
+}
+
+STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels)
+{
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user);
+   return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels);
+}
+
+STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp);
+}
+
+STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user);
+   return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp);
+}
+
+#ifndef STBI_NO_GIF
+STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp)
+{
+   unsigned char *result;
+   stbi__context s; 
+   stbi__start_mem(&s,buffer,len); 
+   
+   result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp);
+   if (stbi__vertically_flip_on_load) {
+      stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); 
+   }
+
+   return result; 
+}
+#endif
+
+#ifndef STBI_NO_LINEAR
+static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp)
+{
+   unsigned char *data;
+   #ifndef STBI_NO_HDR
+   if (stbi__hdr_test(s)) {
+      stbi__result_info ri;
+      float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri);
+      if (hdr_data)
+         stbi__float_postprocess(hdr_data,x,y,comp,req_comp);
+      return hdr_data;
+   }
+   #endif
+   data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp);
+   if (data)
+      return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp);
+   return stbi__errpf("unknown image type", "Image not of any known type, or corrupt");
+}
+
+STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__loadf_main(&s,x,y,comp,req_comp);
+}
+
+STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user);
+   return stbi__loadf_main(&s,x,y,comp,req_comp);
+}
+
+#ifndef STBI_NO_STDIO
+STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp)
+{
+   float *result;
+   FILE *f = stbi__fopen(filename, "rb");
+   if (!f) return stbi__errpf("can't fopen", "Unable to open file");
+   result = stbi_loadf_from_file(f,x,y,comp,req_comp);
+   fclose(f);
+   return result;
+}
+
+STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp)
+{
+   stbi__context s;
+   stbi__start_file(&s,f);
+   return stbi__loadf_main(&s,x,y,comp,req_comp);
+}
+#endif // !STBI_NO_STDIO
+
+#endif // !STBI_NO_LINEAR
+
+// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is
+// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always
+// reports false!
+
+STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len)
+{
+   #ifndef STBI_NO_HDR
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__hdr_test(&s);
+   #else
+   STBI_NOTUSED(buffer);
+   STBI_NOTUSED(len);
+   return 0;
+   #endif
+}
+
+#ifndef STBI_NO_STDIO
+STBIDEF int      stbi_is_hdr          (char const *filename)
+{
+   FILE *f = stbi__fopen(filename, "rb");
+   int result=0;
+   if (f) {
+      result = stbi_is_hdr_from_file(f);
+      fclose(f);
+   }
+   return result;
+}
+
+STBIDEF int stbi_is_hdr_from_file(FILE *f)
+{
+   #ifndef STBI_NO_HDR
+   long pos = ftell(f);
+   int res;
+   stbi__context s;
+   stbi__start_file(&s,f);
+   res = stbi__hdr_test(&s);
+   fseek(f, pos, SEEK_SET);
+   return res;
+   #else
+   STBI_NOTUSED(f);
+   return 0;
+   #endif
+}
+#endif // !STBI_NO_STDIO
+
+STBIDEF int      stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user)
+{
+   #ifndef STBI_NO_HDR
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user);
+   return stbi__hdr_test(&s);
+   #else
+   STBI_NOTUSED(clbk);
+   STBI_NOTUSED(user);
+   return 0;
+   #endif
+}
+
+#ifndef STBI_NO_LINEAR
+static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f;
+
+STBIDEF void   stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; }
+STBIDEF void   stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; }
+#endif
+
+static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f;
+
+STBIDEF void   stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; }
+STBIDEF void   stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; }
+
+
+//////////////////////////////////////////////////////////////////////////////
+//
+// Common code used by all image loaders
+//
+
+enum
+{
+   STBI__SCAN_load=0,
+   STBI__SCAN_type,
+   STBI__SCAN_header
+};
+
+static void stbi__refill_buffer(stbi__context *s)
+{
+   int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen);
+   if (n == 0) {
+      // at end of file, treat same as if from memory, but need to handle case
+      // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file
+      s->read_from_callbacks = 0;
+      s->img_buffer = s->buffer_start;
+      s->img_buffer_end = s->buffer_start+1;
+      *s->img_buffer = 0;
+   } else {
+      s->img_buffer = s->buffer_start;
+      s->img_buffer_end = s->buffer_start + n;
+   }
+}
+
+stbi_inline static stbi_uc stbi__get8(stbi__context *s)
+{
+   if (s->img_buffer < s->img_buffer_end)
+      return *s->img_buffer++;
+   if (s->read_from_callbacks) {
+      stbi__refill_buffer(s);
+      return *s->img_buffer++;
+   }
+   return 0;
+}
+
+stbi_inline static int stbi__at_eof(stbi__context *s)
+{
+   if (s->io.read) {
+      if (!(s->io.eof)(s->io_user_data)) return 0;
+      // if feof() is true, check if buffer = end
+      // special case: we've only got the special 0 character at the end
+      if (s->read_from_callbacks == 0) return 1;
+   }
+
+   return s->img_buffer >= s->img_buffer_end;
+}
+
+static void stbi__skip(stbi__context *s, int n)
+{
+   if (n < 0) {
+      s->img_buffer = s->img_buffer_end;
+      return;
+   }
+   if (s->io.read) {
+      int blen = (int) (s->img_buffer_end - s->img_buffer);
+      if (blen < n) {
+         s->img_buffer = s->img_buffer_end;
+         (s->io.skip)(s->io_user_data, n - blen);
+         return;
+      }
+   }
+   s->img_buffer += n;
+}
+
+static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n)
+{
+   if (s->io.read) {
+      int blen = (int) (s->img_buffer_end - s->img_buffer);
+      if (blen < n) {
+         int res, count;
+
+         memcpy(buffer, s->img_buffer, blen);
+
+         count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen);
+         res = (count == (n-blen));
+         s->img_buffer = s->img_buffer_end;
+         return res;
+      }
+   }
+
+   if (s->img_buffer+n <= s->img_buffer_end) {
+      memcpy(buffer, s->img_buffer, n);
+      s->img_buffer += n;
+      return 1;
+   } else
+      return 0;
+}
+
+static int stbi__get16be(stbi__context *s)
+{
+   int z = stbi__get8(s);
+   return (z << 8) + stbi__get8(s);
+}
+
+static stbi__uint32 stbi__get32be(stbi__context *s)
+{
+   stbi__uint32 z = stbi__get16be(s);
+   return (z << 16) + stbi__get16be(s);
+}
+
+#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF)
+// nothing
+#else
+static int stbi__get16le(stbi__context *s)
+{
+   int z = stbi__get8(s);
+   return z + (stbi__get8(s) << 8);
+}
+#endif
+
+#ifndef STBI_NO_BMP
+static stbi__uint32 stbi__get32le(stbi__context *s)
+{
+   stbi__uint32 z = stbi__get16le(s);
+   return z + (stbi__get16le(s) << 16);
+}
+#endif
+
+#define STBI__BYTECAST(x)  ((stbi_uc) ((x) & 255))  // truncate int to byte without warnings
+
+
+//////////////////////////////////////////////////////////////////////////////
+//
+//  generic converter from built-in img_n to req_comp
+//    individual types do this automatically as much as possible (e.g. jpeg
+//    does all cases internally since it needs to colorspace convert anyway,
+//    and it never has alpha, so very few cases ). png can automatically
+//    interleave an alpha=255 channel, but falls back to this for other cases
+//
+//  assume data buffer is malloced, so malloc a new one and free that one
+//  only failure mode is malloc failing
+
+static stbi_uc stbi__compute_y(int r, int g, int b)
+{
+   return (stbi_uc) (((r*77) + (g*150) +  (29*b)) >> 8);
+}
+
+static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y)
+{
+   int i,j;
+   unsigned char *good;
+
+   if (req_comp == img_n) return data;
+   STBI_ASSERT(req_comp >= 1 && req_comp <= 4);
+
+   good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0);
+   if (good == NULL) {
+      STBI_FREE(data);
+      return stbi__errpuc("outofmem", "Out of memory");
+   }
+
+   for (j=0; j < (int) y; ++j) {
+      unsigned char *src  = data + j * x * img_n   ;
+      unsigned char *dest = good + j * x * req_comp;
+
+      #define STBI__COMBO(a,b)  ((a)*8+(b))
+      #define STBI__CASE(a,b)   case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b)
+      // convert source image with img_n components to one with req_comp components;
+      // avoid switch per pixel, so use switch per scanline and massive macros
+      switch (STBI__COMBO(img_n, req_comp)) {
+         STBI__CASE(1,2) { dest[0]=src[0], dest[1]=255;                                     } break;
+         STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0];                                  } break;
+         STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=255;                     } break;
+         STBI__CASE(2,1) { dest[0]=src[0];                                                  } break;
+         STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0];                                  } break;
+         STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1];                  } break;
+         STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255;        } break;
+         STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]);                   } break;
+         STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255;    } break;
+         STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]);                   } break;
+         STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; } break;
+         STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2];                    } break;
+         default: STBI_ASSERT(0);
+      }
+      #undef STBI__CASE
+   }
+
+   STBI_FREE(data);
+   return good;
+}
+
+static stbi__uint16 stbi__compute_y_16(int r, int g, int b)
+{
+   return (stbi__uint16) (((r*77) + (g*150) +  (29*b)) >> 8);
+}
+
+static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y)
+{
+   int i,j;
+   stbi__uint16 *good;
+
+   if (req_comp == img_n) return data;
+   STBI_ASSERT(req_comp >= 1 && req_comp <= 4);
+
+   good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2);
+   if (good == NULL) {
+      STBI_FREE(data);
+      return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory");
+   }
+
+   for (j=0; j < (int) y; ++j) {
+      stbi__uint16 *src  = data + j * x * img_n   ;
+      stbi__uint16 *dest = good + j * x * req_comp;
+
+      #define STBI__COMBO(a,b)  ((a)*8+(b))
+      #define STBI__CASE(a,b)   case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b)
+      // convert source image with img_n components to one with req_comp components;
+      // avoid switch per pixel, so use switch per scanline and massive macros
+      switch (STBI__COMBO(img_n, req_comp)) {
+         STBI__CASE(1,2) { dest[0]=src[0], dest[1]=0xffff;                                     } break;
+         STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0];                                     } break;
+         STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=0xffff;                     } break;
+         STBI__CASE(2,1) { dest[0]=src[0];                                                     } break;
+         STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0];                                     } break;
+         STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1];                     } break;
+         STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=0xffff;        } break;
+         STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]);                   } break;
+         STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = 0xffff; } break;
+         STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]);                   } break;
+         STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = src[3]; } break;
+         STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2];                       } break;
+         default: STBI_ASSERT(0);
+      }
+      #undef STBI__CASE
+   }
+
+   STBI_FREE(data);
+   return good;
+}
+
+#ifndef STBI_NO_LINEAR
+static float   *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp)
+{
+   int i,k,n;
+   float *output;
+   if (!data) return NULL;
+   output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0);
+   if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); }
+   // compute number of non-alpha components
+   if (comp & 1) n = comp; else n = comp-1;
+   for (i=0; i < x*y; ++i) {
+      for (k=0; k < n; ++k) {
+         output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale);
+      }
+      if (k < comp) output[i*comp + k] = data[i*comp+k]/255.0f;
+   }
+   STBI_FREE(data);
+   return output;
+}
+#endif
+
+#ifndef STBI_NO_HDR
+#define stbi__float2int(x)   ((int) (x))
+static stbi_uc *stbi__hdr_to_ldr(float   *data, int x, int y, int comp)
+{
+   int i,k,n;
+   stbi_uc *output;
+   if (!data) return NULL;
+   output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0);
+   if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); }
+   // compute number of non-alpha components
+   if (comp & 1) n = comp; else n = comp-1;
+   for (i=0; i < x*y; ++i) {
+      for (k=0; k < n; ++k) {
+         float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f;
+         if (z < 0) z = 0;
+         if (z > 255) z = 255;
+         output[i*comp + k] = (stbi_uc) stbi__float2int(z);
+      }
+      if (k < comp) {
+         float z = data[i*comp+k] * 255 + 0.5f;
+         if (z < 0) z = 0;
+         if (z > 255) z = 255;
+         output[i*comp + k] = (stbi_uc) stbi__float2int(z);
+      }
+   }
+   STBI_FREE(data);
+   return output;
+}
+#endif
+
+//////////////////////////////////////////////////////////////////////////////
+//
+//  "baseline" JPEG/JFIF decoder
+//
+//    simple implementation
+//      - doesn't support delayed output of y-dimension
+//      - simple interface (only one output format: 8-bit interleaved RGB)
+//      - doesn't try to recover corrupt jpegs
+//      - doesn't allow partial loading, loading multiple at once
+//      - still fast on x86 (copying globals into locals doesn't help x86)
+//      - allocates lots of intermediate memory (full size of all components)
+//        - non-interleaved case requires this anyway
+//        - allows good upsampling (see next)
+//    high-quality
+//      - upsampled channels are bilinearly interpolated, even across blocks
+//      - quality integer IDCT derived from IJG's 'slow'
+//    performance
+//      - fast huffman; reasonable integer IDCT
+//      - some SIMD kernels for common paths on targets with SSE2/NEON
+//      - uses a lot of intermediate memory, could cache poorly
+
+#ifndef STBI_NO_JPEG
+
+// huffman decoding acceleration
+#define FAST_BITS   9  // larger handles more cases; smaller stomps less cache
+
+typedef struct
+{
+   stbi_uc  fast[1 << FAST_BITS];
+   // weirdly, repacking this into AoS is a 10% speed loss, instead of a win
+   stbi__uint16 code[256];
+   stbi_uc  values[256];
+   stbi_uc  size[257];
+   unsigned int maxcode[18];
+   int    delta[17];   // old 'firstsymbol' - old 'firstcode'
+} stbi__huffman;
+
+typedef struct
+{
+   stbi__context *s;
+   stbi__huffman huff_dc[4];
+   stbi__huffman huff_ac[4];
+   stbi__uint16 dequant[4][64];
+   stbi__int16 fast_ac[4][1 << FAST_BITS];
+
+// sizes for components, interleaved MCUs
+   int img_h_max, img_v_max;
+   int img_mcu_x, img_mcu_y;
+   int img_mcu_w, img_mcu_h;
+
+// definition of jpeg image component
+   struct
+   {
+      int id;
+      int h,v;
+      int tq;
+      int hd,ha;
+      int dc_pred;
+
+      int x,y,w2,h2;
+      stbi_uc *data;
+      void *raw_data, *raw_coeff;
+      stbi_uc *linebuf;
+      short   *coeff;   // progressive only
+      int      coeff_w, coeff_h; // number of 8x8 coefficient blocks
+   } img_comp[4];
+
+   stbi__uint32   code_buffer; // jpeg entropy-coded buffer
+   int            code_bits;   // number of valid bits
+   unsigned char  marker;      // marker seen while filling entropy buffer
+   int            nomore;      // flag if we saw a marker so must stop
+
+   int            progressive;
+   int            spec_start;
+   int            spec_end;
+   int            succ_high;
+   int            succ_low;
+   int            eob_run;
+   int            jfif;
+   int            app14_color_transform; // Adobe APP14 tag
+   int            rgb;
+
+   int scan_n, order[4];
+   int restart_interval, todo;
+
+// kernels
+   void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]);
+   void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step);
+   stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs);
+} stbi__jpeg;
+
+static int stbi__build_huffman(stbi__huffman *h, int *count)
+{
+   int i,j,k=0;
+   unsigned int code;
+   // build size list for each symbol (from JPEG spec)
+   for (i=0; i < 16; ++i)
+      for (j=0; j < count[i]; ++j)
+         h->size[k++] = (stbi_uc) (i+1);
+   h->size[k] = 0;
+
+   // compute actual symbols (from jpeg spec)
+   code = 0;
+   k = 0;
+   for(j=1; j <= 16; ++j) {
+      // compute delta to add to code to compute symbol id
+      h->delta[j] = k - code;
+      if (h->size[k] == j) {
+         while (h->size[k] == j)
+            h->code[k++] = (stbi__uint16) (code++);
+         if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG");
+      }
+      // compute largest code + 1 for this size, preshifted as needed later
+      h->maxcode[j] = code << (16-j);
+      code <<= 1;
+   }
+   h->maxcode[j] = 0xffffffff;
+
+   // build non-spec acceleration table; 255 is flag for not-accelerated
+   memset(h->fast, 255, 1 << FAST_BITS);
+   for (i=0; i < k; ++i) {
+      int s = h->size[i];
+      if (s <= FAST_BITS) {
+         int c = h->code[i] << (FAST_BITS-s);
+         int m = 1 << (FAST_BITS-s);
+         for (j=0; j < m; ++j) {
+            h->fast[c+j] = (stbi_uc) i;
+         }
+      }
+   }
+   return 1;
+}
+
+// build a table that decodes both magnitude and value of small ACs in
+// one go.
+static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h)
+{
+   int i;
+   for (i=0; i < (1 << FAST_BITS); ++i) {
+      stbi_uc fast = h->fast[i];
+      fast_ac[i] = 0;
+      if (fast < 255) {
+         int rs = h->values[fast];
+         int run = (rs >> 4) & 15;
+         int magbits = rs & 15;
+         int len = h->size[fast];
+
+         if (magbits && len + magbits <= FAST_BITS) {
+            // magnitude code followed by receive_extend code
+            int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits);
+            int m = 1 << (magbits - 1);
+            if (k < m) k += (~0U << magbits) + 1;
+            // if the result is small enough, we can fit it in fast_ac table
+            if (k >= -128 && k <= 127)
+               fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits));
+         }
+      }
+   }
+}
+
+static void stbi__grow_buffer_unsafe(stbi__jpeg *j)
+{
+   do {
+      unsigned int b = j->nomore ? 0 : stbi__get8(j->s);
+      if (b == 0xff) {
+         int c = stbi__get8(j->s);
+         while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes
+         if (c != 0) {
+            j->marker = (unsigned char) c;
+            j->nomore = 1;
+            return;
+         }
+      }
+      j->code_buffer |= b << (24 - j->code_bits);
+      j->code_bits += 8;
+   } while (j->code_bits <= 24);
+}
+
+// (1 << n) - 1
+static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535};
+
+// decode a jpeg huffman value from the bitstream
+stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h)
+{
+   unsigned int temp;
+   int c,k;
+
+   if (j->code_bits < 16) stbi__grow_buffer_unsafe(j);
+
+   // look at the top FAST_BITS and determine what symbol ID it is,
+   // if the code is <= FAST_BITS
+   c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1);
+   k = h->fast[c];
+   if (k < 255) {
+      int s = h->size[k];
+      if (s > j->code_bits)
+         return -1;
+      j->code_buffer <<= s;
+      j->code_bits -= s;
+      return h->values[k];
+   }
+
+   // naive test is to shift the code_buffer down so k bits are
+   // valid, then test against maxcode. To speed this up, we've
+   // preshifted maxcode left so that it has (16-k) 0s at the
+   // end; in other words, regardless of the number of bits, it
+   // wants to be compared against something shifted to have 16;
+   // that way we don't need to shift inside the loop.
+   temp = j->code_buffer >> 16;
+   for (k=FAST_BITS+1 ; ; ++k)
+      if (temp < h->maxcode[k])
+         break;
+   if (k == 17) {
+      // error! code not found
+      j->code_bits -= 16;
+      return -1;
+   }
+
+   if (k > j->code_bits)
+      return -1;
+
+   // convert the huffman code to the symbol id
+   c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k];
+   STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]);
+
+   // convert the id to a symbol
+   j->code_bits -= k;
+   j->code_buffer <<= k;
+   return h->values[c];
+}
+
+// bias[n] = (-1<<n) + 1
+static const int stbi__jbias[16] = {0,-1,-3,-7,-15,-31,-63,-127,-255,-511,-1023,-2047,-4095,-8191,-16383,-32767};
+
+// combined JPEG 'receive' and JPEG 'extend', since baseline
+// always extends everything it receives.
+stbi_inline static int stbi__extend_receive(stbi__jpeg *j, int n)
+{
+   unsigned int k;
+   int sgn;
+   if (j->code_bits < n) stbi__grow_buffer_unsafe(j);
+
+   sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB
+   k = stbi_lrot(j->code_buffer, n);
+   STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask)));
+   j->code_buffer = k & ~stbi__bmask[n];
+   k &= stbi__bmask[n];
+   j->code_bits -= n;
+   return k + (stbi__jbias[n] & ~sgn);
+}
+
+// get some unsigned bits
+stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n)
+{
+   unsigned int k;
+   if (j->code_bits < n) stbi__grow_buffer_unsafe(j);
+   k = stbi_lrot(j->code_buffer, n);
+   j->code_buffer = k & ~stbi__bmask[n];
+   k &= stbi__bmask[n];
+   j->code_bits -= n;
+   return k;
+}
+
+stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j)
+{
+   unsigned int k;
+   if (j->code_bits < 1) stbi__grow_buffer_unsafe(j);
+   k = j->code_buffer;
+   j->code_buffer <<= 1;
+   --j->code_bits;
+   return k & 0x80000000;
+}
+
+// given a value that's at position X in the zigzag stream,
+// where does it appear in the 8x8 matrix coded as row-major?
+static const stbi_uc stbi__jpeg_dezigzag[64+15] =
+{
+    0,  1,  8, 16,  9,  2,  3, 10,
+   17, 24, 32, 25, 18, 11,  4,  5,
+   12, 19, 26, 33, 40, 48, 41, 34,
+   27, 20, 13,  6,  7, 14, 21, 28,
+   35, 42, 49, 56, 57, 50, 43, 36,
+   29, 22, 15, 23, 30, 37, 44, 51,
+   58, 59, 52, 45, 38, 31, 39, 46,
+   53, 60, 61, 54, 47, 55, 62, 63,
+   // let corrupt input sample past end
+   63, 63, 63, 63, 63, 63, 63, 63,
+   63, 63, 63, 63, 63, 63, 63
+};
+
+// decode one 64-entry block--
+static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant)
+{
+   int diff,dc,k;
+   int t;
+
+   if (j->code_bits < 16) stbi__grow_buffer_unsafe(j);
+   t = stbi__jpeg_huff_decode(j, hdc);
+   if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG");
+
+   // 0 all the ac values now so we can do it 32-bits at a time
+   memset(data,0,64*sizeof(data[0]));
+
+   diff = t ? stbi__extend_receive(j, t) : 0;
+   dc = j->img_comp[b].dc_pred + diff;
+   j->img_comp[b].dc_pred = dc;
+   data[0] = (short) (dc * dequant[0]);
+
+   // decode AC components, see JPEG spec
+   k = 1;
+   do {
+      unsigned int zig;
+      int c,r,s;
+      if (j->code_bits < 16) stbi__grow_buffer_unsafe(j);
+      c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1);
+      r = fac[c];
+      if (r) { // fast-AC path
+         k += (r >> 4) & 15; // run
+         s = r & 15; // combined length
+         j->code_buffer <<= s;
+         j->code_bits -= s;
+         // decode into unzigzag'd location
+         zig = stbi__jpeg_dezigzag[k++];
+         data[zig] = (short) ((r >> 8) * dequant[zig]);
+      } else {
+         int rs = stbi__jpeg_huff_decode(j, hac);
+         if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG");
+         s = rs & 15;
+         r = rs >> 4;
+         if (s == 0) {
+            if (rs != 0xf0) break; // end block
+            k += 16;
+         } else {
+            k += r;
+            // decode into unzigzag'd location
+            zig = stbi__jpeg_dezigzag[k++];
+            data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]);
+         }
+      }
+   } while (k < 64);
+   return 1;
+}
+
+static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b)
+{
+   int diff,dc;
+   int t;
+   if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG");
+
+   if (j->code_bits < 16) stbi__grow_buffer_unsafe(j);
+
+   if (j->succ_high == 0) {
+      // first scan for DC coefficient, must be first
+      memset(data,0,64*sizeof(data[0])); // 0 all the ac values now
+      t = stbi__jpeg_huff_decode(j, hdc);
+      diff = t ? stbi__extend_receive(j, t) : 0;
+
+      dc = j->img_comp[b].dc_pred + diff;
+      j->img_comp[b].dc_pred = dc;
+      data[0] = (short) (dc << j->succ_low);
+   } else {
+      // refinement scan for DC coefficient
+      if (stbi__jpeg_get_bit(j))
+         data[0] += (short) (1 << j->succ_low);
+   }
+   return 1;
+}
+
+// @OPTIMIZE: store non-zigzagged during the decode passes,
+// and only de-zigzag when dequantizing
+static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac)
+{
+   int k;
+   if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG");
+
+   if (j->succ_high == 0) {
+      int shift = j->succ_low;
+
+      if (j->eob_run) {
+         --j->eob_run;
+         return 1;
+      }
+
+      k = j->spec_start;
+      do {
+         unsigned int zig;
+         int c,r,s;
+         if (j->code_bits < 16) stbi__grow_buffer_unsafe(j);
+         c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1);
+         r = fac[c];
+         if (r) { // fast-AC path
+            k += (r >> 4) & 15; // run
+            s = r & 15; // combined length
+            j->code_buffer <<= s;
+            j->code_bits -= s;
+            zig = stbi__jpeg_dezigzag[k++];
+            data[zig] = (short) ((r >> 8) << shift);
+         } else {
+            int rs = stbi__jpeg_huff_decode(j, hac);
+            if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG");
+            s = rs & 15;
+            r = rs >> 4;
+            if (s == 0) {
+               if (r < 15) {
+                  j->eob_run = (1 << r);
+                  if (r)
+                     j->eob_run += stbi__jpeg_get_bits(j, r);
+                  --j->eob_run;
+                  break;
+               }
+               k += 16;
+            } else {
+               k += r;
+               zig = stbi__jpeg_dezigzag[k++];
+               data[zig] = (short) (stbi__extend_receive(j,s) << shift);
+            }
+         }
+      } while (k <= j->spec_end);
+   } else {
+      // refinement scan for these AC coefficients
+
+      short bit = (short) (1 << j->succ_low);
+
+      if (j->eob_run) {
+         --j->eob_run;
+         for (k = j->spec_start; k <= j->spec_end; ++k) {
+            short *p = &data[stbi__jpeg_dezigzag[k]];
+            if (*p != 0)
+               if (stbi__jpeg_get_bit(j))
+                  if ((*p & bit)==0) {
+                     if (*p > 0)
+                        *p += bit;
+                     else
+                        *p -= bit;
+                  }
+         }
+      } else {
+         k = j->spec_start;
+         do {
+            int r,s;
+            int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh
+            if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG");
+            s = rs & 15;
+            r = rs >> 4;
+            if (s == 0) {
+               if (r < 15) {
+                  j->eob_run = (1 << r) - 1;
+                  if (r)
+                     j->eob_run += stbi__jpeg_get_bits(j, r);
+                  r = 64; // force end of block
+               } else {
+                  // r=15 s=0 should write 16 0s, so we just do
+                  // a run of 15 0s and then write s (which is 0),
+                  // so we don't have to do anything special here
+               }
+            } else {
+               if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG");
+               // sign bit
+               if (stbi__jpeg_get_bit(j))
+                  s = bit;
+               else
+                  s = -bit;
+            }
+
+            // advance by r
+            while (k <= j->spec_end) {
+               short *p = &data[stbi__jpeg_dezigzag[k++]];
+               if (*p != 0) {
+                  if (stbi__jpeg_get_bit(j))
+                     if ((*p & bit)==0) {
+                        if (*p > 0)
+                           *p += bit;
+                        else
+                           *p -= bit;
+                     }
+               } else {
+                  if (r == 0) {
+                     *p = (short) s;
+                     break;
+                  }
+                  --r;
+               }
+            }
+         } while (k <= j->spec_end);
+      }
+   }
+   return 1;
+}
+
+// take a -128..127 value and stbi__clamp it and convert to 0..255
+stbi_inline static stbi_uc stbi__clamp(int x)
+{
+   // trick to use a single test to catch both cases
+   if ((unsigned int) x > 255) {
+      if (x < 0) return 0;
+      if (x > 255) return 255;
+   }
+   return (stbi_uc) x;
+}
+
+#define stbi__f2f(x)  ((int) (((x) * 4096 + 0.5)))
+#define stbi__fsh(x)  ((x) * 4096)
+
+// derived from jidctint -- DCT_ISLOW
+#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \
+   int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \
+   p2 = s2;                                    \
+   p3 = s6;                                    \
+   p1 = (p2+p3) * stbi__f2f(0.5411961f);       \
+   t2 = p1 + p3*stbi__f2f(-1.847759065f);      \
+   t3 = p1 + p2*stbi__f2f( 0.765366865f);      \
+   p2 = s0;                                    \
+   p3 = s4;                                    \
+   t0 = stbi__fsh(p2+p3);                      \
+   t1 = stbi__fsh(p2-p3);                      \
+   x0 = t0+t3;                                 \
+   x3 = t0-t3;                                 \
+   x1 = t1+t2;                                 \
+   x2 = t1-t2;                                 \
+   t0 = s7;                                    \
+   t1 = s5;                                    \
+   t2 = s3;                                    \
+   t3 = s1;                                    \
+   p3 = t0+t2;                                 \
+   p4 = t1+t3;                                 \
+   p1 = t0+t3;                                 \
+   p2 = t1+t2;                                 \
+   p5 = (p3+p4)*stbi__f2f( 1.175875602f);      \
+   t0 = t0*stbi__f2f( 0.298631336f);           \
+   t1 = t1*stbi__f2f( 2.053119869f);           \
+   t2 = t2*stbi__f2f( 3.072711026f);           \
+   t3 = t3*stbi__f2f( 1.501321110f);           \
+   p1 = p5 + p1*stbi__f2f(-0.899976223f);      \
+   p2 = p5 + p2*stbi__f2f(-2.562915447f);      \
+   p3 = p3*stbi__f2f(-1.961570560f);           \
+   p4 = p4*stbi__f2f(-0.390180644f);           \
+   t3 += p1+p4;                                \
+   t2 += p2+p3;                                \
+   t1 += p2+p4;                                \
+   t0 += p1+p3;
+
+static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64])
+{
+   int i,val[64],*v=val;
+   stbi_uc *o;
+   short *d = data;
+
+   // columns
+   for (i=0; i < 8; ++i,++d, ++v) {
+      // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing
+      if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0
+           && d[40]==0 && d[48]==0 && d[56]==0) {
+         //    no shortcut                 0     seconds
+         //    (1|2|3|4|5|6|7)==0          0     seconds
+         //    all separate               -0.047 seconds
+         //    1 && 2|3 && 4|5 && 6|7:    -0.047 seconds
+         int dcterm = d[0]*4;
+         v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm;
+      } else {
+         STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56])
+         // constants scaled things up by 1<<12; let's bring them back
+         // down, but keep 2 extra bits of precision
+         x0 += 512; x1 += 512; x2 += 512; x3 += 512;
+         v[ 0] = (x0+t3) >> 10;
+         v[56] = (x0-t3) >> 10;
+         v[ 8] = (x1+t2) >> 10;
+         v[48] = (x1-t2) >> 10;
+         v[16] = (x2+t1) >> 10;
+         v[40] = (x2-t1) >> 10;
+         v[24] = (x3+t0) >> 10;
+         v[32] = (x3-t0) >> 10;
+      }
+   }
+
+   for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) {
+      // no fast case since the first 1D IDCT spread components out
+      STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7])
+      // constants scaled things up by 1<<12, plus we had 1<<2 from first
+      // loop, plus horizontal and vertical each scale by sqrt(8) so together
+      // we've got an extra 1<<3, so 1<<17 total we need to remove.
+      // so we want to round that, which means adding 0.5 * 1<<17,
+      // aka 65536. Also, we'll end up with -128 to 127 that we want
+      // to encode as 0..255 by adding 128, so we'll add that before the shift
+      x0 += 65536 + (128<<17);
+      x1 += 65536 + (128<<17);
+      x2 += 65536 + (128<<17);
+      x3 += 65536 + (128<<17);
+      // tried computing the shifts into temps, or'ing the temps to see
+      // if any were out of range, but that was slower
+      o[0] = stbi__clamp((x0+t3) >> 17);
+      o[7] = stbi__clamp((x0-t3) >> 17);
+      o[1] = stbi__clamp((x1+t2) >> 17);
+      o[6] = stbi__clamp((x1-t2) >> 17);
+      o[2] = stbi__clamp((x2+t1) >> 17);
+      o[5] = stbi__clamp((x2-t1) >> 17);
+      o[3] = stbi__clamp((x3+t0) >> 17);
+      o[4] = stbi__clamp((x3-t0) >> 17);
+   }
+}
+
+#ifdef STBI_SSE2
+// sse2 integer IDCT. not the fastest possible implementation but it
+// produces bit-identical results to the generic C version so it's
+// fully "transparent".
+static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64])
+{
+   // This is constructed to match our regular (generic) integer IDCT exactly.
+   __m128i row0, row1, row2, row3, row4, row5, row6, row7;
+   __m128i tmp;
+
+   // dot product constant: even elems=x, odd elems=y
+   #define dct_const(x,y)  _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y))
+
+   // out(0) = c0[even]*x + c0[odd]*y   (c0, x, y 16-bit, out 32-bit)
+   // out(1) = c1[even]*x + c1[odd]*y
+   #define dct_rot(out0,out1, x,y,c0,c1) \
+      __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \
+      __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \
+      __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \
+      __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \
+      __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \
+      __m128i out1##_h = _mm_madd_epi16(c0##hi, c1)
+
+   // out = in << 12  (in 16-bit, out 32-bit)
+   #define dct_widen(out, in) \
+      __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \
+      __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4)
+
+   // wide add
+   #define dct_wadd(out, a, b) \
+      __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \
+      __m128i out##_h = _mm_add_epi32(a##_h, b##_h)
+
+   // wide sub
+   #define dct_wsub(out, a, b) \
+      __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \
+      __m128i out##_h = _mm_sub_epi32(a##_h, b##_h)
+
+   // butterfly a/b, add bias, then shift by "s" and pack
+   #define dct_bfly32o(out0, out1, a,b,bias,s) \
+      { \
+         __m128i abiased_l = _mm_add_epi32(a##_l, bias); \
+         __m128i abiased_h = _mm_add_epi32(a##_h, bias); \
+         dct_wadd(sum, abiased, b); \
+         dct_wsub(dif, abiased, b); \
+         out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \
+         out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \
+      }
+
+   // 8-bit interleave step (for transposes)
+   #define dct_interleave8(a, b) \
+      tmp = a; \
+      a = _mm_unpacklo_epi8(a, b); \
+      b = _mm_unpackhi_epi8(tmp, b)
+
+   // 16-bit interleave step (for transposes)
+   #define dct_interleave16(a, b) \
+      tmp = a; \
+      a = _mm_unpacklo_epi16(a, b); \
+      b = _mm_unpackhi_epi16(tmp, b)
+
+   #define dct_pass(bias,shift) \
+      { \
+         /* even part */ \
+         dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \
+         __m128i sum04 = _mm_add_epi16(row0, row4); \
+         __m128i dif04 = _mm_sub_epi16(row0, row4); \
+         dct_widen(t0e, sum04); \
+         dct_widen(t1e, dif04); \
+         dct_wadd(x0, t0e, t3e); \
+         dct_wsub(x3, t0e, t3e); \
+         dct_wadd(x1, t1e, t2e); \
+         dct_wsub(x2, t1e, t2e); \
+         /* odd part */ \
+         dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \
+         dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \
+         __m128i sum17 = _mm_add_epi16(row1, row7); \
+         __m128i sum35 = _mm_add_epi16(row3, row5); \
+         dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \
+         dct_wadd(x4, y0o, y4o); \
+         dct_wadd(x5, y1o, y5o); \
+         dct_wadd(x6, y2o, y5o); \
+         dct_wadd(x7, y3o, y4o); \
+         dct_bfly32o(row0,row7, x0,x7,bias,shift); \
+         dct_bfly32o(row1,row6, x1,x6,bias,shift); \
+         dct_bfly32o(row2,row5, x2,x5,bias,shift); \
+         dct_bfly32o(row3,row4, x3,x4,bias,shift); \
+      }
+
+   __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f));
+   __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f));
+   __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f));
+   __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f));
+   __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f));
+   __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f));
+   __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f));
+   __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f));
+
+   // rounding biases in column/row passes, see stbi__idct_block for explanation.
+   __m128i bias_0 = _mm_set1_epi32(512);
+   __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17));
+
+   // load
+   row0 = _mm_load_si128((const __m128i *) (data + 0*8));
+   row1 = _mm_load_si128((const __m128i *) (data + 1*8));
+   row2 = _mm_load_si128((const __m128i *) (data + 2*8));
+   row3 = _mm_load_si128((const __m128i *) (data + 3*8));
+   row4 = _mm_load_si128((const __m128i *) (data + 4*8));
+   row5 = _mm_load_si128((const __m128i *) (data + 5*8));
+   row6 = _mm_load_si128((const __m128i *) (data + 6*8));
+   row7 = _mm_load_si128((const __m128i *) (data + 7*8));
+
+   // column pass
+   dct_pass(bias_0, 10);
+
+   {
+      // 16bit 8x8 transpose pass 1
+      dct_interleave16(row0, row4);
+      dct_interleave16(row1, row5);
+      dct_interleave16(row2, row6);
+      dct_interleave16(row3, row7);
+
+      // transpose pass 2
+      dct_interleave16(row0, row2);
+      dct_interleave16(row1, row3);
+      dct_interleave16(row4, row6);
+      dct_interleave16(row5, row7);
+
+      // transpose pass 3
+      dct_interleave16(row0, row1);
+      dct_interleave16(row2, row3);
+      dct_interleave16(row4, row5);
+      dct_interleave16(row6, row7);
+   }
+
+   // row pass
+   dct_pass(bias_1, 17);
+
+   {
+      // pack
+      __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7
+      __m128i p1 = _mm_packus_epi16(row2, row3);
+      __m128i p2 = _mm_packus_epi16(row4, row5);
+      __m128i p3 = _mm_packus_epi16(row6, row7);
+
+      // 8bit 8x8 transpose pass 1
+      dct_interleave8(p0, p2); // a0e0a1e1...
+      dct_interleave8(p1, p3); // c0g0c1g1...
+
+      // transpose pass 2
+      dct_interleave8(p0, p1); // a0c0e0g0...
+      dct_interleave8(p2, p3); // b0d0f0h0...
+
+      // transpose pass 3
+      dct_interleave8(p0, p2); // a0b0c0d0...
+      dct_interleave8(p1, p3); // a4b4c4d4...
+
+      // store
+      _mm_storel_epi64((__m128i *) out, p0); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, p2); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, p1); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, p3); out += out_stride;
+      _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e));
+   }
+
+#undef dct_const
+#undef dct_rot
+#undef dct_widen
+#undef dct_wadd
+#undef dct_wsub
+#undef dct_bfly32o
+#undef dct_interleave8
+#undef dct_interleave16
+#undef dct_pass
+}
+
+#endif // STBI_SSE2
+
+#ifdef STBI_NEON
+
+// NEON integer IDCT. should produce bit-identical
+// results to the generic C version.
+static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64])
+{
+   int16x8_t row0, row1, row2, row3, row4, row5, row6, row7;
+
+   int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f));
+   int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f));
+   int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f));
+   int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f));
+   int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f));
+   int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f));
+   int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f));
+   int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f));
+   int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f));
+   int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f));
+   int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f));
+   int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f));
+
+#define dct_long_mul(out, inq, coeff) \
+   int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \
+   int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff)
+
+#define dct_long_mac(out, acc, inq, coeff) \
+   int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \
+   int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff)
+
+#define dct_widen(out, inq) \
+   int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \
+   int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12)
+
+// wide add
+#define dct_wadd(out, a, b) \
+   int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \
+   int32x4_t out##_h = vaddq_s32(a##_h, b##_h)
+
+// wide sub
+#define dct_wsub(out, a, b) \
+   int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \
+   int32x4_t out##_h = vsubq_s32(a##_h, b##_h)
+
+// butterfly a/b, then shift using "shiftop" by "s" and pack
+#define dct_bfly32o(out0,out1, a,b,shiftop,s) \
+   { \
+      dct_wadd(sum, a, b); \
+      dct_wsub(dif, a, b); \
+      out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \
+      out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \
+   }
+
+#define dct_pass(shiftop, shift) \
+   { \
+      /* even part */ \
+      int16x8_t sum26 = vaddq_s16(row2, row6); \
+      dct_long_mul(p1e, sum26, rot0_0); \
+      dct_long_mac(t2e, p1e, row6, rot0_1); \
+      dct_long_mac(t3e, p1e, row2, rot0_2); \
+      int16x8_t sum04 = vaddq_s16(row0, row4); \
+      int16x8_t dif04 = vsubq_s16(row0, row4); \
+      dct_widen(t0e, sum04); \
+      dct_widen(t1e, dif04); \
+      dct_wadd(x0, t0e, t3e); \
+      dct_wsub(x3, t0e, t3e); \
+      dct_wadd(x1, t1e, t2e); \
+      dct_wsub(x2, t1e, t2e); \
+      /* odd part */ \
+      int16x8_t sum15 = vaddq_s16(row1, row5); \
+      int16x8_t sum17 = vaddq_s16(row1, row7); \
+      int16x8_t sum35 = vaddq_s16(row3, row5); \
+      int16x8_t sum37 = vaddq_s16(row3, row7); \
+      int16x8_t sumodd = vaddq_s16(sum17, sum35); \
+      dct_long_mul(p5o, sumodd, rot1_0); \
+      dct_long_mac(p1o, p5o, sum17, rot1_1); \
+      dct_long_mac(p2o, p5o, sum35, rot1_2); \
+      dct_long_mul(p3o, sum37, rot2_0); \
+      dct_long_mul(p4o, sum15, rot2_1); \
+      dct_wadd(sump13o, p1o, p3o); \
+      dct_wadd(sump24o, p2o, p4o); \
+      dct_wadd(sump23o, p2o, p3o); \
+      dct_wadd(sump14o, p1o, p4o); \
+      dct_long_mac(x4, sump13o, row7, rot3_0); \
+      dct_long_mac(x5, sump24o, row5, rot3_1); \
+      dct_long_mac(x6, sump23o, row3, rot3_2); \
+      dct_long_mac(x7, sump14o, row1, rot3_3); \
+      dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \
+      dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \
+      dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \
+      dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \
+   }
+
+   // load
+   row0 = vld1q_s16(data + 0*8);
+   row1 = vld1q_s16(data + 1*8);
+   row2 = vld1q_s16(data + 2*8);
+   row3 = vld1q_s16(data + 3*8);
+   row4 = vld1q_s16(data + 4*8);
+   row5 = vld1q_s16(data + 5*8);
+   row6 = vld1q_s16(data + 6*8);
+   row7 = vld1q_s16(data + 7*8);
+
+   // add DC bias
+   row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0));
+
+   // column pass
+   dct_pass(vrshrn_n_s32, 10);
+
+   // 16bit 8x8 transpose
+   {
+// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively.
+// whether compilers actually get this is another story, sadly.
+#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; }
+#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); }
+#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); }
+
+      // pass 1
+      dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6
+      dct_trn16(row2, row3);
+      dct_trn16(row4, row5);
+      dct_trn16(row6, row7);
+
+      // pass 2
+      dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4
+      dct_trn32(row1, row3);
+      dct_trn32(row4, row6);
+      dct_trn32(row5, row7);
+
+      // pass 3
+      dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0
+      dct_trn64(row1, row5);
+      dct_trn64(row2, row6);
+      dct_trn64(row3, row7);
+
+#undef dct_trn16
+#undef dct_trn32
+#undef dct_trn64
+   }
+
+   // row pass
+   // vrshrn_n_s32 only supports shifts up to 16, we need
+   // 17. so do a non-rounding shift of 16 first then follow
+   // up with a rounding shift by 1.
+   dct_pass(vshrn_n_s32, 16);
+
+   {
+      // pack and round
+      uint8x8_t p0 = vqrshrun_n_s16(row0, 1);
+      uint8x8_t p1 = vqrshrun_n_s16(row1, 1);
+      uint8x8_t p2 = vqrshrun_n_s16(row2, 1);
+      uint8x8_t p3 = vqrshrun_n_s16(row3, 1);
+      uint8x8_t p4 = vqrshrun_n_s16(row4, 1);
+      uint8x8_t p5 = vqrshrun_n_s16(row5, 1);
+      uint8x8_t p6 = vqrshrun_n_s16(row6, 1);
+      uint8x8_t p7 = vqrshrun_n_s16(row7, 1);
+
+      // again, these can translate into one instruction, but often don't.
+#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; }
+#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); }
+#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); }
+
+      // sadly can't use interleaved stores here since we only write
+      // 8 bytes to each scan line!
+
+      // 8x8 8-bit transpose pass 1
+      dct_trn8_8(p0, p1);
+      dct_trn8_8(p2, p3);
+      dct_trn8_8(p4, p5);
+      dct_trn8_8(p6, p7);
+
+      // pass 2
+      dct_trn8_16(p0, p2);
+      dct_trn8_16(p1, p3);
+      dct_trn8_16(p4, p6);
+      dct_trn8_16(p5, p7);
+
+      // pass 3
+      dct_trn8_32(p0, p4);
+      dct_trn8_32(p1, p5);
+      dct_trn8_32(p2, p6);
+      dct_trn8_32(p3, p7);
+
+      // store
+      vst1_u8(out, p0); out += out_stride;
+      vst1_u8(out, p1); out += out_stride;
+      vst1_u8(out, p2); out += out_stride;
+      vst1_u8(out, p3); out += out_stride;
+      vst1_u8(out, p4); out += out_stride;
+      vst1_u8(out, p5); out += out_stride;
+      vst1_u8(out, p6); out += out_stride;
+      vst1_u8(out, p7);
+
+#undef dct_trn8_8
+#undef dct_trn8_16
+#undef dct_trn8_32
+   }
+
+#undef dct_long_mul
+#undef dct_long_mac
+#undef dct_widen
+#undef dct_wadd
+#undef dct_wsub
+#undef dct_bfly32o
+#undef dct_pass
+}
+
+#endif // STBI_NEON
+
+#define STBI__MARKER_none  0xff
+// if there's a pending marker from the entropy stream, return that
+// otherwise, fetch from the stream and get a marker. if there's no
+// marker, return 0xff, which is never a valid marker value
+static stbi_uc stbi__get_marker(stbi__jpeg *j)
+{
+   stbi_uc x;
+   if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; }
+   x = stbi__get8(j->s);
+   if (x != 0xff) return STBI__MARKER_none;
+   while (x == 0xff)
+      x = stbi__get8(j->s); // consume repeated 0xff fill bytes
+   return x;
+}
+
+// in each scan, we'll have scan_n components, and the order
+// of the components is specified by order[]
+#define STBI__RESTART(x)     ((x) >= 0xd0 && (x) <= 0xd7)
+
+// after a restart interval, stbi__jpeg_reset the entropy decoder and
+// the dc prediction
+static void stbi__jpeg_reset(stbi__jpeg *j)
+{
+   j->code_bits = 0;
+   j->code_buffer = 0;
+   j->nomore = 0;
+   j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0;
+   j->marker = STBI__MARKER_none;
+   j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff;
+   j->eob_run = 0;
+   // no more than 1<<31 MCUs if no restart_interal? that's plenty safe,
+   // since we don't even allow 1<<30 pixels
+}
+
+static int stbi__parse_entropy_coded_data(stbi__jpeg *z)
+{
+   stbi__jpeg_reset(z);
+   if (!z->progressive) {
+      if (z->scan_n == 1) {
+         int i,j;
+         STBI_SIMD_ALIGN(short, data[64]);
+         int n = z->order[0];
+         // non-interleaved data, we just need to process one block at a time,
+         // in trivial scanline order
+         // number of blocks to do just depends on how many actual "pixels" this
+         // component has, independent of interleaved MCU blocking and such
+         int w = (z->img_comp[n].x+7) >> 3;
+         int h = (z->img_comp[n].y+7) >> 3;
+         for (j=0; j < h; ++j) {
+            for (i=0; i < w; ++i) {
+               int ha = z->img_comp[n].ha;
+               if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0;
+               z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data);
+               // every data block is an MCU, so countdown the restart interval
+               if (--z->todo <= 0) {
+                  if (z->code_bits < 24) stbi__grow_buffer_unsafe(z);
+                  // if it's NOT a restart, then just bail, so we get corrupt data
+                  // rather than no data
+                  if (!STBI__RESTART(z->marker)) return 1;
+                  stbi__jpeg_reset(z);
+               }
+            }
+         }
+         return 1;
+      } else { // interleaved
+         int i,j,k,x,y;
+         STBI_SIMD_ALIGN(short, data[64]);
+         for (j=0; j < z->img_mcu_y; ++j) {
+            for (i=0; i < z->img_mcu_x; ++i) {
+               // scan an interleaved mcu... process scan_n components in order
+               for (k=0; k < z->scan_n; ++k) {
+                  int n = z->order[k];
+                  // scan out an mcu's worth of this component; that's just determined
+                  // by the basic H and V specified for the component
+                  for (y=0; y < z->img_comp[n].v; ++y) {
+                     for (x=0; x < z->img_comp[n].h; ++x) {
+                        int x2 = (i*z->img_comp[n].h + x)*8;
+                        int y2 = (j*z->img_comp[n].v + y)*8;
+                        int ha = z->img_comp[n].ha;
+                        if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0;
+                        z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data);
+                     }
+                  }
+               }
+               // after all interleaved components, that's an interleaved MCU,
+               // so now count down the restart interval
+               if (--z->todo <= 0) {
+                  if (z->code_bits < 24) stbi__grow_buffer_unsafe(z);
+                  if (!STBI__RESTART(z->marker)) return 1;
+                  stbi__jpeg_reset(z);
+               }
+            }
+         }
+         return 1;
+      }
+   } else {
+      if (z->scan_n == 1) {
+         int i,j;
+         int n = z->order[0];
+         // non-interleaved data, we just need to process one block at a time,
+         // in trivial scanline order
+         // number of blocks to do just depends on how many actual "pixels" this
+         // component has, independent of interleaved MCU blocking and such
+         int w = (z->img_comp[n].x+7) >> 3;
+         int h = (z->img_comp[n].y+7) >> 3;
+         for (j=0; j < h; ++j) {
+            for (i=0; i < w; ++i) {
+               short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w);
+               if (z->spec_start == 0) {
+                  if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n))
+                     return 0;
+               } else {
+                  int ha = z->img_comp[n].ha;
+                  if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha]))
+                     return 0;
+               }
+               // every data block is an MCU, so countdown the restart interval
+               if (--z->todo <= 0) {
+                  if (z->code_bits < 24) stbi__grow_buffer_unsafe(z);
+                  if (!STBI__RESTART(z->marker)) return 1;
+                  stbi__jpeg_reset(z);
+               }
+            }
+         }
+         return 1;
+      } else { // interleaved
+         int i,j,k,x,y;
+         for (j=0; j < z->img_mcu_y; ++j) {
+            for (i=0; i < z->img_mcu_x; ++i) {
+               // scan an interleaved mcu... process scan_n components in order
+               for (k=0; k < z->scan_n; ++k) {
+                  int n = z->order[k];
+                  // scan out an mcu's worth of this component; that's just determined
+                  // by the basic H and V specified for the component
+                  for (y=0; y < z->img_comp[n].v; ++y) {
+                     for (x=0; x < z->img_comp[n].h; ++x) {
+                        int x2 = (i*z->img_comp[n].h + x);
+                        int y2 = (j*z->img_comp[n].v + y);
+                        short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w);
+                        if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n))
+                           return 0;
+                     }
+                  }
+               }
+               // after all interleaved components, that's an interleaved MCU,
+               // so now count down the restart interval
+               if (--z->todo <= 0) {
+                  if (z->code_bits < 24) stbi__grow_buffer_unsafe(z);
+                  if (!STBI__RESTART(z->marker)) return 1;
+                  stbi__jpeg_reset(z);
+               }
+            }
+         }
+         return 1;
+      }
+   }
+}
+
+static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant)
+{
+   int i;
+   for (i=0; i < 64; ++i)
+      data[i] *= dequant[i];
+}
+
+static void stbi__jpeg_finish(stbi__jpeg *z)
+{
+   if (z->progressive) {
+      // dequantize and idct the data
+      int i,j,n;
+      for (n=0; n < z->s->img_n; ++n) {
+         int w = (z->img_comp[n].x+7) >> 3;
+         int h = (z->img_comp[n].y+7) >> 3;
+         for (j=0; j < h; ++j) {
+            for (i=0; i < w; ++i) {
+               short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w);
+               stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]);
+               z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data);
+            }
+         }
+      }
+   }
+}
+
+static int stbi__process_marker(stbi__jpeg *z, int m)
+{
+   int L;
+   switch (m) {
+      case STBI__MARKER_none: // no marker found
+         return stbi__err("expected marker","Corrupt JPEG");
+
+      case 0xDD: // DRI - specify restart interval
+         if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG");
+         z->restart_interval = stbi__get16be(z->s);
+         return 1;
+
+      case 0xDB: // DQT - define quantization table
+         L = stbi__get16be(z->s)-2;
+         while (L > 0) {
+            int q = stbi__get8(z->s);
+            int p = q >> 4, sixteen = (p != 0);
+            int t = q & 15,i;
+            if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG");
+            if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG");
+
+            for (i=0; i < 64; ++i)
+               z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s));
+            L -= (sixteen ? 129 : 65);
+         }
+         return L==0;
+
+      case 0xC4: // DHT - define huffman table
+         L = stbi__get16be(z->s)-2;
+         while (L > 0) {
+            stbi_uc *v;
+            int sizes[16],i,n=0;
+            int q = stbi__get8(z->s);
+            int tc = q >> 4;
+            int th = q & 15;
+            if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG");
+            for (i=0; i < 16; ++i) {
+               sizes[i] = stbi__get8(z->s);
+               n += sizes[i];
+            }
+            L -= 17;
+            if (tc == 0) {
+               if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0;
+               v = z->huff_dc[th].values;
+            } else {
+               if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0;
+               v = z->huff_ac[th].values;
+            }
+            for (i=0; i < n; ++i)
+               v[i] = stbi__get8(z->s);
+            if (tc != 0)
+               stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th);
+            L -= n;
+         }
+         return L==0;
+   }
+
+   // check for comment block or APP blocks
+   if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) {
+      L = stbi__get16be(z->s);
+      if (L < 2) {
+         if (m == 0xFE)
+            return stbi__err("bad COM len","Corrupt JPEG");
+         else
+            return stbi__err("bad APP len","Corrupt JPEG");
+      }
+      L -= 2;
+
+      if (m == 0xE0 && L >= 5) { // JFIF APP0 segment
+         static const unsigned char tag[5] = {'J','F','I','F','\0'};
+         int ok = 1;
+         int i;
+         for (i=0; i < 5; ++i)
+            if (stbi__get8(z->s) != tag[i])
+               ok = 0;
+         L -= 5;
+         if (ok)
+            z->jfif = 1;
+      } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment
+         static const unsigned char tag[6] = {'A','d','o','b','e','\0'};
+         int ok = 1;
+         int i;
+         for (i=0; i < 6; ++i)
+            if (stbi__get8(z->s) != tag[i])
+               ok = 0;
+         L -= 6;
+         if (ok) {
+            stbi__get8(z->s); // version
+            stbi__get16be(z->s); // flags0
+            stbi__get16be(z->s); // flags1
+            z->app14_color_transform = stbi__get8(z->s); // color transform
+            L -= 6;
+         }
+      }
+
+      stbi__skip(z->s, L);
+      return 1;
+   }
+
+   return stbi__err("unknown marker","Corrupt JPEG");
+}
+
+// after we see SOS
+static int stbi__process_scan_header(stbi__jpeg *z)
+{
+   int i;
+   int Ls = stbi__get16be(z->s);
+   z->scan_n = stbi__get8(z->s);
+   if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG");
+   if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG");
+   for (i=0; i < z->scan_n; ++i) {
+      int id = stbi__get8(z->s), which;
+      int q = stbi__get8(z->s);
+      for (which = 0; which < z->s->img_n; ++which)
+         if (z->img_comp[which].id == id)
+            break;
+      if (which == z->s->img_n) return 0; // no match
+      z->img_comp[which].hd = q >> 4;   if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG");
+      z->img_comp[which].ha = q & 15;   if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG");
+      z->order[i] = which;
+   }
+
+   {
+      int aa;
+      z->spec_start = stbi__get8(z->s);
+      z->spec_end   = stbi__get8(z->s); // should be 63, but might be 0
+      aa = stbi__get8(z->s);
+      z->succ_high = (aa >> 4);
+      z->succ_low  = (aa & 15);
+      if (z->progressive) {
+         if (z->spec_start > 63 || z->spec_end > 63  || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13)
+            return stbi__err("bad SOS", "Corrupt JPEG");
+      } else {
+         if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG");
+         if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG");
+         z->spec_end = 63;
+      }
+   }
+
+   return 1;
+}
+
+static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why)
+{
+   int i;
+   for (i=0; i < ncomp; ++i) {
+      if (z->img_comp[i].raw_data) {
+         STBI_FREE(z->img_comp[i].raw_data);
+         z->img_comp[i].raw_data = NULL;
+         z->img_comp[i].data = NULL;
+      }
+      if (z->img_comp[i].raw_coeff) {
+         STBI_FREE(z->img_comp[i].raw_coeff);
+         z->img_comp[i].raw_coeff = 0;
+         z->img_comp[i].coeff = 0;
+      }
+      if (z->img_comp[i].linebuf) {
+         STBI_FREE(z->img_comp[i].linebuf);
+         z->img_comp[i].linebuf = NULL;
+      }
+   }
+   return why;
+}
+
+static int stbi__process_frame_header(stbi__jpeg *z, int scan)
+{
+   stbi__context *s = z->s;
+   int Lf,p,i,q, h_max=1,v_max=1,c;
+   Lf = stbi__get16be(s);         if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG
+   p  = stbi__get8(s);            if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline
+   s->img_y = stbi__get16be(s);   if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG
+   s->img_x = stbi__get16be(s);   if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires
+   c = stbi__get8(s);
+   if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG");
+   s->img_n = c;
+   for (i=0; i < c; ++i) {
+      z->img_comp[i].data = NULL;
+      z->img_comp[i].linebuf = NULL;
+   }
+
+   if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG");
+
+   z->rgb = 0;
+   for (i=0; i < s->img_n; ++i) {
+      static const unsigned char rgb[3] = { 'R', 'G', 'B' };
+      z->img_comp[i].id = stbi__get8(s);
+      if (s->img_n == 3 && z->img_comp[i].id == rgb[i])
+         ++z->rgb;
+      q = stbi__get8(s);
+      z->img_comp[i].h = (q >> 4);  if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG");
+      z->img_comp[i].v = q & 15;    if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG");
+      z->img_comp[i].tq = stbi__get8(s);  if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG");
+   }
+
+   if (scan != STBI__SCAN_load) return 1;
+
+   if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode");
+
+   for (i=0; i < s->img_n; ++i) {
+      if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h;
+      if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v;
+   }
+
+   // compute interleaved mcu info
+   z->img_h_max = h_max;
+   z->img_v_max = v_max;
+   z->img_mcu_w = h_max * 8;
+   z->img_mcu_h = v_max * 8;
+   // these sizes can't be more than 17 bits
+   z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w;
+   z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h;
+
+   for (i=0; i < s->img_n; ++i) {
+      // number of effective pixels (e.g. for non-interleaved MCU)
+      z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max;
+      z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max;
+      // to simplify generation, we'll allocate enough memory to decode
+      // the bogus oversized data from using interleaved MCUs and their
+      // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't
+      // discard the extra data until colorspace conversion
+      //
+      // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier)
+      // so these muls can't overflow with 32-bit ints (which we require)
+      z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8;
+      z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8;
+      z->img_comp[i].coeff = 0;
+      z->img_comp[i].raw_coeff = 0;
+      z->img_comp[i].linebuf = NULL;
+      z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15);
+      if (z->img_comp[i].raw_data == NULL)
+         return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory"));
+      // align blocks for idct using mmx/sse
+      z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15);
+      if (z->progressive) {
+         // w2, h2 are multiples of 8 (see above)
+         z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8;
+         z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8;
+         z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15);
+         if (z->img_comp[i].raw_coeff == NULL)
+            return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory"));
+         z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15);
+      }
+   }
+
+   return 1;
+}
+
+// use comparisons since in some cases we handle more than one case (e.g. SOF)
+#define stbi__DNL(x)         ((x) == 0xdc)
+#define stbi__SOI(x)         ((x) == 0xd8)
+#define stbi__EOI(x)         ((x) == 0xd9)
+#define stbi__SOF(x)         ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2)
+#define stbi__SOS(x)         ((x) == 0xda)
+
+#define stbi__SOF_progressive(x)   ((x) == 0xc2)
+
+static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan)
+{
+   int m;
+   z->jfif = 0;
+   z->app14_color_transform = -1; // valid values are 0,1,2
+   z->marker = STBI__MARKER_none; // initialize cached marker to empty
+   m = stbi__get_marker(z);
+   if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG");
+   if (scan == STBI__SCAN_type) return 1;
+   m = stbi__get_marker(z);
+   while (!stbi__SOF(m)) {
+      if (!stbi__process_marker(z,m)) return 0;
+      m = stbi__get_marker(z);
+      while (m == STBI__MARKER_none) {
+         // some files have extra padding after their blocks, so ok, we'll scan
+         if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG");
+         m = stbi__get_marker(z);
+      }
+   }
+   z->progressive = stbi__SOF_progressive(m);
+   if (!stbi__process_frame_header(z, scan)) return 0;
+   return 1;
+}
+
+// decode image to YCbCr format
+static int stbi__decode_jpeg_image(stbi__jpeg *j)
+{
+   int m;
+   for (m = 0; m < 4; m++) {
+      j->img_comp[m].raw_data = NULL;
+      j->img_comp[m].raw_coeff = NULL;
+   }
+   j->restart_interval = 0;
+   if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0;
+   m = stbi__get_marker(j);
+   while (!stbi__EOI(m)) {
+      if (stbi__SOS(m)) {
+         if (!stbi__process_scan_header(j)) return 0;
+         if (!stbi__parse_entropy_coded_data(j)) return 0;
+         if (j->marker == STBI__MARKER_none ) {
+            // handle 0s at the end of image data from IP Kamera 9060
+            while (!stbi__at_eof(j->s)) {
+               int x = stbi__get8(j->s);
+               if (x == 255) {
+                  j->marker = stbi__get8(j->s);
+                  break;
+               }
+            }
+            // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0
+         }
+      } else if (stbi__DNL(m)) {
+         int Ld = stbi__get16be(j->s);
+         stbi__uint32 NL = stbi__get16be(j->s);
+         if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG");
+         if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG");
+      } else {
+         if (!stbi__process_marker(j, m)) return 0;
+      }
+      m = stbi__get_marker(j);
+   }
+   if (j->progressive)
+      stbi__jpeg_finish(j);
+   return 1;
+}
+
+// static jfif-centered resampling (across block boundaries)
+
+typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1,
+                                    int w, int hs);
+
+#define stbi__div4(x) ((stbi_uc) ((x) >> 2))
+
+static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   STBI_NOTUSED(out);
+   STBI_NOTUSED(in_far);
+   STBI_NOTUSED(w);
+   STBI_NOTUSED(hs);
+   return in_near;
+}
+
+static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   // need to generate two samples vertically for every one in input
+   int i;
+   STBI_NOTUSED(hs);
+   for (i=0; i < w; ++i)
+      out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2);
+   return out;
+}
+
+static stbi_uc*  stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   // need to generate two samples horizontally for every one in input
+   int i;
+   stbi_uc *input = in_near;
+
+   if (w == 1) {
+      // if only one sample, can't do any interpolation
+      out[0] = out[1] = input[0];
+      return out;
+   }
+
+   out[0] = input[0];
+   out[1] = stbi__div4(input[0]*3 + input[1] + 2);
+   for (i=1; i < w-1; ++i) {
+      int n = 3*input[i]+2;
+      out[i*2+0] = stbi__div4(n+input[i-1]);
+      out[i*2+1] = stbi__div4(n+input[i+1]);
+   }
+   out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2);
+   out[i*2+1] = input[w-1];
+
+   STBI_NOTUSED(in_far);
+   STBI_NOTUSED(hs);
+
+   return out;
+}
+
+#define stbi__div16(x) ((stbi_uc) ((x) >> 4))
+
+static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   // need to generate 2x2 samples for every one in input
+   int i,t0,t1;
+   if (w == 1) {
+      out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2);
+      return out;
+   }
+
+   t1 = 3*in_near[0] + in_far[0];
+   out[0] = stbi__div4(t1+2);
+   for (i=1; i < w; ++i) {
+      t0 = t1;
+      t1 = 3*in_near[i]+in_far[i];
+      out[i*2-1] = stbi__div16(3*t0 + t1 + 8);
+      out[i*2  ] = stbi__div16(3*t1 + t0 + 8);
+   }
+   out[w*2-1] = stbi__div4(t1+2);
+
+   STBI_NOTUSED(hs);
+
+   return out;
+}
+
+#if defined(STBI_SSE2) || defined(STBI_NEON)
+static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   // need to generate 2x2 samples for every one in input
+   int i=0,t0,t1;
+
+   if (w == 1) {
+      out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2);
+      return out;
+   }
+
+   t1 = 3*in_near[0] + in_far[0];
+   // process groups of 8 pixels for as long as we can.
+   // note we can't handle the last pixel in a row in this loop
+   // because we need to handle the filter boundary conditions.
+   for (; i < ((w-1) & ~7); i += 8) {
+#if defined(STBI_SSE2)
+      // load and perform the vertical filtering pass
+      // this uses 3*x + y = 4*x + (y - x)
+      __m128i zero  = _mm_setzero_si128();
+      __m128i farb  = _mm_loadl_epi64((__m128i *) (in_far + i));
+      __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i));
+      __m128i farw  = _mm_unpacklo_epi8(farb, zero);
+      __m128i nearw = _mm_unpacklo_epi8(nearb, zero);
+      __m128i diff  = _mm_sub_epi16(farw, nearw);
+      __m128i nears = _mm_slli_epi16(nearw, 2);
+      __m128i curr  = _mm_add_epi16(nears, diff); // current row
+
+      // horizontal filter works the same based on shifted vers of current
+      // row. "prev" is current row shifted right by 1 pixel; we need to
+      // insert the previous pixel value (from t1).
+      // "next" is current row shifted left by 1 pixel, with first pixel
+      // of next block of 8 pixels added in.
+      __m128i prv0 = _mm_slli_si128(curr, 2);
+      __m128i nxt0 = _mm_srli_si128(curr, 2);
+      __m128i prev = _mm_insert_epi16(prv0, t1, 0);
+      __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7);
+
+      // horizontal filter, polyphase implementation since it's convenient:
+      // even pixels = 3*cur + prev = cur*4 + (prev - cur)
+      // odd  pixels = 3*cur + next = cur*4 + (next - cur)
+      // note the shared term.
+      __m128i bias  = _mm_set1_epi16(8);
+      __m128i curs = _mm_slli_epi16(curr, 2);
+      __m128i prvd = _mm_sub_epi16(prev, curr);
+      __m128i nxtd = _mm_sub_epi16(next, curr);
+      __m128i curb = _mm_add_epi16(curs, bias);
+      __m128i even = _mm_add_epi16(prvd, curb);
+      __m128i odd  = _mm_add_epi16(nxtd, curb);
+
+      // interleave even and odd pixels, then undo scaling.
+      __m128i int0 = _mm_unpacklo_epi16(even, odd);
+      __m128i int1 = _mm_unpackhi_epi16(even, odd);
+      __m128i de0  = _mm_srli_epi16(int0, 4);
+      __m128i de1  = _mm_srli_epi16(int1, 4);
+
+      // pack and write output
+      __m128i outv = _mm_packus_epi16(de0, de1);
+      _mm_storeu_si128((__m128i *) (out + i*2), outv);
+#elif defined(STBI_NEON)
+      // load and perform the vertical filtering pass
+      // this uses 3*x + y = 4*x + (y - x)
+      uint8x8_t farb  = vld1_u8(in_far + i);
+      uint8x8_t nearb = vld1_u8(in_near + i);
+      int16x8_t diff  = vreinterpretq_s16_u16(vsubl_u8(farb, nearb));
+      int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2));
+      int16x8_t curr  = vaddq_s16(nears, diff); // current row
+
+      // horizontal filter works the same based on shifted vers of current
+      // row. "prev" is current row shifted right by 1 pixel; we need to
+      // insert the previous pixel value (from t1).
+      // "next" is current row shifted left by 1 pixel, with first pixel
+      // of next block of 8 pixels added in.
+      int16x8_t prv0 = vextq_s16(curr, curr, 7);
+      int16x8_t nxt0 = vextq_s16(curr, curr, 1);
+      int16x8_t prev = vsetq_lane_s16(t1, prv0, 0);
+      int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7);
+
+      // horizontal filter, polyphase implementation since it's convenient:
+      // even pixels = 3*cur + prev = cur*4 + (prev - cur)
+      // odd  pixels = 3*cur + next = cur*4 + (next - cur)
+      // note the shared term.
+      int16x8_t curs = vshlq_n_s16(curr, 2);
+      int16x8_t prvd = vsubq_s16(prev, curr);
+      int16x8_t nxtd = vsubq_s16(next, curr);
+      int16x8_t even = vaddq_s16(curs, prvd);
+      int16x8_t odd  = vaddq_s16(curs, nxtd);
+
+      // undo scaling and round, then store with even/odd phases interleaved
+      uint8x8x2_t o;
+      o.val[0] = vqrshrun_n_s16(even, 4);
+      o.val[1] = vqrshrun_n_s16(odd,  4);
+      vst2_u8(out + i*2, o);
+#endif
+
+      // "previous" value for next iter
+      t1 = 3*in_near[i+7] + in_far[i+7];
+   }
+
+   t0 = t1;
+   t1 = 3*in_near[i] + in_far[i];
+   out[i*2] = stbi__div16(3*t1 + t0 + 8);
+
+   for (++i; i < w; ++i) {
+      t0 = t1;
+      t1 = 3*in_near[i]+in_far[i];
+      out[i*2-1] = stbi__div16(3*t0 + t1 + 8);
+      out[i*2  ] = stbi__div16(3*t1 + t0 + 8);
+   }
+   out[w*2-1] = stbi__div4(t1+2);
+
+   STBI_NOTUSED(hs);
+
+   return out;
+}
+#endif
+
+static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs)
+{
+   // resample with nearest-neighbor
+   int i,j;
+   STBI_NOTUSED(in_far);
+   for (i=0; i < w; ++i)
+      for (j=0; j < hs; ++j)
+         out[i*hs+j] = in_near[i];
+   return out;
+}
+
+// this is a reduced-precision calculation of YCbCr-to-RGB introduced
+// to make sure the code produces the same results in both SIMD and scalar
+#define stbi__float2fixed(x)  (((int) ((x) * 4096.0f + 0.5f)) << 8)
+static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step)
+{
+   int i;
+   for (i=0; i < count; ++i) {
+      int y_fixed = (y[i] << 20) + (1<<19); // rounding
+      int r,g,b;
+      int cr = pcr[i] - 128;
+      int cb = pcb[i] - 128;
+      r = y_fixed +  cr* stbi__float2fixed(1.40200f);
+      g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000);
+      b = y_fixed                                     +   cb* stbi__float2fixed(1.77200f);
+      r >>= 20;
+      g >>= 20;
+      b >>= 20;
+      if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; }
+      if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; }
+      if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; }
+      out[0] = (stbi_uc)r;
+      out[1] = (stbi_uc)g;
+      out[2] = (stbi_uc)b;
+      out[3] = 255;
+      out += step;
+   }
+}
+
+#if defined(STBI_SSE2) || defined(STBI_NEON)
+static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step)
+{
+   int i = 0;
+
+#ifdef STBI_SSE2
+   // step == 3 is pretty ugly on the final interleave, and i'm not convinced
+   // it's useful in practice (you wouldn't use it for textures, for example).
+   // so just accelerate step == 4 case.
+   if (step == 4) {
+      // this is a fairly straightforward implementation and not super-optimized.
+      __m128i signflip  = _mm_set1_epi8(-0x80);
+      __m128i cr_const0 = _mm_set1_epi16(   (short) ( 1.40200f*4096.0f+0.5f));
+      __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f));
+      __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f));
+      __m128i cb_const1 = _mm_set1_epi16(   (short) ( 1.77200f*4096.0f+0.5f));
+      __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128);
+      __m128i xw = _mm_set1_epi16(255); // alpha channel
+
+      for (; i+7 < count; i += 8) {
+         // load
+         __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i));
+         __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i));
+         __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i));
+         __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128
+         __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128
+
+         // unpack to short (and left-shift cr, cb by 8)
+         __m128i yw  = _mm_unpacklo_epi8(y_bias, y_bytes);
+         __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased);
+         __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased);
+
+         // color transform
+         __m128i yws = _mm_srli_epi16(yw, 4);
+         __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw);
+         __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw);
+         __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1);
+         __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1);
+         __m128i rws = _mm_add_epi16(cr0, yws);
+         __m128i gwt = _mm_add_epi16(cb0, yws);
+         __m128i bws = _mm_add_epi16(yws, cb1);
+         __m128i gws = _mm_add_epi16(gwt, cr1);
+
+         // descale
+         __m128i rw = _mm_srai_epi16(rws, 4);
+         __m128i bw = _mm_srai_epi16(bws, 4);
+         __m128i gw = _mm_srai_epi16(gws, 4);
+
+         // back to byte, set up for transpose
+         __m128i brb = _mm_packus_epi16(rw, bw);
+         __m128i gxb = _mm_packus_epi16(gw, xw);
+
+         // transpose to interleave channels
+         __m128i t0 = _mm_unpacklo_epi8(brb, gxb);
+         __m128i t1 = _mm_unpackhi_epi8(brb, gxb);
+         __m128i o0 = _mm_unpacklo_epi16(t0, t1);
+         __m128i o1 = _mm_unpackhi_epi16(t0, t1);
+
+         // store
+         _mm_storeu_si128((__m128i *) (out + 0), o0);
+         _mm_storeu_si128((__m128i *) (out + 16), o1);
+         out += 32;
+      }
+   }
+#endif
+
+#ifdef STBI_NEON
+   // in this version, step=3 support would be easy to add. but is there demand?
+   if (step == 4) {
+      // this is a fairly straightforward implementation and not super-optimized.
+      uint8x8_t signflip = vdup_n_u8(0x80);
+      int16x8_t cr_const0 = vdupq_n_s16(   (short) ( 1.40200f*4096.0f+0.5f));
+      int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f));
+      int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f));
+      int16x8_t cb_const1 = vdupq_n_s16(   (short) ( 1.77200f*4096.0f+0.5f));
+
+      for (; i+7 < count; i += 8) {
+         // load
+         uint8x8_t y_bytes  = vld1_u8(y + i);
+         uint8x8_t cr_bytes = vld1_u8(pcr + i);
+         uint8x8_t cb_bytes = vld1_u8(pcb + i);
+         int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip));
+         int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip));
+
+         // expand to s16
+         int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4));
+         int16x8_t crw = vshll_n_s8(cr_biased, 7);
+         int16x8_t cbw = vshll_n_s8(cb_biased, 7);
+
+         // color transform
+         int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0);
+         int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0);
+         int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1);
+         int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1);
+         int16x8_t rws = vaddq_s16(yws, cr0);
+         int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1);
+         int16x8_t bws = vaddq_s16(yws, cb1);
+
+         // undo scaling, round, convert to byte
+         uint8x8x4_t o;
+         o.val[0] = vqrshrun_n_s16(rws, 4);
+         o.val[1] = vqrshrun_n_s16(gws, 4);
+         o.val[2] = vqrshrun_n_s16(bws, 4);
+         o.val[3] = vdup_n_u8(255);
+
+         // store, interleaving r/g/b/a
+         vst4_u8(out, o);
+         out += 8*4;
+      }
+   }
+#endif
+
+   for (; i < count; ++i) {
+      int y_fixed = (y[i] << 20) + (1<<19); // rounding
+      int r,g,b;
+      int cr = pcr[i] - 128;
+      int cb = pcb[i] - 128;
+      r = y_fixed + cr* stbi__float2fixed(1.40200f);
+      g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000);
+      b = y_fixed                                   +   cb* stbi__float2fixed(1.77200f);
+      r >>= 20;
+      g >>= 20;
+      b >>= 20;
+      if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; }
+      if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; }
+      if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; }
+      out[0] = (stbi_uc)r;
+      out[1] = (stbi_uc)g;
+      out[2] = (stbi_uc)b;
+      out[3] = 255;
+      out += step;
+   }
+}
+#endif
+
+// set up the kernels
+static void stbi__setup_jpeg(stbi__jpeg *j)
+{
+   j->idct_block_kernel = stbi__idct_block;
+   j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row;
+   j->resample_row_hv_2_kernel = stbi__resample_row_hv_2;
+
+#ifdef STBI_SSE2
+   if (stbi__sse2_available()) {
+      j->idct_block_kernel = stbi__idct_simd;
+      j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd;
+      j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd;
+   }
+#endif
+
+#ifdef STBI_NEON
+   j->idct_block_kernel = stbi__idct_simd;
+   j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd;
+   j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd;
+#endif
+}
+
+// clean up the temporary component buffers
+static void stbi__cleanup_jpeg(stbi__jpeg *j)
+{
+   stbi__free_jpeg_components(j, j->s->img_n, 0);
+}
+
+typedef struct
+{
+   resample_row_func resample;
+   stbi_uc *line0,*line1;
+   int hs,vs;   // expansion factor in each axis
+   int w_lores; // horizontal pixels pre-expansion
+   int ystep;   // how far through vertical expansion we are
+   int ypos;    // which pre-expansion row we're on
+} stbi__resample;
+
+// fast 0..255 * 0..255 => 0..255 rounded multiplication
+static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y)
+{
+   unsigned int t = x*y + 128;
+   return (stbi_uc) ((t + (t >>8)) >> 8);
+}
+
+static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp)
+{
+   int n, decode_n, is_rgb;
+   z->s->img_n = 0; // make stbi__cleanup_jpeg safe
+
+   // validate req_comp
+   if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error");
+
+   // load a jpeg image from whichever source, but leave in YCbCr format
+   if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; }
+
+   // determine actual number of components to generate
+   n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1;
+
+   is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif));
+
+   if (z->s->img_n == 3 && n < 3 && !is_rgb)
+      decode_n = 1;
+   else
+      decode_n = z->s->img_n;
+
+   // resample and color-convert
+   {
+      int k;
+      unsigned int i,j;
+      stbi_uc *output;
+      stbi_uc *coutput[4];
+
+      stbi__resample res_comp[4];
+
+      for (k=0; k < decode_n; ++k) {
+         stbi__resample *r = &res_comp[k];
+
+         // allocate line buffer big enough for upsampling off the edges
+         // with upsample factor of 4
+         z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3);
+         if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); }
+
+         r->hs      = z->img_h_max / z->img_comp[k].h;
+         r->vs      = z->img_v_max / z->img_comp[k].v;
+         r->ystep   = r->vs >> 1;
+         r->w_lores = (z->s->img_x + r->hs-1) / r->hs;
+         r->ypos    = 0;
+         r->line0   = r->line1 = z->img_comp[k].data;
+
+         if      (r->hs == 1 && r->vs == 1) r->resample = resample_row_1;
+         else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2;
+         else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2;
+         else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel;
+         else                               r->resample = stbi__resample_row_generic;
+      }
+
+      // can't error after this so, this is safe
+      output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1);
+      if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); }
+
+      // now go ahead and resample
+      for (j=0; j < z->s->img_y; ++j) {
+         stbi_uc *out = output + n * z->s->img_x * j;
+         for (k=0; k < decode_n; ++k) {
+            stbi__resample *r = &res_comp[k];
+            int y_bot = r->ystep >= (r->vs >> 1);
+            coutput[k] = r->resample(z->img_comp[k].linebuf,
+                                     y_bot ? r->line1 : r->line0,
+                                     y_bot ? r->line0 : r->line1,
+                                     r->w_lores, r->hs);
+            if (++r->ystep >= r->vs) {
+               r->ystep = 0;
+               r->line0 = r->line1;
+               if (++r->ypos < z->img_comp[k].y)
+                  r->line1 += z->img_comp[k].w2;
+            }
+         }
+         if (n >= 3) {
+            stbi_uc *y = coutput[0];
+            if (z->s->img_n == 3) {
+               if (is_rgb) {
+                  for (i=0; i < z->s->img_x; ++i) {
+                     out[0] = y[i];
+                     out[1] = coutput[1][i];
+                     out[2] = coutput[2][i];
+                     out[3] = 255;
+                     out += n;
+                  }
+               } else {
+                  z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);
+               }
+            } else if (z->s->img_n == 4) {
+               if (z->app14_color_transform == 0) { // CMYK
+                  for (i=0; i < z->s->img_x; ++i) {
+                     stbi_uc m = coutput[3][i];
+                     out[0] = stbi__blinn_8x8(coutput[0][i], m);
+                     out[1] = stbi__blinn_8x8(coutput[1][i], m);
+                     out[2] = stbi__blinn_8x8(coutput[2][i], m);
+                     out[3] = 255;
+                     out += n;
+                  }
+               } else if (z->app14_color_transform == 2) { // YCCK
+                  z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);
+                  for (i=0; i < z->s->img_x; ++i) {
+                     stbi_uc m = coutput[3][i];
+                     out[0] = stbi__blinn_8x8(255 - out[0], m);
+                     out[1] = stbi__blinn_8x8(255 - out[1], m);
+                     out[2] = stbi__blinn_8x8(255 - out[2], m);
+                     out += n;
+                  }
+               } else { // YCbCr + alpha?  Ignore the fourth channel for now
+                  z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n);
+               }
+            } else
+               for (i=0; i < z->s->img_x; ++i) {
+                  out[0] = out[1] = out[2] = y[i];
+                  out[3] = 255; // not used if n==3
+                  out += n;
+               }
+         } else {
+            if (is_rgb) {
+               if (n == 1)
+                  for (i=0; i < z->s->img_x; ++i)
+                     *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]);
+               else {
+                  for (i=0; i < z->s->img_x; ++i, out += 2) {
+                     out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]);
+                     out[1] = 255;
+                  }
+               }
+            } else if (z->s->img_n == 4 && z->app14_color_transform == 0) {
+               for (i=0; i < z->s->img_x; ++i) {
+                  stbi_uc m = coutput[3][i];
+                  stbi_uc r = stbi__blinn_8x8(coutput[0][i], m);
+                  stbi_uc g = stbi__blinn_8x8(coutput[1][i], m);
+                  stbi_uc b = stbi__blinn_8x8(coutput[2][i], m);
+                  out[0] = stbi__compute_y(r, g, b);
+                  out[1] = 255;
+                  out += n;
+               }
+            } else if (z->s->img_n == 4 && z->app14_color_transform == 2) {
+               for (i=0; i < z->s->img_x; ++i) {
+                  out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]);
+                  out[1] = 255;
+                  out += n;
+               }
+            } else {
+               stbi_uc *y = coutput[0];
+               if (n == 1)
+                  for (i=0; i < z->s->img_x; ++i) out[i] = y[i];
+               else
+                  for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255;
+            }
+         }
+      }
+      stbi__cleanup_jpeg(z);
+      *out_x = z->s->img_x;
+      *out_y = z->s->img_y;
+      if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output
+      return output;
+   }
+}
+
+static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   unsigned char* result;
+   stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg));
+   STBI_NOTUSED(ri);
+   j->s = s;
+   stbi__setup_jpeg(j);
+   result = load_jpeg_image(j, x,y,comp,req_comp);
+   STBI_FREE(j);
+   return result;
+}
+
+static int stbi__jpeg_test(stbi__context *s)
+{
+   int r;
+   stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg));
+   j->s = s;
+   stbi__setup_jpeg(j);
+   r = stbi__decode_jpeg_header(j, STBI__SCAN_type);
+   stbi__rewind(s);
+   STBI_FREE(j);
+   return r;
+}
+
+static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp)
+{
+   if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) {
+      stbi__rewind( j->s );
+      return 0;
+   }
+   if (x) *x = j->s->img_x;
+   if (y) *y = j->s->img_y;
+   if (comp) *comp = j->s->img_n >= 3 ? 3 : 1;
+   return 1;
+}
+
+static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   int result;
+   stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg)));
+   j->s = s;
+   result = stbi__jpeg_info_raw(j, x, y, comp);
+   STBI_FREE(j);
+   return result;
+}
+#endif
+
+// public domain zlib decode    v0.2  Sean Barrett 2006-11-18
+//    simple implementation
+//      - all input must be provided in an upfront buffer
+//      - all output is written to a single output buffer (can malloc/realloc)
+//    performance
+//      - fast huffman
+
+#ifndef STBI_NO_ZLIB
+
+// fast-way is faster to check than jpeg huffman, but slow way is slower
+#define STBI__ZFAST_BITS  9 // accelerate all cases in default tables
+#define STBI__ZFAST_MASK  ((1 << STBI__ZFAST_BITS) - 1)
+
+// zlib-style huffman encoding
+// (jpegs packs from left, zlib from right, so can't share code)
+typedef struct
+{
+   stbi__uint16 fast[1 << STBI__ZFAST_BITS];
+   stbi__uint16 firstcode[16];
+   int maxcode[17];
+   stbi__uint16 firstsymbol[16];
+   stbi_uc  size[288];
+   stbi__uint16 value[288];
+} stbi__zhuffman;
+
+stbi_inline static int stbi__bitreverse16(int n)
+{
+  n = ((n & 0xAAAA) >>  1) | ((n & 0x5555) << 1);
+  n = ((n & 0xCCCC) >>  2) | ((n & 0x3333) << 2);
+  n = ((n & 0xF0F0) >>  4) | ((n & 0x0F0F) << 4);
+  n = ((n & 0xFF00) >>  8) | ((n & 0x00FF) << 8);
+  return n;
+}
+
+stbi_inline static int stbi__bit_reverse(int v, int bits)
+{
+   STBI_ASSERT(bits <= 16);
+   // to bit reverse n bits, reverse 16 and shift
+   // e.g. 11 bits, bit reverse and shift away 5
+   return stbi__bitreverse16(v) >> (16-bits);
+}
+
+static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num)
+{
+   int i,k=0;
+   int code, next_code[16], sizes[17];
+
+   // DEFLATE spec for generating codes
+   memset(sizes, 0, sizeof(sizes));
+   memset(z->fast, 0, sizeof(z->fast));
+   for (i=0; i < num; ++i)
+      ++sizes[sizelist[i]];
+   sizes[0] = 0;
+   for (i=1; i < 16; ++i)
+      if (sizes[i] > (1 << i))
+         return stbi__err("bad sizes", "Corrupt PNG");
+   code = 0;
+   for (i=1; i < 16; ++i) {
+      next_code[i] = code;
+      z->firstcode[i] = (stbi__uint16) code;
+      z->firstsymbol[i] = (stbi__uint16) k;
+      code = (code + sizes[i]);
+      if (sizes[i])
+         if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG");
+      z->maxcode[i] = code << (16-i); // preshift for inner loop
+      code <<= 1;
+      k += sizes[i];
+   }
+   z->maxcode[16] = 0x10000; // sentinel
+   for (i=0; i < num; ++i) {
+      int s = sizelist[i];
+      if (s) {
+         int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s];
+         stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i);
+         z->size [c] = (stbi_uc     ) s;
+         z->value[c] = (stbi__uint16) i;
+         if (s <= STBI__ZFAST_BITS) {
+            int j = stbi__bit_reverse(next_code[s],s);
+            while (j < (1 << STBI__ZFAST_BITS)) {
+               z->fast[j] = fastv;
+               j += (1 << s);
+            }
+         }
+         ++next_code[s];
+      }
+   }
+   return 1;
+}
+
+// zlib-from-memory implementation for PNG reading
+//    because PNG allows splitting the zlib stream arbitrarily,
+//    and it's annoying structurally to have PNG call ZLIB call PNG,
+//    we require PNG read all the IDATs and combine them into a single
+//    memory buffer
+
+typedef struct
+{
+   stbi_uc *zbuffer, *zbuffer_end;
+   int num_bits;
+   stbi__uint32 code_buffer;
+
+   char *zout;
+   char *zout_start;
+   char *zout_end;
+   int   z_expandable;
+
+   stbi__zhuffman z_length, z_distance;
+} stbi__zbuf;
+
+stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z)
+{
+   if (z->zbuffer >= z->zbuffer_end) return 0;
+   return *z->zbuffer++;
+}
+
+static void stbi__fill_bits(stbi__zbuf *z)
+{
+   do {
+      STBI_ASSERT(z->code_buffer < (1U << z->num_bits));
+      z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits;
+      z->num_bits += 8;
+   } while (z->num_bits <= 24);
+}
+
+stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n)
+{
+   unsigned int k;
+   if (z->num_bits < n) stbi__fill_bits(z);
+   k = z->code_buffer & ((1 << n) - 1);
+   z->code_buffer >>= n;
+   z->num_bits -= n;
+   return k;
+}
+
+static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z)
+{
+   int b,s,k;
+   // not resolved by fast table, so compute it the slow way
+   // use jpeg approach, which requires MSbits at top
+   k = stbi__bit_reverse(a->code_buffer, 16);
+   for (s=STBI__ZFAST_BITS+1; ; ++s)
+      if (k < z->maxcode[s])
+         break;
+   if (s == 16) return -1; // invalid code!
+   // code size is s, so:
+   b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s];
+   STBI_ASSERT(z->size[b] == s);
+   a->code_buffer >>= s;
+   a->num_bits -= s;
+   return z->value[b];
+}
+
+stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z)
+{
+   int b,s;
+   if (a->num_bits < 16) stbi__fill_bits(a);
+   b = z->fast[a->code_buffer & STBI__ZFAST_MASK];
+   if (b) {
+      s = b >> 9;
+      a->code_buffer >>= s;
+      a->num_bits -= s;
+      return b & 511;
+   }
+   return stbi__zhuffman_decode_slowpath(a, z);
+}
+
+static int stbi__zexpand(stbi__zbuf *z, char *zout, int n)  // need to make room for n bytes
+{
+   char *q;
+   int cur, limit, old_limit;
+   z->zout = zout;
+   if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG");
+   cur   = (int) (z->zout     - z->zout_start);
+   limit = old_limit = (int) (z->zout_end - z->zout_start);
+   while (cur + n > limit)
+      limit *= 2;
+   q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit);
+   STBI_NOTUSED(old_limit);
+   if (q == NULL) return stbi__err("outofmem", "Out of memory");
+   z->zout_start = q;
+   z->zout       = q + cur;
+   z->zout_end   = q + limit;
+   return 1;
+}
+
+static const int stbi__zlength_base[31] = {
+   3,4,5,6,7,8,9,10,11,13,
+   15,17,19,23,27,31,35,43,51,59,
+   67,83,99,115,131,163,195,227,258,0,0 };
+
+static const int stbi__zlength_extra[31]=
+{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 };
+
+static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,
+257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0};
+
+static const int stbi__zdist_extra[32] =
+{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13};
+
+static int stbi__parse_huffman_block(stbi__zbuf *a)
+{
+   char *zout = a->zout;
+   for(;;) {
+      int z = stbi__zhuffman_decode(a, &a->z_length);
+      if (z < 256) {
+         if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes
+         if (zout >= a->zout_end) {
+            if (!stbi__zexpand(a, zout, 1)) return 0;
+            zout = a->zout;
+         }
+         *zout++ = (char) z;
+      } else {
+         stbi_uc *p;
+         int len,dist;
+         if (z == 256) {
+            a->zout = zout;
+            return 1;
+         }
+         z -= 257;
+         len = stbi__zlength_base[z];
+         if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]);
+         z = stbi__zhuffman_decode(a, &a->z_distance);
+         if (z < 0) return stbi__err("bad huffman code","Corrupt PNG");
+         dist = stbi__zdist_base[z];
+         if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]);
+         if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG");
+         if (zout + len > a->zout_end) {
+            if (!stbi__zexpand(a, zout, len)) return 0;
+            zout = a->zout;
+         }
+         p = (stbi_uc *) (zout - dist);
+         if (dist == 1) { // run of one byte; common in images.
+            stbi_uc v = *p;
+            if (len) { do *zout++ = v; while (--len); }
+         } else {
+            if (len) { do *zout++ = *p++; while (--len); }
+         }
+      }
+   }
+}
+
+static int stbi__compute_huffman_codes(stbi__zbuf *a)
+{
+   static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 };
+   stbi__zhuffman z_codelength;
+   stbi_uc lencodes[286+32+137];//padding for maximum single op
+   stbi_uc codelength_sizes[19];
+   int i,n;
+
+   int hlit  = stbi__zreceive(a,5) + 257;
+   int hdist = stbi__zreceive(a,5) + 1;
+   int hclen = stbi__zreceive(a,4) + 4;
+   int ntot  = hlit + hdist;
+
+   memset(codelength_sizes, 0, sizeof(codelength_sizes));
+   for (i=0; i < hclen; ++i) {
+      int s = stbi__zreceive(a,3);
+      codelength_sizes[length_dezigzag[i]] = (stbi_uc) s;
+   }
+   if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0;
+
+   n = 0;
+   while (n < ntot) {
+      int c = stbi__zhuffman_decode(a, &z_codelength);
+      if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG");
+      if (c < 16)
+         lencodes[n++] = (stbi_uc) c;
+      else {
+         stbi_uc fill = 0;
+         if (c == 16) {
+            c = stbi__zreceive(a,2)+3;
+            if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG");
+            fill = lencodes[n-1];
+         } else if (c == 17)
+            c = stbi__zreceive(a,3)+3;
+         else {
+            STBI_ASSERT(c == 18);
+            c = stbi__zreceive(a,7)+11;
+         }
+         if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG");
+         memset(lencodes+n, fill, c);
+         n += c;
+      }
+   }
+   if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG");
+   if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0;
+   if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0;
+   return 1;
+}
+
+static int stbi__parse_uncompressed_block(stbi__zbuf *a)
+{
+   stbi_uc header[4];
+   int len,nlen,k;
+   if (a->num_bits & 7)
+      stbi__zreceive(a, a->num_bits & 7); // discard
+   // drain the bit-packed data into header
+   k = 0;
+   while (a->num_bits > 0) {
+      header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check
+      a->code_buffer >>= 8;
+      a->num_bits -= 8;
+   }
+   STBI_ASSERT(a->num_bits == 0);
+   // now fill header the normal way
+   while (k < 4)
+      header[k++] = stbi__zget8(a);
+   len  = header[1] * 256 + header[0];
+   nlen = header[3] * 256 + header[2];
+   if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG");
+   if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG");
+   if (a->zout + len > a->zout_end)
+      if (!stbi__zexpand(a, a->zout, len)) return 0;
+   memcpy(a->zout, a->zbuffer, len);
+   a->zbuffer += len;
+   a->zout += len;
+   return 1;
+}
+
+static int stbi__parse_zlib_header(stbi__zbuf *a)
+{
+   int cmf   = stbi__zget8(a);
+   int cm    = cmf & 15;
+   /* int cinfo = cmf >> 4; */
+   int flg   = stbi__zget8(a);
+   if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec
+   if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png
+   if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png
+   // window = 1 << (8 + cinfo)... but who cares, we fully buffer output
+   return 1;
+}
+
+static const stbi_uc stbi__zdefault_length[288] =
+{
+   8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
+   8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
+   8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
+   8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
+   8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,
+   9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,
+   9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,
+   9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,
+   7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8
+};
+static const stbi_uc stbi__zdefault_distance[32] =
+{
+   5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+};
+/*
+Init algorithm:
+{
+   int i;   // use <= to match clearly with spec
+   for (i=0; i <= 143; ++i)     stbi__zdefault_length[i]   = 8;
+   for (   ; i <= 255; ++i)     stbi__zdefault_length[i]   = 9;
+   for (   ; i <= 279; ++i)     stbi__zdefault_length[i]   = 7;
+   for (   ; i <= 287; ++i)     stbi__zdefault_length[i]   = 8;
+
+   for (i=0; i <=  31; ++i)     stbi__zdefault_distance[i] = 5;
+}
+*/
+
+static int stbi__parse_zlib(stbi__zbuf *a, int parse_header)
+{
+   int final, type;
+   if (parse_header)
+      if (!stbi__parse_zlib_header(a)) return 0;
+   a->num_bits = 0;
+   a->code_buffer = 0;
+   do {
+      final = stbi__zreceive(a,1);
+      type = stbi__zreceive(a,2);
+      if (type == 0) {
+         if (!stbi__parse_uncompressed_block(a)) return 0;
+      } else if (type == 3) {
+         return 0;
+      } else {
+         if (type == 1) {
+            // use fixed code lengths
+            if (!stbi__zbuild_huffman(&a->z_length  , stbi__zdefault_length  , 288)) return 0;
+            if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance,  32)) return 0;
+         } else {
+            if (!stbi__compute_huffman_codes(a)) return 0;
+         }
+         if (!stbi__parse_huffman_block(a)) return 0;
+      }
+   } while (!final);
+   return 1;
+}
+
+static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header)
+{
+   a->zout_start = obuf;
+   a->zout       = obuf;
+   a->zout_end   = obuf + olen;
+   a->z_expandable = exp;
+
+   return stbi__parse_zlib(a, parse_header);
+}
+
+STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen)
+{
+   stbi__zbuf a;
+   char *p = (char *) stbi__malloc(initial_size);
+   if (p == NULL) return NULL;
+   a.zbuffer = (stbi_uc *) buffer;
+   a.zbuffer_end = (stbi_uc *) buffer + len;
+   if (stbi__do_zlib(&a, p, initial_size, 1, 1)) {
+      if (outlen) *outlen = (int) (a.zout - a.zout_start);
+      return a.zout_start;
+   } else {
+      STBI_FREE(a.zout_start);
+      return NULL;
+   }
+}
+
+STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen)
+{
+   return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen);
+}
+
+STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header)
+{
+   stbi__zbuf a;
+   char *p = (char *) stbi__malloc(initial_size);
+   if (p == NULL) return NULL;
+   a.zbuffer = (stbi_uc *) buffer;
+   a.zbuffer_end = (stbi_uc *) buffer + len;
+   if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) {
+      if (outlen) *outlen = (int) (a.zout - a.zout_start);
+      return a.zout_start;
+   } else {
+      STBI_FREE(a.zout_start);
+      return NULL;
+   }
+}
+
+STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen)
+{
+   stbi__zbuf a;
+   a.zbuffer = (stbi_uc *) ibuffer;
+   a.zbuffer_end = (stbi_uc *) ibuffer + ilen;
+   if (stbi__do_zlib(&a, obuffer, olen, 0, 1))
+      return (int) (a.zout - a.zout_start);
+   else
+      return -1;
+}
+
+STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen)
+{
+   stbi__zbuf a;
+   char *p = (char *) stbi__malloc(16384);
+   if (p == NULL) return NULL;
+   a.zbuffer = (stbi_uc *) buffer;
+   a.zbuffer_end = (stbi_uc *) buffer+len;
+   if (stbi__do_zlib(&a, p, 16384, 1, 0)) {
+      if (outlen) *outlen = (int) (a.zout - a.zout_start);
+      return a.zout_start;
+   } else {
+      STBI_FREE(a.zout_start);
+      return NULL;
+   }
+}
+
+STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen)
+{
+   stbi__zbuf a;
+   a.zbuffer = (stbi_uc *) ibuffer;
+   a.zbuffer_end = (stbi_uc *) ibuffer + ilen;
+   if (stbi__do_zlib(&a, obuffer, olen, 0, 0))
+      return (int) (a.zout - a.zout_start);
+   else
+      return -1;
+}
+#endif
+
+// public domain "baseline" PNG decoder   v0.10  Sean Barrett 2006-11-18
+//    simple implementation
+//      - only 8-bit samples
+//      - no CRC checking
+//      - allocates lots of intermediate memory
+//        - avoids problem of streaming data between subsystems
+//        - avoids explicit window management
+//    performance
+//      - uses stb_zlib, a PD zlib implementation with fast huffman decoding
+
+#ifndef STBI_NO_PNG
+typedef struct
+{
+   stbi__uint32 length;
+   stbi__uint32 type;
+} stbi__pngchunk;
+
+static stbi__pngchunk stbi__get_chunk_header(stbi__context *s)
+{
+   stbi__pngchunk c;
+   c.length = stbi__get32be(s);
+   c.type   = stbi__get32be(s);
+   return c;
+}
+
+static int stbi__check_png_header(stbi__context *s)
+{
+   static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 };
+   int i;
+   for (i=0; i < 8; ++i)
+      if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG");
+   return 1;
+}
+
+typedef struct
+{
+   stbi__context *s;
+   stbi_uc *idata, *expanded, *out;
+   int depth;
+} stbi__png;
+
+
+enum {
+   STBI__F_none=0,
+   STBI__F_sub=1,
+   STBI__F_up=2,
+   STBI__F_avg=3,
+   STBI__F_paeth=4,
+   // synthetic filters used for first scanline to avoid needing a dummy row of 0s
+   STBI__F_avg_first,
+   STBI__F_paeth_first
+};
+
+static stbi_uc first_row_filter[5] =
+{
+   STBI__F_none,
+   STBI__F_sub,
+   STBI__F_none,
+   STBI__F_avg_first,
+   STBI__F_paeth_first
+};
+
+static int stbi__paeth(int a, int b, int c)
+{
+   int p = a + b - c;
+   int pa = abs(p-a);
+   int pb = abs(p-b);
+   int pc = abs(p-c);
+   if (pa <= pb && pa <= pc) return a;
+   if (pb <= pc) return b;
+   return c;
+}
+
+static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 };
+
+// create the png data from post-deflated data
+static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color)
+{
+   int bytes = (depth == 16? 2 : 1);
+   stbi__context *s = a->s;
+   stbi__uint32 i,j,stride = x*out_n*bytes;
+   stbi__uint32 img_len, img_width_bytes;
+   int k;
+   int img_n = s->img_n; // copy it into a local for later
+
+   int output_bytes = out_n*bytes;
+   int filter_bytes = img_n*bytes;
+   int width = x;
+
+   STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1);
+   a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into
+   if (!a->out) return stbi__err("outofmem", "Out of memory");
+
+   if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG");
+   img_width_bytes = (((img_n * x * depth) + 7) >> 3);
+   img_len = (img_width_bytes + 1) * y;
+
+   // we used to check for exact match between raw_len and img_len on non-interlaced PNGs,
+   // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros),
+   // so just check for raw_len < img_len always.
+   if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG");
+
+   for (j=0; j < y; ++j) {
+      stbi_uc *cur = a->out + stride*j;
+      stbi_uc *prior;
+      int filter = *raw++;
+
+      if (filter > 4)
+         return stbi__err("invalid filter","Corrupt PNG");
+
+      if (depth < 8) {
+         STBI_ASSERT(img_width_bytes <= x);
+         cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place
+         filter_bytes = 1;
+         width = img_width_bytes;
+      }
+      prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above
+
+      // if first row, use special filter that doesn't sample previous row
+      if (j == 0) filter = first_row_filter[filter];
+
+      // handle first byte explicitly
+      for (k=0; k < filter_bytes; ++k) {
+         switch (filter) {
+            case STBI__F_none       : cur[k] = raw[k]; break;
+            case STBI__F_sub        : cur[k] = raw[k]; break;
+            case STBI__F_up         : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break;
+            case STBI__F_avg        : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break;
+            case STBI__F_paeth      : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break;
+            case STBI__F_avg_first  : cur[k] = raw[k]; break;
+            case STBI__F_paeth_first: cur[k] = raw[k]; break;
+         }
+      }
+
+      if (depth == 8) {
+         if (img_n != out_n)
+            cur[img_n] = 255; // first pixel
+         raw += img_n;
+         cur += out_n;
+         prior += out_n;
+      } else if (depth == 16) {
+         if (img_n != out_n) {
+            cur[filter_bytes]   = 255; // first pixel top byte
+            cur[filter_bytes+1] = 255; // first pixel bottom byte
+         }
+         raw += filter_bytes;
+         cur += output_bytes;
+         prior += output_bytes;
+      } else {
+         raw += 1;
+         cur += 1;
+         prior += 1;
+      }
+
+      // this is a little gross, so that we don't switch per-pixel or per-component
+      if (depth < 8 || img_n == out_n) {
+         int nk = (width - 1)*filter_bytes;
+         #define STBI__CASE(f) \
+             case f:     \
+                for (k=0; k < nk; ++k)
+         switch (filter) {
+            // "none" filter turns into a memcpy here; make that explicit.
+            case STBI__F_none:         memcpy(cur, raw, nk); break;
+            STBI__CASE(STBI__F_sub)          { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break;
+            STBI__CASE(STBI__F_up)           { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break;
+            STBI__CASE(STBI__F_avg)          { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break;
+            STBI__CASE(STBI__F_paeth)        { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break;
+            STBI__CASE(STBI__F_avg_first)    { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break;
+            STBI__CASE(STBI__F_paeth_first)  { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break;
+         }
+         #undef STBI__CASE
+         raw += nk;
+      } else {
+         STBI_ASSERT(img_n+1 == out_n);
+         #define STBI__CASE(f) \
+             case f:     \
+                for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \
+                   for (k=0; k < filter_bytes; ++k)
+         switch (filter) {
+            STBI__CASE(STBI__F_none)         { cur[k] = raw[k]; } break;
+            STBI__CASE(STBI__F_sub)          { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break;
+            STBI__CASE(STBI__F_up)           { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break;
+            STBI__CASE(STBI__F_avg)          { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break;
+            STBI__CASE(STBI__F_paeth)        { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break;
+            STBI__CASE(STBI__F_avg_first)    { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break;
+            STBI__CASE(STBI__F_paeth_first)  { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break;
+         }
+         #undef STBI__CASE
+
+         // the loop above sets the high byte of the pixels' alpha, but for
+         // 16 bit png files we also need the low byte set. we'll do that here.
+         if (depth == 16) {
+            cur = a->out + stride*j; // start at the beginning of the row again
+            for (i=0; i < x; ++i,cur+=output_bytes) {
+               cur[filter_bytes+1] = 255;
+            }
+         }
+      }
+   }
+
+   // we make a separate pass to expand bits to pixels; for performance,
+   // this could run two scanlines behind the above code, so it won't
+   // intefere with filtering but will still be in the cache.
+   if (depth < 8) {
+      for (j=0; j < y; ++j) {
+         stbi_uc *cur = a->out + stride*j;
+         stbi_uc *in  = a->out + stride*j + x*out_n - img_width_bytes;
+         // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit
+         // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop
+         stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range
+
+         // note that the final byte might overshoot and write more data than desired.
+         // we can allocate enough data that this never writes out of memory, but it
+         // could also overwrite the next scanline. can it overwrite non-empty data
+         // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel.
+         // so we need to explicitly clamp the final ones
+
+         if (depth == 4) {
+            for (k=x*img_n; k >= 2; k-=2, ++in) {
+               *cur++ = scale * ((*in >> 4)       );
+               *cur++ = scale * ((*in     ) & 0x0f);
+            }
+            if (k > 0) *cur++ = scale * ((*in >> 4)       );
+         } else if (depth == 2) {
+            for (k=x*img_n; k >= 4; k-=4, ++in) {
+               *cur++ = scale * ((*in >> 6)       );
+               *cur++ = scale * ((*in >> 4) & 0x03);
+               *cur++ = scale * ((*in >> 2) & 0x03);
+               *cur++ = scale * ((*in     ) & 0x03);
+            }
+            if (k > 0) *cur++ = scale * ((*in >> 6)       );
+            if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03);
+            if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03);
+         } else if (depth == 1) {
+            for (k=x*img_n; k >= 8; k-=8, ++in) {
+               *cur++ = scale * ((*in >> 7)       );
+               *cur++ = scale * ((*in >> 6) & 0x01);
+               *cur++ = scale * ((*in >> 5) & 0x01);
+               *cur++ = scale * ((*in >> 4) & 0x01);
+               *cur++ = scale * ((*in >> 3) & 0x01);
+               *cur++ = scale * ((*in >> 2) & 0x01);
+               *cur++ = scale * ((*in >> 1) & 0x01);
+               *cur++ = scale * ((*in     ) & 0x01);
+            }
+            if (k > 0) *cur++ = scale * ((*in >> 7)       );
+            if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01);
+            if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01);
+            if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01);
+            if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01);
+            if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01);
+            if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01);
+         }
+         if (img_n != out_n) {
+            int q;
+            // insert alpha = 255
+            cur = a->out + stride*j;
+            if (img_n == 1) {
+               for (q=x-1; q >= 0; --q) {
+                  cur[q*2+1] = 255;
+                  cur[q*2+0] = cur[q];
+               }
+            } else {
+               STBI_ASSERT(img_n == 3);
+               for (q=x-1; q >= 0; --q) {
+                  cur[q*4+3] = 255;
+                  cur[q*4+2] = cur[q*3+2];
+                  cur[q*4+1] = cur[q*3+1];
+                  cur[q*4+0] = cur[q*3+0];
+               }
+            }
+         }
+      }
+   } else if (depth == 16) {
+      // force the image data from big-endian to platform-native.
+      // this is done in a separate pass due to the decoding relying
+      // on the data being untouched, but could probably be done
+      // per-line during decode if care is taken.
+      stbi_uc *cur = a->out;
+      stbi__uint16 *cur16 = (stbi__uint16*)cur;
+
+      for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) {
+         *cur16 = (cur[0] << 8) | cur[1];
+      }
+   }
+
+   return 1;
+}
+
+static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced)
+{
+   int bytes = (depth == 16 ? 2 : 1);
+   int out_bytes = out_n * bytes;
+   stbi_uc *final;
+   int p;
+   if (!interlaced)
+      return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color);
+
+   // de-interlacing
+   final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0);
+   for (p=0; p < 7; ++p) {
+      int xorig[] = { 0,4,0,2,0,1,0 };
+      int yorig[] = { 0,0,4,0,2,0,1 };
+      int xspc[]  = { 8,8,4,4,2,2,1 };
+      int yspc[]  = { 8,8,8,4,4,2,2 };
+      int i,j,x,y;
+      // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1
+      x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p];
+      y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p];
+      if (x && y) {
+         stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y;
+         if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) {
+            STBI_FREE(final);
+            return 0;
+         }
+         for (j=0; j < y; ++j) {
+            for (i=0; i < x; ++i) {
+               int out_y = j*yspc[p]+yorig[p];
+               int out_x = i*xspc[p]+xorig[p];
+               memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes,
+                      a->out + (j*x+i)*out_bytes, out_bytes);
+            }
+         }
+         STBI_FREE(a->out);
+         image_data += img_len;
+         image_data_len -= img_len;
+      }
+   }
+   a->out = final;
+
+   return 1;
+}
+
+static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n)
+{
+   stbi__context *s = z->s;
+   stbi__uint32 i, pixel_count = s->img_x * s->img_y;
+   stbi_uc *p = z->out;
+
+   // compute color-based transparency, assuming we've
+   // already got 255 as the alpha value in the output
+   STBI_ASSERT(out_n == 2 || out_n == 4);
+
+   if (out_n == 2) {
+      for (i=0; i < pixel_count; ++i) {
+         p[1] = (p[0] == tc[0] ? 0 : 255);
+         p += 2;
+      }
+   } else {
+      for (i=0; i < pixel_count; ++i) {
+         if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2])
+            p[3] = 0;
+         p += 4;
+      }
+   }
+   return 1;
+}
+
+static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n)
+{
+   stbi__context *s = z->s;
+   stbi__uint32 i, pixel_count = s->img_x * s->img_y;
+   stbi__uint16 *p = (stbi__uint16*) z->out;
+
+   // compute color-based transparency, assuming we've
+   // already got 65535 as the alpha value in the output
+   STBI_ASSERT(out_n == 2 || out_n == 4);
+
+   if (out_n == 2) {
+      for (i = 0; i < pixel_count; ++i) {
+         p[1] = (p[0] == tc[0] ? 0 : 65535);
+         p += 2;
+      }
+   } else {
+      for (i = 0; i < pixel_count; ++i) {
+         if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2])
+            p[3] = 0;
+         p += 4;
+      }
+   }
+   return 1;
+}
+
+static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n)
+{
+   stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y;
+   stbi_uc *p, *temp_out, *orig = a->out;
+
+   p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0);
+   if (p == NULL) return stbi__err("outofmem", "Out of memory");
+
+   // between here and free(out) below, exitting would leak
+   temp_out = p;
+
+   if (pal_img_n == 3) {
+      for (i=0; i < pixel_count; ++i) {
+         int n = orig[i]*4;
+         p[0] = palette[n  ];
+         p[1] = palette[n+1];
+         p[2] = palette[n+2];
+         p += 3;
+      }
+   } else {
+      for (i=0; i < pixel_count; ++i) {
+         int n = orig[i]*4;
+         p[0] = palette[n  ];
+         p[1] = palette[n+1];
+         p[2] = palette[n+2];
+         p[3] = palette[n+3];
+         p += 4;
+      }
+   }
+   STBI_FREE(a->out);
+   a->out = temp_out;
+
+   STBI_NOTUSED(len);
+
+   return 1;
+}
+
+static int stbi__unpremultiply_on_load = 0;
+static int stbi__de_iphone_flag = 0;
+
+STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply)
+{
+   stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply;
+}
+
+STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert)
+{
+   stbi__de_iphone_flag = flag_true_if_should_convert;
+}
+
+static void stbi__de_iphone(stbi__png *z)
+{
+   stbi__context *s = z->s;
+   stbi__uint32 i, pixel_count = s->img_x * s->img_y;
+   stbi_uc *p = z->out;
+
+   if (s->img_out_n == 3) {  // convert bgr to rgb
+      for (i=0; i < pixel_count; ++i) {
+         stbi_uc t = p[0];
+         p[0] = p[2];
+         p[2] = t;
+         p += 3;
+      }
+   } else {
+      STBI_ASSERT(s->img_out_n == 4);
+      if (stbi__unpremultiply_on_load) {
+         // convert bgr to rgb and unpremultiply
+         for (i=0; i < pixel_count; ++i) {
+            stbi_uc a = p[3];
+            stbi_uc t = p[0];
+            if (a) {
+               stbi_uc half = a / 2;
+               p[0] = (p[2] * 255 + half) / a;
+               p[1] = (p[1] * 255 + half) / a;
+               p[2] = ( t   * 255 + half) / a;
+            } else {
+               p[0] = p[2];
+               p[2] = t;
+            }
+            p += 4;
+         }
+      } else {
+         // convert bgr to rgb
+         for (i=0; i < pixel_count; ++i) {
+            stbi_uc t = p[0];
+            p[0] = p[2];
+            p[2] = t;
+            p += 4;
+         }
+      }
+   }
+}
+
+#define STBI__PNG_TYPE(a,b,c,d)  (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d))
+
+static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp)
+{
+   stbi_uc palette[1024], pal_img_n=0;
+   stbi_uc has_trans=0, tc[3];
+   stbi__uint16 tc16[3];
+   stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0;
+   int first=1,k,interlace=0, color=0, is_iphone=0;
+   stbi__context *s = z->s;
+
+   z->expanded = NULL;
+   z->idata = NULL;
+   z->out = NULL;
+
+   if (!stbi__check_png_header(s)) return 0;
+
+   if (scan == STBI__SCAN_type) return 1;
+
+   for (;;) {
+      stbi__pngchunk c = stbi__get_chunk_header(s);
+      switch (c.type) {
+         case STBI__PNG_TYPE('C','g','B','I'):
+            is_iphone = 1;
+            stbi__skip(s, c.length);
+            break;
+         case STBI__PNG_TYPE('I','H','D','R'): {
+            int comp,filter;
+            if (!first) return stbi__err("multiple IHDR","Corrupt PNG");
+            first = 0;
+            if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG");
+            s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)");
+            s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)");
+            z->depth = stbi__get8(s);  if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16)  return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only");
+            color = stbi__get8(s);  if (color > 6)         return stbi__err("bad ctype","Corrupt PNG");
+            if (color == 3 && z->depth == 16)                  return stbi__err("bad ctype","Corrupt PNG");
+            if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG");
+            comp  = stbi__get8(s);  if (comp) return stbi__err("bad comp method","Corrupt PNG");
+            filter= stbi__get8(s);  if (filter) return stbi__err("bad filter method","Corrupt PNG");
+            interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG");
+            if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG");
+            if (!pal_img_n) {
+               s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0);
+               if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode");
+               if (scan == STBI__SCAN_header) return 1;
+            } else {
+               // if paletted, then pal_n is our final components, and
+               // img_n is # components to decompress/filter.
+               s->img_n = 1;
+               if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG");
+               // if SCAN_header, have to scan to see if we have a tRNS
+            }
+            break;
+         }
+
+         case STBI__PNG_TYPE('P','L','T','E'):  {
+            if (first) return stbi__err("first not IHDR", "Corrupt PNG");
+            if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG");
+            pal_len = c.length / 3;
+            if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG");
+            for (i=0; i < pal_len; ++i) {
+               palette[i*4+0] = stbi__get8(s);
+               palette[i*4+1] = stbi__get8(s);
+               palette[i*4+2] = stbi__get8(s);
+               palette[i*4+3] = 255;
+            }
+            break;
+         }
+
+         case STBI__PNG_TYPE('t','R','N','S'): {
+            if (first) return stbi__err("first not IHDR", "Corrupt PNG");
+            if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG");
+            if (pal_img_n) {
+               if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; }
+               if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG");
+               if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG");
+               pal_img_n = 4;
+               for (i=0; i < c.length; ++i)
+                  palette[i*4+3] = stbi__get8(s);
+            } else {
+               if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG");
+               if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG");
+               has_trans = 1;
+               if (z->depth == 16) {
+                  for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is
+               } else {
+                  for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger
+               }
+            }
+            break;
+         }
+
+         case STBI__PNG_TYPE('I','D','A','T'): {
+            if (first) return stbi__err("first not IHDR", "Corrupt PNG");
+            if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG");
+            if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; }
+            if ((int)(ioff + c.length) < (int)ioff) return 0;
+            if (ioff + c.length > idata_limit) {
+               stbi__uint32 idata_limit_old = idata_limit;
+               stbi_uc *p;
+               if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096;
+               while (ioff + c.length > idata_limit)
+                  idata_limit *= 2;
+               STBI_NOTUSED(idata_limit_old);
+               p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory");
+               z->idata = p;
+            }
+            if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG");
+            ioff += c.length;
+            break;
+         }
+
+         case STBI__PNG_TYPE('I','E','N','D'): {
+            stbi__uint32 raw_len, bpl;
+            if (first) return stbi__err("first not IHDR", "Corrupt PNG");
+            if (scan != STBI__SCAN_load) return 1;
+            if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG");
+            // initial guess for decoded data size to avoid unnecessary reallocs
+            bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component
+            raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */;
+            z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone);
+            if (z->expanded == NULL) return 0; // zlib should set error
+            STBI_FREE(z->idata); z->idata = NULL;
+            if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans)
+               s->img_out_n = s->img_n+1;
+            else
+               s->img_out_n = s->img_n;
+            if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0;
+            if (has_trans) {
+               if (z->depth == 16) {
+                  if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0;
+               } else {
+                  if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0;
+               }
+            }
+            if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2)
+               stbi__de_iphone(z);
+            if (pal_img_n) {
+               // pal_img_n == 3 or 4
+               s->img_n = pal_img_n; // record the actual colors we had
+               s->img_out_n = pal_img_n;
+               if (req_comp >= 3) s->img_out_n = req_comp;
+               if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n))
+                  return 0;
+            } else if (has_trans) {
+               // non-paletted image with tRNS -> source image has (constant) alpha
+               ++s->img_n;
+            }
+            STBI_FREE(z->expanded); z->expanded = NULL;
+            return 1;
+         }
+
+         default:
+            // if critical, fail
+            if (first) return stbi__err("first not IHDR", "Corrupt PNG");
+            if ((c.type & (1 << 29)) == 0) {
+               #ifndef STBI_NO_FAILURE_STRINGS
+               // not threadsafe
+               static char invalid_chunk[] = "XXXX PNG chunk not known";
+               invalid_chunk[0] = STBI__BYTECAST(c.type >> 24);
+               invalid_chunk[1] = STBI__BYTECAST(c.type >> 16);
+               invalid_chunk[2] = STBI__BYTECAST(c.type >>  8);
+               invalid_chunk[3] = STBI__BYTECAST(c.type >>  0);
+               #endif
+               return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type");
+            }
+            stbi__skip(s, c.length);
+            break;
+      }
+      // end of PNG chunk, read and skip CRC
+      stbi__get32be(s);
+   }
+}
+
+static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri)
+{
+   void *result=NULL;
+   if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error");
+   if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) {
+      if (p->depth < 8)
+         ri->bits_per_channel = 8;
+      else
+         ri->bits_per_channel = p->depth;
+      result = p->out;
+      p->out = NULL;
+      if (req_comp && req_comp != p->s->img_out_n) {
+         if (ri->bits_per_channel == 8)
+            result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y);
+         else
+            result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y);
+         p->s->img_out_n = req_comp;
+         if (result == NULL) return result;
+      }
+      *x = p->s->img_x;
+      *y = p->s->img_y;
+      if (n) *n = p->s->img_n;
+   }
+   STBI_FREE(p->out);      p->out      = NULL;
+   STBI_FREE(p->expanded); p->expanded = NULL;
+   STBI_FREE(p->idata);    p->idata    = NULL;
+
+   return result;
+}
+
+static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   stbi__png p;
+   p.s = s;
+   return stbi__do_png(&p, x,y,comp,req_comp, ri);
+}
+
+static int stbi__png_test(stbi__context *s)
+{
+   int r;
+   r = stbi__check_png_header(s);
+   stbi__rewind(s);
+   return r;
+}
+
+static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp)
+{
+   if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) {
+      stbi__rewind( p->s );
+      return 0;
+   }
+   if (x) *x = p->s->img_x;
+   if (y) *y = p->s->img_y;
+   if (comp) *comp = p->s->img_n;
+   return 1;
+}
+
+static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   stbi__png p;
+   p.s = s;
+   return stbi__png_info_raw(&p, x, y, comp);
+}
+
+static int stbi__png_is16(stbi__context *s)
+{
+   stbi__png p;
+   p.s = s;
+   if (!stbi__png_info_raw(&p, NULL, NULL, NULL))
+	   return 0;
+   if (p.depth != 16) {
+      stbi__rewind(p.s);
+      return 0;
+   }
+   return 1;
+}
+#endif
+
+// Microsoft/Windows BMP image
+
+#ifndef STBI_NO_BMP
+static int stbi__bmp_test_raw(stbi__context *s)
+{
+   int r;
+   int sz;
+   if (stbi__get8(s) != 'B') return 0;
+   if (stbi__get8(s) != 'M') return 0;
+   stbi__get32le(s); // discard filesize
+   stbi__get16le(s); // discard reserved
+   stbi__get16le(s); // discard reserved
+   stbi__get32le(s); // discard data offset
+   sz = stbi__get32le(s);
+   r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124);
+   return r;
+}
+
+static int stbi__bmp_test(stbi__context *s)
+{
+   int r = stbi__bmp_test_raw(s);
+   stbi__rewind(s);
+   return r;
+}
+
+
+// returns 0..31 for the highest set bit
+static int stbi__high_bit(unsigned int z)
+{
+   int n=0;
+   if (z == 0) return -1;
+   if (z >= 0x10000) n += 16, z >>= 16;
+   if (z >= 0x00100) n +=  8, z >>=  8;
+   if (z >= 0x00010) n +=  4, z >>=  4;
+   if (z >= 0x00004) n +=  2, z >>=  2;
+   if (z >= 0x00002) n +=  1, z >>=  1;
+   return n;
+}
+
+static int stbi__bitcount(unsigned int a)
+{
+   a = (a & 0x55555555) + ((a >>  1) & 0x55555555); // max 2
+   a = (a & 0x33333333) + ((a >>  2) & 0x33333333); // max 4
+   a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits
+   a = (a + (a >> 8)); // max 16 per 8 bits
+   a = (a + (a >> 16)); // max 32 per 8 bits
+   return a & 0xff;
+}
+
+// extract an arbitrarily-aligned N-bit value (N=bits)
+// from v, and then make it 8-bits long and fractionally
+// extend it to full full range.
+static int stbi__shiftsigned(int v, int shift, int bits)
+{
+   static unsigned int mul_table[9] = {
+      0,
+      0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/,
+      0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/,
+   };
+   static unsigned int shift_table[9] = {
+      0, 0,0,1,0,2,4,6,0,
+   };
+   if (shift < 0)
+      v <<= -shift;
+   else
+      v >>= shift;
+   STBI_ASSERT(v >= 0 && v < 256);
+   v >>= (8-bits);
+   STBI_ASSERT(bits >= 0 && bits <= 8);
+   return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits];
+}
+
+typedef struct
+{
+   int bpp, offset, hsz;
+   unsigned int mr,mg,mb,ma, all_a;
+} stbi__bmp_data;
+
+static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info)
+{
+   int hsz;
+   if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP");
+   stbi__get32le(s); // discard filesize
+   stbi__get16le(s); // discard reserved
+   stbi__get16le(s); // discard reserved
+   info->offset = stbi__get32le(s);
+   info->hsz = hsz = stbi__get32le(s);
+   info->mr = info->mg = info->mb = info->ma = 0;
+
+   if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown");
+   if (hsz == 12) {
+      s->img_x = stbi__get16le(s);
+      s->img_y = stbi__get16le(s);
+   } else {
+      s->img_x = stbi__get32le(s);
+      s->img_y = stbi__get32le(s);
+   }
+   if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP");
+   info->bpp = stbi__get16le(s);
+   if (hsz != 12) {
+      int compress = stbi__get32le(s);
+      if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE");
+      stbi__get32le(s); // discard sizeof
+      stbi__get32le(s); // discard hres
+      stbi__get32le(s); // discard vres
+      stbi__get32le(s); // discard colorsused
+      stbi__get32le(s); // discard max important
+      if (hsz == 40 || hsz == 56) {
+         if (hsz == 56) {
+            stbi__get32le(s);
+            stbi__get32le(s);
+            stbi__get32le(s);
+            stbi__get32le(s);
+         }
+         if (info->bpp == 16 || info->bpp == 32) {
+            if (compress == 0) {
+               if (info->bpp == 32) {
+                  info->mr = 0xffu << 16;
+                  info->mg = 0xffu <<  8;
+                  info->mb = 0xffu <<  0;
+                  info->ma = 0xffu << 24;
+                  info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0
+               } else {
+                  info->mr = 31u << 10;
+                  info->mg = 31u <<  5;
+                  info->mb = 31u <<  0;
+               }
+            } else if (compress == 3) {
+               info->mr = stbi__get32le(s);
+               info->mg = stbi__get32le(s);
+               info->mb = stbi__get32le(s);
+               // not documented, but generated by photoshop and handled by mspaint
+               if (info->mr == info->mg && info->mg == info->mb) {
+                  // ?!?!?
+                  return stbi__errpuc("bad BMP", "bad BMP");
+               }
+            } else
+               return stbi__errpuc("bad BMP", "bad BMP");
+         }
+      } else {
+         int i;
+         if (hsz != 108 && hsz != 124)
+            return stbi__errpuc("bad BMP", "bad BMP");
+         info->mr = stbi__get32le(s);
+         info->mg = stbi__get32le(s);
+         info->mb = stbi__get32le(s);
+         info->ma = stbi__get32le(s);
+         stbi__get32le(s); // discard color space
+         for (i=0; i < 12; ++i)
+            stbi__get32le(s); // discard color space parameters
+         if (hsz == 124) {
+            stbi__get32le(s); // discard rendering intent
+            stbi__get32le(s); // discard offset of profile data
+            stbi__get32le(s); // discard size of profile data
+            stbi__get32le(s); // discard reserved
+         }
+      }
+   }
+   return (void *) 1;
+}
+
+
+static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   stbi_uc *out;
+   unsigned int mr=0,mg=0,mb=0,ma=0, all_a;
+   stbi_uc pal[256][4];
+   int psize=0,i,j,width;
+   int flip_vertically, pad, target;
+   stbi__bmp_data info;
+   STBI_NOTUSED(ri);
+
+   info.all_a = 255;
+   if (stbi__bmp_parse_header(s, &info) == NULL)
+      return NULL; // error code already set
+
+   flip_vertically = ((int) s->img_y) > 0;
+   s->img_y = abs((int) s->img_y);
+
+   mr = info.mr;
+   mg = info.mg;
+   mb = info.mb;
+   ma = info.ma;
+   all_a = info.all_a;
+
+   if (info.hsz == 12) {
+      if (info.bpp < 24)
+         psize = (info.offset - 14 - 24) / 3;
+   } else {
+      if (info.bpp < 16)
+         psize = (info.offset - 14 - info.hsz) >> 2;
+   }
+
+   s->img_n = ma ? 4 : 3;
+   if (req_comp && req_comp >= 3) // we can directly decode 3 or 4
+      target = req_comp;
+   else
+      target = s->img_n; // if they want monochrome, we'll post-convert
+
+   // sanity-check size
+   if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0))
+      return stbi__errpuc("too large", "Corrupt BMP");
+
+   out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0);
+   if (!out) return stbi__errpuc("outofmem", "Out of memory");
+   if (info.bpp < 16) {
+      int z=0;
+      if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); }
+      for (i=0; i < psize; ++i) {
+         pal[i][2] = stbi__get8(s);
+         pal[i][1] = stbi__get8(s);
+         pal[i][0] = stbi__get8(s);
+         if (info.hsz != 12) stbi__get8(s);
+         pal[i][3] = 255;
+      }
+      stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4));
+      if (info.bpp == 1) width = (s->img_x + 7) >> 3;
+      else if (info.bpp == 4) width = (s->img_x + 1) >> 1;
+      else if (info.bpp == 8) width = s->img_x;
+      else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); }
+      pad = (-width)&3;
+      if (info.bpp == 1) {
+         for (j=0; j < (int) s->img_y; ++j) {
+            int bit_offset = 7, v = stbi__get8(s);
+            for (i=0; i < (int) s->img_x; ++i) {
+               int color = (v>>bit_offset)&0x1;
+               out[z++] = pal[color][0];
+               out[z++] = pal[color][1];
+               out[z++] = pal[color][2];
+               if((--bit_offset) < 0) {
+                  bit_offset = 7;
+                  v = stbi__get8(s);
+               }
+            }
+            stbi__skip(s, pad);
+         }
+      } else {
+         for (j=0; j < (int) s->img_y; ++j) {
+            for (i=0; i < (int) s->img_x; i += 2) {
+               int v=stbi__get8(s),v2=0;
+               if (info.bpp == 4) {
+                  v2 = v & 15;
+                  v >>= 4;
+               }
+               out[z++] = pal[v][0];
+               out[z++] = pal[v][1];
+               out[z++] = pal[v][2];
+               if (target == 4) out[z++] = 255;
+               if (i+1 == (int) s->img_x) break;
+               v = (info.bpp == 8) ? stbi__get8(s) : v2;
+               out[z++] = pal[v][0];
+               out[z++] = pal[v][1];
+               out[z++] = pal[v][2];
+               if (target == 4) out[z++] = 255;
+            }
+            stbi__skip(s, pad);
+         }
+      }
+   } else {
+      int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0;
+      int z = 0;
+      int easy=0;
+      stbi__skip(s, info.offset - 14 - info.hsz);
+      if (info.bpp == 24) width = 3 * s->img_x;
+      else if (info.bpp == 16) width = 2*s->img_x;
+      else /* bpp = 32 and pad = 0 */ width=0;
+      pad = (-width) & 3;
+      if (info.bpp == 24) {
+         easy = 1;
+      } else if (info.bpp == 32) {
+         if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000)
+            easy = 2;
+      }
+      if (!easy) {
+         if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); }
+         // right shift amt to put high bit in position #7
+         rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr);
+         gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg);
+         bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb);
+         ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma);
+      }
+      for (j=0; j < (int) s->img_y; ++j) {
+         if (easy) {
+            for (i=0; i < (int) s->img_x; ++i) {
+               unsigned char a;
+               out[z+2] = stbi__get8(s);
+               out[z+1] = stbi__get8(s);
+               out[z+0] = stbi__get8(s);
+               z += 3;
+               a = (easy == 2 ? stbi__get8(s) : 255);
+               all_a |= a;
+               if (target == 4) out[z++] = a;
+            }
+         } else {
+            int bpp = info.bpp;
+            for (i=0; i < (int) s->img_x; ++i) {
+               stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s));
+               unsigned int a;
+               out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount));
+               out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount));
+               out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount));
+               a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255);
+               all_a |= a;
+               if (target == 4) out[z++] = STBI__BYTECAST(a);
+            }
+         }
+         stbi__skip(s, pad);
+      }
+   }
+
+   // if alpha channel is all 0s, replace with all 255s
+   if (target == 4 && all_a == 0)
+      for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4)
+         out[i] = 255;
+
+   if (flip_vertically) {
+      stbi_uc t;
+      for (j=0; j < (int) s->img_y>>1; ++j) {
+         stbi_uc *p1 = out +      j     *s->img_x*target;
+         stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target;
+         for (i=0; i < (int) s->img_x*target; ++i) {
+            t = p1[i], p1[i] = p2[i], p2[i] = t;
+         }
+      }
+   }
+
+   if (req_comp && req_comp != target) {
+      out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y);
+      if (out == NULL) return out; // stbi__convert_format frees input on failure
+   }
+
+   *x = s->img_x;
+   *y = s->img_y;
+   if (comp) *comp = s->img_n;
+   return out;
+}
+#endif
+
+// Targa Truevision - TGA
+// by Jonathan Dummer
+#ifndef STBI_NO_TGA
+// returns STBI_rgb or whatever, 0 on error
+static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16)
+{
+   // only RGB or RGBA (incl. 16bit) or grey allowed
+   if (is_rgb16) *is_rgb16 = 0;
+   switch(bits_per_pixel) {
+      case 8:  return STBI_grey;
+      case 16: if(is_grey) return STBI_grey_alpha;
+               // fallthrough
+      case 15: if(is_rgb16) *is_rgb16 = 1;
+               return STBI_rgb;
+      case 24: // fallthrough
+      case 32: return bits_per_pixel/8;
+      default: return 0;
+   }
+}
+
+static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp)
+{
+    int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp;
+    int sz, tga_colormap_type;
+    stbi__get8(s);                   // discard Offset
+    tga_colormap_type = stbi__get8(s); // colormap type
+    if( tga_colormap_type > 1 ) {
+        stbi__rewind(s);
+        return 0;      // only RGB or indexed allowed
+    }
+    tga_image_type = stbi__get8(s); // image type
+    if ( tga_colormap_type == 1 ) { // colormapped (paletted) image
+        if (tga_image_type != 1 && tga_image_type != 9) {
+            stbi__rewind(s);
+            return 0;
+        }
+        stbi__skip(s,4);       // skip index of first colormap entry and number of entries
+        sz = stbi__get8(s);    //   check bits per palette color entry
+        if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) {
+            stbi__rewind(s);
+            return 0;
+        }
+        stbi__skip(s,4);       // skip image x and y origin
+        tga_colormap_bpp = sz;
+    } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE
+        if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) {
+            stbi__rewind(s);
+            return 0; // only RGB or grey allowed, +/- RLE
+        }
+        stbi__skip(s,9); // skip colormap specification and image x/y origin
+        tga_colormap_bpp = 0;
+    }
+    tga_w = stbi__get16le(s);
+    if( tga_w < 1 ) {
+        stbi__rewind(s);
+        return 0;   // test width
+    }
+    tga_h = stbi__get16le(s);
+    if( tga_h < 1 ) {
+        stbi__rewind(s);
+        return 0;   // test height
+    }
+    tga_bits_per_pixel = stbi__get8(s); // bits per pixel
+    stbi__get8(s); // ignore alpha bits
+    if (tga_colormap_bpp != 0) {
+        if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) {
+            // when using a colormap, tga_bits_per_pixel is the size of the indexes
+            // I don't think anything but 8 or 16bit indexes makes sense
+            stbi__rewind(s);
+            return 0;
+        }
+        tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL);
+    } else {
+        tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL);
+    }
+    if(!tga_comp) {
+      stbi__rewind(s);
+      return 0;
+    }
+    if (x) *x = tga_w;
+    if (y) *y = tga_h;
+    if (comp) *comp = tga_comp;
+    return 1;                   // seems to have passed everything
+}
+
+static int stbi__tga_test(stbi__context *s)
+{
+   int res = 0;
+   int sz, tga_color_type;
+   stbi__get8(s);      //   discard Offset
+   tga_color_type = stbi__get8(s);   //   color type
+   if ( tga_color_type > 1 ) goto errorEnd;   //   only RGB or indexed allowed
+   sz = stbi__get8(s);   //   image type
+   if ( tga_color_type == 1 ) { // colormapped (paletted) image
+      if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9
+      stbi__skip(s,4);       // skip index of first colormap entry and number of entries
+      sz = stbi__get8(s);    //   check bits per palette color entry
+      if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd;
+      stbi__skip(s,4);       // skip image x and y origin
+   } else { // "normal" image w/o colormap
+      if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE
+      stbi__skip(s,9); // skip colormap specification and image x/y origin
+   }
+   if ( stbi__get16le(s) < 1 ) goto errorEnd;      //   test width
+   if ( stbi__get16le(s) < 1 ) goto errorEnd;      //   test height
+   sz = stbi__get8(s);   //   bits per pixel
+   if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index
+   if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd;
+
+   res = 1; // if we got this far, everything's good and we can return 1 instead of 0
+
+errorEnd:
+   stbi__rewind(s);
+   return res;
+}
+
+// read 16bit value and convert to 24bit RGB
+static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out)
+{
+   stbi__uint16 px = (stbi__uint16)stbi__get16le(s);
+   stbi__uint16 fiveBitMask = 31;
+   // we have 3 channels with 5bits each
+   int r = (px >> 10) & fiveBitMask;
+   int g = (px >> 5) & fiveBitMask;
+   int b = px & fiveBitMask;
+   // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later
+   out[0] = (stbi_uc)((r * 255)/31);
+   out[1] = (stbi_uc)((g * 255)/31);
+   out[2] = (stbi_uc)((b * 255)/31);
+
+   // some people claim that the most significant bit might be used for alpha
+   // (possibly if an alpha-bit is set in the "image descriptor byte")
+   // but that only made 16bit test images completely translucent..
+   // so let's treat all 15 and 16bit TGAs as RGB with no alpha.
+}
+
+static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   //   read in the TGA header stuff
+   int tga_offset = stbi__get8(s);
+   int tga_indexed = stbi__get8(s);
+   int tga_image_type = stbi__get8(s);
+   int tga_is_RLE = 0;
+   int tga_palette_start = stbi__get16le(s);
+   int tga_palette_len = stbi__get16le(s);
+   int tga_palette_bits = stbi__get8(s);
+   int tga_x_origin = stbi__get16le(s);
+   int tga_y_origin = stbi__get16le(s);
+   int tga_width = stbi__get16le(s);
+   int tga_height = stbi__get16le(s);
+   int tga_bits_per_pixel = stbi__get8(s);
+   int tga_comp, tga_rgb16=0;
+   int tga_inverted = stbi__get8(s);
+   // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?)
+   //   image data
+   unsigned char *tga_data;
+   unsigned char *tga_palette = NULL;
+   int i, j;
+   unsigned char raw_data[4] = {0};
+   int RLE_count = 0;
+   int RLE_repeating = 0;
+   int read_next_pixel = 1;
+   STBI_NOTUSED(ri);
+
+   //   do a tiny bit of precessing
+   if ( tga_image_type >= 8 )
+   {
+      tga_image_type -= 8;
+      tga_is_RLE = 1;
+   }
+   tga_inverted = 1 - ((tga_inverted >> 5) & 1);
+
+   //   If I'm paletted, then I'll use the number of bits from the palette
+   if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16);
+   else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16);
+
+   if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency
+      return stbi__errpuc("bad format", "Can't find out TGA pixelformat");
+
+   //   tga info
+   *x = tga_width;
+   *y = tga_height;
+   if (comp) *comp = tga_comp;
+
+   if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0))
+      return stbi__errpuc("too large", "Corrupt TGA");
+
+   tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0);
+   if (!tga_data) return stbi__errpuc("outofmem", "Out of memory");
+
+   // skip to the data's starting position (offset usually = 0)
+   stbi__skip(s, tga_offset );
+
+   if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) {
+      for (i=0; i < tga_height; ++i) {
+         int row = tga_inverted ? tga_height -i - 1 : i;
+         stbi_uc *tga_row = tga_data + row*tga_width*tga_comp;
+         stbi__getn(s, tga_row, tga_width * tga_comp);
+      }
+   } else  {
+      //   do I need to load a palette?
+      if ( tga_indexed)
+      {
+         //   any data to skip? (offset usually = 0)
+         stbi__skip(s, tga_palette_start );
+         //   load the palette
+         tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0);
+         if (!tga_palette) {
+            STBI_FREE(tga_data);
+            return stbi__errpuc("outofmem", "Out of memory");
+         }
+         if (tga_rgb16) {
+            stbi_uc *pal_entry = tga_palette;
+            STBI_ASSERT(tga_comp == STBI_rgb);
+            for (i=0; i < tga_palette_len; ++i) {
+               stbi__tga_read_rgb16(s, pal_entry);
+               pal_entry += tga_comp;
+            }
+         } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) {
+               STBI_FREE(tga_data);
+               STBI_FREE(tga_palette);
+               return stbi__errpuc("bad palette", "Corrupt TGA");
+         }
+      }
+      //   load the data
+      for (i=0; i < tga_width * tga_height; ++i)
+      {
+         //   if I'm in RLE mode, do I need to get a RLE stbi__pngchunk?
+         if ( tga_is_RLE )
+         {
+            if ( RLE_count == 0 )
+            {
+               //   yep, get the next byte as a RLE command
+               int RLE_cmd = stbi__get8(s);
+               RLE_count = 1 + (RLE_cmd & 127);
+               RLE_repeating = RLE_cmd >> 7;
+               read_next_pixel = 1;
+            } else if ( !RLE_repeating )
+            {
+               read_next_pixel = 1;
+            }
+         } else
+         {
+            read_next_pixel = 1;
+         }
+         //   OK, if I need to read a pixel, do it now
+         if ( read_next_pixel )
+         {
+            //   load however much data we did have
+            if ( tga_indexed )
+            {
+               // read in index, then perform the lookup
+               int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s);
+               if ( pal_idx >= tga_palette_len ) {
+                  // invalid index
+                  pal_idx = 0;
+               }
+               pal_idx *= tga_comp;
+               for (j = 0; j < tga_comp; ++j) {
+                  raw_data[j] = tga_palette[pal_idx+j];
+               }
+            } else if(tga_rgb16) {
+               STBI_ASSERT(tga_comp == STBI_rgb);
+               stbi__tga_read_rgb16(s, raw_data);
+            } else {
+               //   read in the data raw
+               for (j = 0; j < tga_comp; ++j) {
+                  raw_data[j] = stbi__get8(s);
+               }
+            }
+            //   clear the reading flag for the next pixel
+            read_next_pixel = 0;
+         } // end of reading a pixel
+
+         // copy data
+         for (j = 0; j < tga_comp; ++j)
+           tga_data[i*tga_comp+j] = raw_data[j];
+
+         //   in case we're in RLE mode, keep counting down
+         --RLE_count;
+      }
+      //   do I need to invert the image?
+      if ( tga_inverted )
+      {
+         for (j = 0; j*2 < tga_height; ++j)
+         {
+            int index1 = j * tga_width * tga_comp;
+            int index2 = (tga_height - 1 - j) * tga_width * tga_comp;
+            for (i = tga_width * tga_comp; i > 0; --i)
+            {
+               unsigned char temp = tga_data[index1];
+               tga_data[index1] = tga_data[index2];
+               tga_data[index2] = temp;
+               ++index1;
+               ++index2;
+            }
+         }
+      }
+      //   clear my palette, if I had one
+      if ( tga_palette != NULL )
+      {
+         STBI_FREE( tga_palette );
+      }
+   }
+
+   // swap RGB - if the source data was RGB16, it already is in the right order
+   if (tga_comp >= 3 && !tga_rgb16)
+   {
+      unsigned char* tga_pixel = tga_data;
+      for (i=0; i < tga_width * tga_height; ++i)
+      {
+         unsigned char temp = tga_pixel[0];
+         tga_pixel[0] = tga_pixel[2];
+         tga_pixel[2] = temp;
+         tga_pixel += tga_comp;
+      }
+   }
+
+   // convert to target component count
+   if (req_comp && req_comp != tga_comp)
+      tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height);
+
+   //   the things I do to get rid of an error message, and yet keep
+   //   Microsoft's C compilers happy... [8^(
+   tga_palette_start = tga_palette_len = tga_palette_bits =
+         tga_x_origin = tga_y_origin = 0;
+   //   OK, done
+   return tga_data;
+}
+#endif
+
+// *************************************************************************************************
+// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB
+
+#ifndef STBI_NO_PSD
+static int stbi__psd_test(stbi__context *s)
+{
+   int r = (stbi__get32be(s) == 0x38425053);
+   stbi__rewind(s);
+   return r;
+}
+
+static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount)
+{
+   int count, nleft, len;
+
+   count = 0;
+   while ((nleft = pixelCount - count) > 0) {
+      len = stbi__get8(s);
+      if (len == 128) {
+         // No-op.
+      } else if (len < 128) {
+         // Copy next len+1 bytes literally.
+         len++;
+         if (len > nleft) return 0; // corrupt data
+         count += len;
+         while (len) {
+            *p = stbi__get8(s);
+            p += 4;
+            len--;
+         }
+      } else if (len > 128) {
+         stbi_uc   val;
+         // Next -len+1 bytes in the dest are replicated from next source byte.
+         // (Interpret len as a negative 8-bit int.)
+         len = 257 - len;
+         if (len > nleft) return 0; // corrupt data
+         val = stbi__get8(s);
+         count += len;
+         while (len) {
+            *p = val;
+            p += 4;
+            len--;
+         }
+      }
+   }
+
+   return 1;
+}
+
+static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc)
+{
+   int pixelCount;
+   int channelCount, compression;
+   int channel, i;
+   int bitdepth;
+   int w,h;
+   stbi_uc *out;
+   STBI_NOTUSED(ri);
+
+   // Check identifier
+   if (stbi__get32be(s) != 0x38425053)   // "8BPS"
+      return stbi__errpuc("not PSD", "Corrupt PSD image");
+
+   // Check file type version.
+   if (stbi__get16be(s) != 1)
+      return stbi__errpuc("wrong version", "Unsupported version of PSD image");
+
+   // Skip 6 reserved bytes.
+   stbi__skip(s, 6 );
+
+   // Read the number of channels (R, G, B, A, etc).
+   channelCount = stbi__get16be(s);
+   if (channelCount < 0 || channelCount > 16)
+      return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image");
+
+   // Read the rows and columns of the image.
+   h = stbi__get32be(s);
+   w = stbi__get32be(s);
+
+   // Make sure the depth is 8 bits.
+   bitdepth = stbi__get16be(s);
+   if (bitdepth != 8 && bitdepth != 16)
+      return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit");
+
+   // Make sure the color mode is RGB.
+   // Valid options are:
+   //   0: Bitmap
+   //   1: Grayscale
+   //   2: Indexed color
+   //   3: RGB color
+   //   4: CMYK color
+   //   7: Multichannel
+   //   8: Duotone
+   //   9: Lab color
+   if (stbi__get16be(s) != 3)
+      return stbi__errpuc("wrong color format", "PSD is not in RGB color format");
+
+   // Skip the Mode Data.  (It's the palette for indexed color; other info for other modes.)
+   stbi__skip(s,stbi__get32be(s) );
+
+   // Skip the image resources.  (resolution, pen tool paths, etc)
+   stbi__skip(s, stbi__get32be(s) );
+
+   // Skip the reserved data.
+   stbi__skip(s, stbi__get32be(s) );
+
+   // Find out if the data is compressed.
+   // Known values:
+   //   0: no compression
+   //   1: RLE compressed
+   compression = stbi__get16be(s);
+   if (compression > 1)
+      return stbi__errpuc("bad compression", "PSD has an unknown compression format");
+
+   // Check size
+   if (!stbi__mad3sizes_valid(4, w, h, 0))
+      return stbi__errpuc("too large", "Corrupt PSD");
+
+   // Create the destination image.
+
+   if (!compression && bitdepth == 16 && bpc == 16) {
+      out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0);
+      ri->bits_per_channel = 16;
+   } else
+      out = (stbi_uc *) stbi__malloc(4 * w*h);
+
+   if (!out) return stbi__errpuc("outofmem", "Out of memory");
+   pixelCount = w*h;
+
+   // Initialize the data to zero.
+   //memset( out, 0, pixelCount * 4 );
+
+   // Finally, the image data.
+   if (compression) {
+      // RLE as used by .PSD and .TIFF
+      // Loop until you get the number of unpacked bytes you are expecting:
+      //     Read the next source byte into n.
+      //     If n is between 0 and 127 inclusive, copy the next n+1 bytes literally.
+      //     Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times.
+      //     Else if n is 128, noop.
+      // Endloop
+
+      // The RLE-compressed data is preceeded by a 2-byte data count for each row in the data,
+      // which we're going to just skip.
+      stbi__skip(s, h * channelCount * 2 );
+
+      // Read the RLE data by channel.
+      for (channel = 0; channel < 4; channel++) {
+         stbi_uc *p;
+
+         p = out+channel;
+         if (channel >= channelCount) {
+            // Fill this channel with default data.
+            for (i = 0; i < pixelCount; i++, p += 4)
+               *p = (channel == 3 ? 255 : 0);
+         } else {
+            // Read the RLE data.
+            if (!stbi__psd_decode_rle(s, p, pixelCount)) {
+               STBI_FREE(out);
+               return stbi__errpuc("corrupt", "bad RLE data");
+            }
+         }
+      }
+
+   } else {
+      // We're at the raw image data.  It's each channel in order (Red, Green, Blue, Alpha, ...)
+      // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image.
+
+      // Read the data by channel.
+      for (channel = 0; channel < 4; channel++) {
+         if (channel >= channelCount) {
+            // Fill this channel with default data.
+            if (bitdepth == 16 && bpc == 16) {
+               stbi__uint16 *q = ((stbi__uint16 *) out) + channel;
+               stbi__uint16 val = channel == 3 ? 65535 : 0;
+               for (i = 0; i < pixelCount; i++, q += 4)
+                  *q = val;
+            } else {
+               stbi_uc *p = out+channel;
+               stbi_uc val = channel == 3 ? 255 : 0;
+               for (i = 0; i < pixelCount; i++, p += 4)
+                  *p = val;
+            }
+         } else {
+            if (ri->bits_per_channel == 16) {    // output bpc
+               stbi__uint16 *q = ((stbi__uint16 *) out) + channel;
+               for (i = 0; i < pixelCount; i++, q += 4)
+                  *q = (stbi__uint16) stbi__get16be(s);
+            } else {
+               stbi_uc *p = out+channel;
+               if (bitdepth == 16) {  // input bpc
+                  for (i = 0; i < pixelCount; i++, p += 4)
+                     *p = (stbi_uc) (stbi__get16be(s) >> 8);
+               } else {
+                  for (i = 0; i < pixelCount; i++, p += 4)
+                     *p = stbi__get8(s);
+               }
+            }
+         }
+      }
+   }
+
+   // remove weird white matte from PSD
+   if (channelCount >= 4) {
+      if (ri->bits_per_channel == 16) {
+         for (i=0; i < w*h; ++i) {
+            stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i;
+            if (pixel[3] != 0 && pixel[3] != 65535) {
+               float a = pixel[3] / 65535.0f;
+               float ra = 1.0f / a;
+               float inv_a = 65535.0f * (1 - ra);
+               pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a);
+               pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a);
+               pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a);
+            }
+         }
+      } else {
+         for (i=0; i < w*h; ++i) {
+            unsigned char *pixel = out + 4*i;
+            if (pixel[3] != 0 && pixel[3] != 255) {
+               float a = pixel[3] / 255.0f;
+               float ra = 1.0f / a;
+               float inv_a = 255.0f * (1 - ra);
+               pixel[0] = (unsigned char) (pixel[0]*ra + inv_a);
+               pixel[1] = (unsigned char) (pixel[1]*ra + inv_a);
+               pixel[2] = (unsigned char) (pixel[2]*ra + inv_a);
+            }
+         }
+      }
+   }
+
+   // convert to desired output format
+   if (req_comp && req_comp != 4) {
+      if (ri->bits_per_channel == 16)
+         out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h);
+      else
+         out = stbi__convert_format(out, 4, req_comp, w, h);
+      if (out == NULL) return out; // stbi__convert_format frees input on failure
+   }
+
+   if (comp) *comp = 4;
+   *y = h;
+   *x = w;
+
+   return out;
+}
+#endif
+
+// *************************************************************************************************
+// Softimage PIC loader
+// by Tom Seddon
+//
+// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format
+// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/
+
+#ifndef STBI_NO_PIC
+static int stbi__pic_is4(stbi__context *s,const char *str)
+{
+   int i;
+   for (i=0; i<4; ++i)
+      if (stbi__get8(s) != (stbi_uc)str[i])
+         return 0;
+
+   return 1;
+}
+
+static int stbi__pic_test_core(stbi__context *s)
+{
+   int i;
+
+   if (!stbi__pic_is4(s,"\x53\x80\xF6\x34"))
+      return 0;
+
+   for(i=0;i<84;++i)
+      stbi__get8(s);
+
+   if (!stbi__pic_is4(s,"PICT"))
+      return 0;
+
+   return 1;
+}
+
+typedef struct
+{
+   stbi_uc size,type,channel;
+} stbi__pic_packet;
+
+static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest)
+{
+   int mask=0x80, i;
+
+   for (i=0; i<4; ++i, mask>>=1) {
+      if (channel & mask) {
+         if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short");
+         dest[i]=stbi__get8(s);
+      }
+   }
+
+   return dest;
+}
+
+static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src)
+{
+   int mask=0x80,i;
+
+   for (i=0;i<4; ++i, mask>>=1)
+      if (channel&mask)
+         dest[i]=src[i];
+}
+
+static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result)
+{
+   int act_comp=0,num_packets=0,y,chained;
+   stbi__pic_packet packets[10];
+
+   // this will (should...) cater for even some bizarre stuff like having data
+    // for the same channel in multiple packets.
+   do {
+      stbi__pic_packet *packet;
+
+      if (num_packets==sizeof(packets)/sizeof(packets[0]))
+         return stbi__errpuc("bad format","too many packets");
+
+      packet = &packets[num_packets++];
+
+      chained = stbi__get8(s);
+      packet->size    = stbi__get8(s);
+      packet->type    = stbi__get8(s);
+      packet->channel = stbi__get8(s);
+
+      act_comp |= packet->channel;
+
+      if (stbi__at_eof(s))          return stbi__errpuc("bad file","file too short (reading packets)");
+      if (packet->size != 8)  return stbi__errpuc("bad format","packet isn't 8bpp");
+   } while (chained);
+
+   *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel?
+
+   for(y=0; y<height; ++y) {
+      int packet_idx;
+
+      for(packet_idx=0; packet_idx < num_packets; ++packet_idx) {
+         stbi__pic_packet *packet = &packets[packet_idx];
+         stbi_uc *dest = result+y*width*4;
+
+         switch (packet->type) {
+            default:
+               return stbi__errpuc("bad format","packet has bad compression type");
+
+            case 0: {//uncompressed
+               int x;
+
+               for(x=0;x<width;++x, dest+=4)
+                  if (!stbi__readval(s,packet->channel,dest))
+                     return 0;
+               break;
+            }
+
+            case 1://Pure RLE
+               {
+                  int left=width, i;
+
+                  while (left>0) {
+                     stbi_uc count,value[4];
+
+                     count=stbi__get8(s);
+                     if (stbi__at_eof(s))   return stbi__errpuc("bad file","file too short (pure read count)");
+
+                     if (count > left)
+                        count = (stbi_uc) left;
+
+                     if (!stbi__readval(s,packet->channel,value))  return 0;
+
+                     for(i=0; i<count; ++i,dest+=4)
+                        stbi__copyval(packet->channel,dest,value);
+                     left -= count;
+                  }
+               }
+               break;
+
+            case 2: {//Mixed RLE
+               int left=width;
+               while (left>0) {
+                  int count = stbi__get8(s), i;
+                  if (stbi__at_eof(s))  return stbi__errpuc("bad file","file too short (mixed read count)");
+
+                  if (count >= 128) { // Repeated
+                     stbi_uc value[4];
+
+                     if (count==128)
+                        count = stbi__get16be(s);
+                     else
+                        count -= 127;
+                     if (count > left)
+                        return stbi__errpuc("bad file","scanline overrun");
+
+                     if (!stbi__readval(s,packet->channel,value))
+                        return 0;
+
+                     for(i=0;i<count;++i, dest += 4)
+                        stbi__copyval(packet->channel,dest,value);
+                  } else { // Raw
+                     ++count;
+                     if (count>left) return stbi__errpuc("bad file","scanline overrun");
+
+                     for(i=0;i<count;++i, dest+=4)
+                        if (!stbi__readval(s,packet->channel,dest))
+                           return 0;
+                  }
+                  left-=count;
+               }
+               break;
+            }
+         }
+      }
+   }
+
+   return result;
+}
+
+static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri)
+{
+   stbi_uc *result;
+   int i, x,y, internal_comp;
+   STBI_NOTUSED(ri);
+
+   if (!comp) comp = &internal_comp;
+
+   for (i=0; i<92; ++i)
+      stbi__get8(s);
+
+   x = stbi__get16be(s);
+   y = stbi__get16be(s);
+   if (stbi__at_eof(s))  return stbi__errpuc("bad file","file too short (pic header)");
+   if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode");
+
+   stbi__get32be(s); //skip `ratio'
+   stbi__get16be(s); //skip `fields'
+   stbi__get16be(s); //skip `pad'
+
+   // intermediate buffer is RGBA
+   result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0);
+   memset(result, 0xff, x*y*4);
+
+   if (!stbi__pic_load_core(s,x,y,comp, result)) {
+      STBI_FREE(result);
+      result=0;
+   }
+   *px = x;
+   *py = y;
+   if (req_comp == 0) req_comp = *comp;
+   result=stbi__convert_format(result,4,req_comp,x,y);
+
+   return result;
+}
+
+static int stbi__pic_test(stbi__context *s)
+{
+   int r = stbi__pic_test_core(s);
+   stbi__rewind(s);
+   return r;
+}
+#endif
+
+// *************************************************************************************************
+// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb
+
+#ifndef STBI_NO_GIF
+typedef struct
+{
+   stbi__int16 prefix;
+   stbi_uc first;
+   stbi_uc suffix;
+} stbi__gif_lzw;
+
+typedef struct
+{
+   int w,h;
+   stbi_uc *out;                 // output buffer (always 4 components)
+   stbi_uc *background;          // The current "background" as far as a gif is concerned
+   stbi_uc *history; 
+   int flags, bgindex, ratio, transparent, eflags;
+   stbi_uc  pal[256][4];
+   stbi_uc lpal[256][4];
+   stbi__gif_lzw codes[8192];
+   stbi_uc *color_table;
+   int parse, step;
+   int lflags;
+   int start_x, start_y;
+   int max_x, max_y;
+   int cur_x, cur_y;
+   int line_size;
+   int delay;
+} stbi__gif;
+
+static int stbi__gif_test_raw(stbi__context *s)
+{
+   int sz;
+   if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0;
+   sz = stbi__get8(s);
+   if (sz != '9' && sz != '7') return 0;
+   if (stbi__get8(s) != 'a') return 0;
+   return 1;
+}
+
+static int stbi__gif_test(stbi__context *s)
+{
+   int r = stbi__gif_test_raw(s);
+   stbi__rewind(s);
+   return r;
+}
+
+static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp)
+{
+   int i;
+   for (i=0; i < num_entries; ++i) {
+      pal[i][2] = stbi__get8(s);
+      pal[i][1] = stbi__get8(s);
+      pal[i][0] = stbi__get8(s);
+      pal[i][3] = transp == i ? 0 : 255;
+   }
+}
+
+static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info)
+{
+   stbi_uc version;
+   if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8')
+      return stbi__err("not GIF", "Corrupt GIF");
+
+   version = stbi__get8(s);
+   if (version != '7' && version != '9')    return stbi__err("not GIF", "Corrupt GIF");
+   if (stbi__get8(s) != 'a')                return stbi__err("not GIF", "Corrupt GIF");
+
+   stbi__g_failure_reason = "";
+   g->w = stbi__get16le(s);
+   g->h = stbi__get16le(s);
+   g->flags = stbi__get8(s);
+   g->bgindex = stbi__get8(s);
+   g->ratio = stbi__get8(s);
+   g->transparent = -1;
+
+   if (comp != 0) *comp = 4;  // can't actually tell whether it's 3 or 4 until we parse the comments
+
+   if (is_info) return 1;
+
+   if (g->flags & 0x80)
+      stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1);
+
+   return 1;
+}
+
+static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp)
+{
+   stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif));
+   if (!stbi__gif_header(s, g, comp, 1)) {
+      STBI_FREE(g);
+      stbi__rewind( s );
+      return 0;
+   }
+   if (x) *x = g->w;
+   if (y) *y = g->h;
+   STBI_FREE(g);
+   return 1;
+}
+
+static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code)
+{
+   stbi_uc *p, *c;
+   int idx; 
+
+   // recurse to decode the prefixes, since the linked-list is backwards,
+   // and working backwards through an interleaved image would be nasty
+   if (g->codes[code].prefix >= 0)
+      stbi__out_gif_code(g, g->codes[code].prefix);
+
+   if (g->cur_y >= g->max_y) return;
+
+   idx = g->cur_x + g->cur_y; 
+   p = &g->out[idx];
+   g->history[idx / 4] = 1;  
+
+   c = &g->color_table[g->codes[code].suffix * 4];
+   if (c[3] > 128) { // don't render transparent pixels; 
+      p[0] = c[2];
+      p[1] = c[1];
+      p[2] = c[0];
+      p[3] = c[3];
+   }
+   g->cur_x += 4;
+
+   if (g->cur_x >= g->max_x) {
+      g->cur_x = g->start_x;
+      g->cur_y += g->step;
+
+      while (g->cur_y >= g->max_y && g->parse > 0) {
+         g->step = (1 << g->parse) * g->line_size;
+         g->cur_y = g->start_y + (g->step >> 1);
+         --g->parse;
+      }
+   }
+}
+
+static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g)
+{
+   stbi_uc lzw_cs;
+   stbi__int32 len, init_code;
+   stbi__uint32 first;
+   stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear;
+   stbi__gif_lzw *p;
+
+   lzw_cs = stbi__get8(s);
+   if (lzw_cs > 12) return NULL;
+   clear = 1 << lzw_cs;
+   first = 1;
+   codesize = lzw_cs + 1;
+   codemask = (1 << codesize) - 1;
+   bits = 0;
+   valid_bits = 0;
+   for (init_code = 0; init_code < clear; init_code++) {
+      g->codes[init_code].prefix = -1;
+      g->codes[init_code].first = (stbi_uc) init_code;
+      g->codes[init_code].suffix = (stbi_uc) init_code;
+   }
+
+   // support no starting clear code
+   avail = clear+2;
+   oldcode = -1;
+
+   len = 0;
+   for(;;) {
+      if (valid_bits < codesize) {
+         if (len == 0) {
+            len = stbi__get8(s); // start new block
+            if (len == 0)
+               return g->out;
+         }
+         --len;
+         bits |= (stbi__int32) stbi__get8(s) << valid_bits;
+         valid_bits += 8;
+      } else {
+         stbi__int32 code = bits & codemask;
+         bits >>= codesize;
+         valid_bits -= codesize;
+         // @OPTIMIZE: is there some way we can accelerate the non-clear path?
+         if (code == clear) {  // clear code
+            codesize = lzw_cs + 1;
+            codemask = (1 << codesize) - 1;
+            avail = clear + 2;
+            oldcode = -1;
+            first = 0;
+         } else if (code == clear + 1) { // end of stream code
+            stbi__skip(s, len);
+            while ((len = stbi__get8(s)) > 0)
+               stbi__skip(s,len);
+            return g->out;
+         } else if (code <= avail) {
+            if (first) {
+               return stbi__errpuc("no clear code", "Corrupt GIF");
+            }
+
+            if (oldcode >= 0) {
+               p = &g->codes[avail++];
+               if (avail > 8192) {
+                  return stbi__errpuc("too many codes", "Corrupt GIF");
+               }
+
+               p->prefix = (stbi__int16) oldcode;
+               p->first = g->codes[oldcode].first;
+               p->suffix = (code == avail) ? p->first : g->codes[code].first;
+            } else if (code == avail)
+               return stbi__errpuc("illegal code in raster", "Corrupt GIF");
+
+            stbi__out_gif_code(g, (stbi__uint16) code);
+
+            if ((avail & codemask) == 0 && avail <= 0x0FFF) {
+               codesize++;
+               codemask = (1 << codesize) - 1;
+            }
+
+            oldcode = code;
+         } else {
+            return stbi__errpuc("illegal code in raster", "Corrupt GIF");
+         }
+      }
+   }
+}
+
+// this function is designed to support animated gifs, although stb_image doesn't support it
+// two back is the image from two frames ago, used for a very specific disposal format
+static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back)
+{
+   int dispose; 
+   int first_frame; 
+   int pi; 
+   int pcount; 
+
+   // on first frame, any non-written pixels get the background colour (non-transparent)
+   first_frame = 0; 
+   if (g->out == 0) {
+      if (!stbi__gif_header(s, g, comp,0))     return 0; // stbi__g_failure_reason set by stbi__gif_header
+      g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h);
+      g->background = (stbi_uc *) stbi__malloc(4 * g->w * g->h); 
+      g->history = (stbi_uc *) stbi__malloc(g->w * g->h); 
+      if (g->out == 0)                      return stbi__errpuc("outofmem", "Out of memory");
+
+      // image is treated as "tranparent" at the start - ie, nothing overwrites the current background; 
+      // background colour is only used for pixels that are not rendered first frame, after that "background"
+      // color refers to teh color that was there the previous frame. 
+      memset( g->out, 0x00, 4 * g->w * g->h ); 
+      memset( g->background, 0x00, 4 * g->w * g->h ); // state of the background (starts transparent)
+      memset( g->history, 0x00, g->w * g->h );        // pixels that were affected previous frame
+      first_frame = 1; 
+   } else {
+      // second frame - how do we dispoase of the previous one?
+      dispose = (g->eflags & 0x1C) >> 2; 
+      pcount = g->w * g->h; 
+
+      if ((dispose == 3) && (two_back == 0)) {
+         dispose = 2; // if I don't have an image to revert back to, default to the old background
+      }
+
+      if (dispose == 3) { // use previous graphic
+         for (pi = 0; pi < pcount; ++pi) {
+            if (g->history[pi]) {
+               memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); 
+            }
+         }
+      } else if (dispose == 2) { 
+         // restore what was changed last frame to background before that frame; 
+         for (pi = 0; pi < pcount; ++pi) {
+            if (g->history[pi]) {
+               memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); 
+            }
+         }
+      } else {
+         // This is a non-disposal case eithe way, so just 
+         // leave the pixels as is, and they will become the new background
+         // 1: do not dispose
+         // 0:  not specified.
+      }
+
+      // background is what out is after the undoing of the previou frame; 
+      memcpy( g->background, g->out, 4 * g->w * g->h ); 
+   }
+
+   // clear my history; 
+   memset( g->history, 0x00, g->w * g->h );        // pixels that were affected previous frame
+
+   for (;;) {
+      int tag = stbi__get8(s); 
+      switch (tag) {
+         case 0x2C: /* Image Descriptor */
+         {
+            stbi__int32 x, y, w, h;
+            stbi_uc *o;
+
+            x = stbi__get16le(s);
+            y = stbi__get16le(s);
+            w = stbi__get16le(s);
+            h = stbi__get16le(s);
+            if (((x + w) > (g->w)) || ((y + h) > (g->h)))
+               return stbi__errpuc("bad Image Descriptor", "Corrupt GIF");
+
+            g->line_size = g->w * 4;
+            g->start_x = x * 4;
+            g->start_y = y * g->line_size;
+            g->max_x   = g->start_x + w * 4;
+            g->max_y   = g->start_y + h * g->line_size;
+            g->cur_x   = g->start_x;
+            g->cur_y   = g->start_y;
+
+            g->lflags = stbi__get8(s);
+
+            if (g->lflags & 0x40) {
+               g->step = 8 * g->line_size; // first interlaced spacing
+               g->parse = 3;
+            } else {
+               g->step = g->line_size;
+               g->parse = 0;
+            }
+
+            if (g->lflags & 0x80) {
+               stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1);
+               g->color_table = (stbi_uc *) g->lpal;
+            } else if (g->flags & 0x80) {
+               g->color_table = (stbi_uc *) g->pal;
+            } else
+               return stbi__errpuc("missing color table", "Corrupt GIF");            
+            
+            o = stbi__process_gif_raster(s, g);
+            if (o == NULL) return NULL;
+
+            // if this was the first frame, 
+            pcount = g->w * g->h; 
+            if (first_frame && (g->bgindex > 0)) {
+               // if first frame, any pixel not drawn to gets the background color
+               for (pi = 0; pi < pcount; ++pi) {
+                  if (g->history[pi] == 0) {
+                     g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; 
+                     memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); 
+                  }
+               }
+            }
+
+            return o;
+         }
+
+         case 0x21: // Comment Extension.
+         {
+            int len;
+            int ext = stbi__get8(s); 
+            if (ext == 0xF9) { // Graphic Control Extension.
+               len = stbi__get8(s);
+               if (len == 4) {
+                  g->eflags = stbi__get8(s);
+                  g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths.
+
+                  // unset old transparent
+                  if (g->transparent >= 0) {
+                     g->pal[g->transparent][3] = 255; 
+                  } 
+                  if (g->eflags & 0x01) {
+                     g->transparent = stbi__get8(s);
+                     if (g->transparent >= 0) {
+                        g->pal[g->transparent][3] = 0; 
+                     }
+                  } else {
+                     // don't need transparent
+                     stbi__skip(s, 1); 
+                     g->transparent = -1; 
+                  }
+               } else {
+                  stbi__skip(s, len);
+                  break;
+               }
+            } 
+            while ((len = stbi__get8(s)) != 0) {
+               stbi__skip(s, len);
+            }
+            break;
+         }
+
+         case 0x3B: // gif stream termination code
+            return (stbi_uc *) s; // using '1' causes warning on some compilers
+
+         default:
+            return stbi__errpuc("unknown code", "Corrupt GIF");
+      }
+   }
+}
+
+static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp)
+{
+   if (stbi__gif_test(s)) {
+      int layers = 0; 
+      stbi_uc *u = 0;
+      stbi_uc *out = 0;
+      stbi_uc *two_back = 0; 
+      stbi__gif g;
+      int stride; 
+      memset(&g, 0, sizeof(g));
+      if (delays) {
+         *delays = 0; 
+      }
+
+      do {
+         u = stbi__gif_load_next(s, &g, comp, req_comp, two_back);
+         if (u == (stbi_uc *) s) u = 0;  // end of animated gif marker
+
+         if (u) {
+            *x = g.w;
+            *y = g.h;
+            ++layers; 
+            stride = g.w * g.h * 4; 
+         
+            if (out) {
+               out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); 
+               if (delays) {
+                  *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); 
+               }
+            } else {
+               out = (stbi_uc*)stbi__malloc( layers * stride ); 
+               if (delays) {
+                  *delays = (int*) stbi__malloc( layers * sizeof(int) ); 
+               }
+            }
+            memcpy( out + ((layers - 1) * stride), u, stride ); 
+            if (layers >= 2) {
+               two_back = out - 2 * stride; 
+            }
+
+            if (delays) {
+               (*delays)[layers - 1U] = g.delay; 
+            }
+         }
+      } while (u != 0); 
+
+      // free temp buffer; 
+      STBI_FREE(g.out); 
+      STBI_FREE(g.history); 
+      STBI_FREE(g.background); 
+
+      // do the final conversion after loading everything; 
+      if (req_comp && req_comp != 4)
+         out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h);
+
+      *z = layers; 
+      return out;
+   } else {
+      return stbi__errpuc("not GIF", "Image was not as a gif type."); 
+   }
+}
+
+static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   stbi_uc *u = 0;
+   stbi__gif g;
+   memset(&g, 0, sizeof(g));
+
+   u = stbi__gif_load_next(s, &g, comp, req_comp, 0);
+   if (u == (stbi_uc *) s) u = 0;  // end of animated gif marker
+   if (u) {
+      *x = g.w;
+      *y = g.h;
+
+      // moved conversion to after successful load so that the same
+      // can be done for multiple frames. 
+      if (req_comp && req_comp != 4)
+         u = stbi__convert_format(u, 4, req_comp, g.w, g.h);
+   }
+
+   // free buffers needed for multiple frame loading; 
+   STBI_FREE(g.history);
+   STBI_FREE(g.background); 
+
+   return u;
+}
+
+static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   return stbi__gif_info_raw(s,x,y,comp);
+}
+#endif
+
+// *************************************************************************************************
+// Radiance RGBE HDR loader
+// originally by Nicolas Schulz
+#ifndef STBI_NO_HDR
+static int stbi__hdr_test_core(stbi__context *s, const char *signature)
+{
+   int i;
+   for (i=0; signature[i]; ++i)
+      if (stbi__get8(s) != signature[i])
+          return 0;
+   stbi__rewind(s);
+   return 1;
+}
+
+static int stbi__hdr_test(stbi__context* s)
+{
+   int r = stbi__hdr_test_core(s, "#?RADIANCE\n");
+   stbi__rewind(s);
+   if(!r) {
+       r = stbi__hdr_test_core(s, "#?RGBE\n");
+       stbi__rewind(s);
+   }
+   return r;
+}
+
+#define STBI__HDR_BUFLEN  1024
+static char *stbi__hdr_gettoken(stbi__context *z, char *buffer)
+{
+   int len=0;
+   char c = '\0';
+
+   c = (char) stbi__get8(z);
+
+   while (!stbi__at_eof(z) && c != '\n') {
+      buffer[len++] = c;
+      if (len == STBI__HDR_BUFLEN-1) {
+         // flush to end of line
+         while (!stbi__at_eof(z) && stbi__get8(z) != '\n')
+            ;
+         break;
+      }
+      c = (char) stbi__get8(z);
+   }
+
+   buffer[len] = 0;
+   return buffer;
+}
+
+static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp)
+{
+   if ( input[3] != 0 ) {
+      float f1;
+      // Exponent
+      f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8));
+      if (req_comp <= 2)
+         output[0] = (input[0] + input[1] + input[2]) * f1 / 3;
+      else {
+         output[0] = input[0] * f1;
+         output[1] = input[1] * f1;
+         output[2] = input[2] * f1;
+      }
+      if (req_comp == 2) output[1] = 1;
+      if (req_comp == 4) output[3] = 1;
+   } else {
+      switch (req_comp) {
+         case 4: output[3] = 1; /* fallthrough */
+         case 3: output[0] = output[1] = output[2] = 0;
+                 break;
+         case 2: output[1] = 1; /* fallthrough */
+         case 1: output[0] = 0;
+                 break;
+      }
+   }
+}
+
+static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   char buffer[STBI__HDR_BUFLEN];
+   char *token;
+   int valid = 0;
+   int width, height;
+   stbi_uc *scanline;
+   float *hdr_data;
+   int len;
+   unsigned char count, value;
+   int i, j, k, c1,c2, z;
+   const char *headerToken;
+   STBI_NOTUSED(ri);
+
+   // Check identifier
+   headerToken = stbi__hdr_gettoken(s,buffer);
+   if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0)
+      return stbi__errpf("not HDR", "Corrupt HDR image");
+
+   // Parse header
+   for(;;) {
+      token = stbi__hdr_gettoken(s,buffer);
+      if (token[0] == 0) break;
+      if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1;
+   }
+
+   if (!valid)    return stbi__errpf("unsupported format", "Unsupported HDR format");
+
+   // Parse width and height
+   // can't use sscanf() if we're not using stdio!
+   token = stbi__hdr_gettoken(s,buffer);
+   if (strncmp(token, "-Y ", 3))  return stbi__errpf("unsupported data layout", "Unsupported HDR format");
+   token += 3;
+   height = (int) strtol(token, &token, 10);
+   while (*token == ' ') ++token;
+   if (strncmp(token, "+X ", 3))  return stbi__errpf("unsupported data layout", "Unsupported HDR format");
+   token += 3;
+   width = (int) strtol(token, NULL, 10);
+
+   *x = width;
+   *y = height;
+
+   if (comp) *comp = 3;
+   if (req_comp == 0) req_comp = 3;
+
+   if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0))
+      return stbi__errpf("too large", "HDR image is too large");
+
+   // Read data
+   hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0);
+   if (!hdr_data)
+      return stbi__errpf("outofmem", "Out of memory");
+
+   // Load image data
+   // image data is stored as some number of sca
+   if ( width < 8 || width >= 32768) {
+      // Read flat data
+      for (j=0; j < height; ++j) {
+         for (i=0; i < width; ++i) {
+            stbi_uc rgbe[4];
+           main_decode_loop:
+            stbi__getn(s, rgbe, 4);
+            stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp);
+         }
+      }
+   } else {
+      // Read RLE-encoded data
+      scanline = NULL;
+
+      for (j = 0; j < height; ++j) {
+         c1 = stbi__get8(s);
+         c2 = stbi__get8(s);
+         len = stbi__get8(s);
+         if (c1 != 2 || c2 != 2 || (len & 0x80)) {
+            // not run-length encoded, so we have to actually use THIS data as a decoded
+            // pixel (note this can't be a valid pixel--one of RGB must be >= 128)
+            stbi_uc rgbe[4];
+            rgbe[0] = (stbi_uc) c1;
+            rgbe[1] = (stbi_uc) c2;
+            rgbe[2] = (stbi_uc) len;
+            rgbe[3] = (stbi_uc) stbi__get8(s);
+            stbi__hdr_convert(hdr_data, rgbe, req_comp);
+            i = 1;
+            j = 0;
+            STBI_FREE(scanline);
+            goto main_decode_loop; // yes, this makes no sense
+         }
+         len <<= 8;
+         len |= stbi__get8(s);
+         if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); }
+         if (scanline == NULL) {
+            scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0);
+            if (!scanline) {
+               STBI_FREE(hdr_data);
+               return stbi__errpf("outofmem", "Out of memory");
+            }
+         }
+
+         for (k = 0; k < 4; ++k) {
+            int nleft;
+            i = 0;
+            while ((nleft = width - i) > 0) {
+               count = stbi__get8(s);
+               if (count > 128) {
+                  // Run
+                  value = stbi__get8(s);
+                  count -= 128;
+                  if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); }
+                  for (z = 0; z < count; ++z)
+                     scanline[i++ * 4 + k] = value;
+               } else {
+                  // Dump
+                  if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); }
+                  for (z = 0; z < count; ++z)
+                     scanline[i++ * 4 + k] = stbi__get8(s);
+               }
+            }
+         }
+         for (i=0; i < width; ++i)
+            stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp);
+      }
+      if (scanline)
+         STBI_FREE(scanline);
+   }
+
+   return hdr_data;
+}
+
+static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   char buffer[STBI__HDR_BUFLEN];
+   char *token;
+   int valid = 0;
+   int dummy;
+
+   if (!x) x = &dummy;
+   if (!y) y = &dummy;
+   if (!comp) comp = &dummy;
+
+   if (stbi__hdr_test(s) == 0) {
+       stbi__rewind( s );
+       return 0;
+   }
+
+   for(;;) {
+      token = stbi__hdr_gettoken(s,buffer);
+      if (token[0] == 0) break;
+      if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1;
+   }
+
+   if (!valid) {
+       stbi__rewind( s );
+       return 0;
+   }
+   token = stbi__hdr_gettoken(s,buffer);
+   if (strncmp(token, "-Y ", 3)) {
+       stbi__rewind( s );
+       return 0;
+   }
+   token += 3;
+   *y = (int) strtol(token, &token, 10);
+   while (*token == ' ') ++token;
+   if (strncmp(token, "+X ", 3)) {
+       stbi__rewind( s );
+       return 0;
+   }
+   token += 3;
+   *x = (int) strtol(token, NULL, 10);
+   *comp = 3;
+   return 1;
+}
+#endif // STBI_NO_HDR
+
+#ifndef STBI_NO_BMP
+static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   void *p;
+   stbi__bmp_data info;
+
+   info.all_a = 255;
+   p = stbi__bmp_parse_header(s, &info);
+   stbi__rewind( s );
+   if (p == NULL)
+      return 0;
+   if (x) *x = s->img_x;
+   if (y) *y = s->img_y;
+   if (comp) *comp = info.ma ? 4 : 3;
+   return 1;
+}
+#endif
+
+#ifndef STBI_NO_PSD
+static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   int channelCount, dummy, depth;
+   if (!x) x = &dummy;
+   if (!y) y = &dummy;
+   if (!comp) comp = &dummy;
+   if (stbi__get32be(s) != 0x38425053) {
+       stbi__rewind( s );
+       return 0;
+   }
+   if (stbi__get16be(s) != 1) {
+       stbi__rewind( s );
+       return 0;
+   }
+   stbi__skip(s, 6);
+   channelCount = stbi__get16be(s);
+   if (channelCount < 0 || channelCount > 16) {
+       stbi__rewind( s );
+       return 0;
+   }
+   *y = stbi__get32be(s);
+   *x = stbi__get32be(s);
+   depth = stbi__get16be(s);
+   if (depth != 8 && depth != 16) {
+       stbi__rewind( s );
+       return 0;
+   }
+   if (stbi__get16be(s) != 3) {
+       stbi__rewind( s );
+       return 0;
+   }
+   *comp = 4;
+   return 1;
+}
+
+static int stbi__psd_is16(stbi__context *s)
+{
+   int channelCount, depth;
+   if (stbi__get32be(s) != 0x38425053) {
+       stbi__rewind( s );
+       return 0;
+   }
+   if (stbi__get16be(s) != 1) {
+       stbi__rewind( s );
+       return 0;
+   }
+   stbi__skip(s, 6);
+   channelCount = stbi__get16be(s);
+   if (channelCount < 0 || channelCount > 16) {
+       stbi__rewind( s );
+       return 0;
+   }
+   (void) stbi__get32be(s);
+   (void) stbi__get32be(s);
+   depth = stbi__get16be(s);
+   if (depth != 16) {
+       stbi__rewind( s );
+       return 0;
+   }
+   return 1;
+}
+#endif
+
+#ifndef STBI_NO_PIC
+static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   int act_comp=0,num_packets=0,chained,dummy;
+   stbi__pic_packet packets[10];
+
+   if (!x) x = &dummy;
+   if (!y) y = &dummy;
+   if (!comp) comp = &dummy;
+
+   if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) {
+      stbi__rewind(s);
+      return 0;
+   }
+
+   stbi__skip(s, 88);
+
+   *x = stbi__get16be(s);
+   *y = stbi__get16be(s);
+   if (stbi__at_eof(s)) {
+      stbi__rewind( s);
+      return 0;
+   }
+   if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) {
+      stbi__rewind( s );
+      return 0;
+   }
+
+   stbi__skip(s, 8);
+
+   do {
+      stbi__pic_packet *packet;
+
+      if (num_packets==sizeof(packets)/sizeof(packets[0]))
+         return 0;
+
+      packet = &packets[num_packets++];
+      chained = stbi__get8(s);
+      packet->size    = stbi__get8(s);
+      packet->type    = stbi__get8(s);
+      packet->channel = stbi__get8(s);
+      act_comp |= packet->channel;
+
+      if (stbi__at_eof(s)) {
+          stbi__rewind( s );
+          return 0;
+      }
+      if (packet->size != 8) {
+          stbi__rewind( s );
+          return 0;
+      }
+   } while (chained);
+
+   *comp = (act_comp & 0x10 ? 4 : 3);
+
+   return 1;
+}
+#endif
+
+// *************************************************************************************************
+// Portable Gray Map and Portable Pixel Map loader
+// by Ken Miller
+//
+// PGM: http://netpbm.sourceforge.net/doc/pgm.html
+// PPM: http://netpbm.sourceforge.net/doc/ppm.html
+//
+// Known limitations:
+//    Does not support comments in the header section
+//    Does not support ASCII image data (formats P2 and P3)
+//    Does not support 16-bit-per-channel
+
+#ifndef STBI_NO_PNM
+
+static int      stbi__pnm_test(stbi__context *s)
+{
+   char p, t;
+   p = (char) stbi__get8(s);
+   t = (char) stbi__get8(s);
+   if (p != 'P' || (t != '5' && t != '6')) {
+       stbi__rewind( s );
+       return 0;
+   }
+   return 1;
+}
+
+static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri)
+{
+   stbi_uc *out;
+   STBI_NOTUSED(ri);
+
+   if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n))
+      return 0;
+
+   *x = s->img_x;
+   *y = s->img_y;
+   if (comp) *comp = s->img_n;
+
+   if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0))
+      return stbi__errpuc("too large", "PNM too large");
+
+   out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0);
+   if (!out) return stbi__errpuc("outofmem", "Out of memory");
+   stbi__getn(s, out, s->img_n * s->img_x * s->img_y);
+
+   if (req_comp && req_comp != s->img_n) {
+      out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y);
+      if (out == NULL) return out; // stbi__convert_format frees input on failure
+   }
+   return out;
+}
+
+static int      stbi__pnm_isspace(char c)
+{
+   return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r';
+}
+
+static void     stbi__pnm_skip_whitespace(stbi__context *s, char *c)
+{
+   for (;;) {
+      while (!stbi__at_eof(s) && stbi__pnm_isspace(*c))
+         *c = (char) stbi__get8(s);
+
+      if (stbi__at_eof(s) || *c != '#')
+         break;
+
+      while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' )
+         *c = (char) stbi__get8(s);
+   }
+}
+
+static int      stbi__pnm_isdigit(char c)
+{
+   return c >= '0' && c <= '9';
+}
+
+static int      stbi__pnm_getinteger(stbi__context *s, char *c)
+{
+   int value = 0;
+
+   while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) {
+      value = value*10 + (*c - '0');
+      *c = (char) stbi__get8(s);
+   }
+
+   return value;
+}
+
+static int      stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp)
+{
+   int maxv, dummy;
+   char c, p, t;
+
+   if (!x) x = &dummy;
+   if (!y) y = &dummy;
+   if (!comp) comp = &dummy;
+
+   stbi__rewind(s);
+
+   // Get identifier
+   p = (char) stbi__get8(s);
+   t = (char) stbi__get8(s);
+   if (p != 'P' || (t != '5' && t != '6')) {
+       stbi__rewind(s);
+       return 0;
+   }
+
+   *comp = (t == '6') ? 3 : 1;  // '5' is 1-component .pgm; '6' is 3-component .ppm
+
+   c = (char) stbi__get8(s);
+   stbi__pnm_skip_whitespace(s, &c);
+
+   *x = stbi__pnm_getinteger(s, &c); // read width
+   stbi__pnm_skip_whitespace(s, &c);
+
+   *y = stbi__pnm_getinteger(s, &c); // read height
+   stbi__pnm_skip_whitespace(s, &c);
+
+   maxv = stbi__pnm_getinteger(s, &c);  // read max value
+
+   if (maxv > 255)
+      return stbi__err("max value > 255", "PPM image not 8-bit");
+   else
+      return 1;
+}
+#endif
+
+static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp)
+{
+   #ifndef STBI_NO_JPEG
+   if (stbi__jpeg_info(s, x, y, comp)) return 1;
+   #endif
+
+   #ifndef STBI_NO_PNG
+   if (stbi__png_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_GIF
+   if (stbi__gif_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_BMP
+   if (stbi__bmp_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_PSD
+   if (stbi__psd_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_PIC
+   if (stbi__pic_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_PNM
+   if (stbi__pnm_info(s, x, y, comp))  return 1;
+   #endif
+
+   #ifndef STBI_NO_HDR
+   if (stbi__hdr_info(s, x, y, comp))  return 1;
+   #endif
+
+   // test tga last because it's a crappy test!
+   #ifndef STBI_NO_TGA
+   if (stbi__tga_info(s, x, y, comp))
+       return 1;
+   #endif
+   return stbi__err("unknown image type", "Image not of any known type, or corrupt");
+}
+
+static int stbi__is_16_main(stbi__context *s)
+{
+   #ifndef STBI_NO_PNG
+   if (stbi__png_is16(s))  return 1;
+   #endif
+
+   #ifndef STBI_NO_PSD
+   if (stbi__psd_is16(s))  return 1;
+   #endif
+
+   return 0;
+}
+
+#ifndef STBI_NO_STDIO
+STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp)
+{
+    FILE *f = stbi__fopen(filename, "rb");
+    int result;
+    if (!f) return stbi__err("can't fopen", "Unable to open file");
+    result = stbi_info_from_file(f, x, y, comp);
+    fclose(f);
+    return result;
+}
+
+STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp)
+{
+   int r;
+   stbi__context s;
+   long pos = ftell(f);
+   stbi__start_file(&s, f);
+   r = stbi__info_main(&s,x,y,comp);
+   fseek(f,pos,SEEK_SET);
+   return r;
+}
+
+STBIDEF int stbi_is_16_bit(char const *filename)
+{
+    FILE *f = stbi__fopen(filename, "rb");
+    int result;
+    if (!f) return stbi__err("can't fopen", "Unable to open file");
+    result = stbi_is_16_bit_from_file(f);
+    fclose(f);
+    return result;
+}
+
+STBIDEF int stbi_is_16_bit_from_file(FILE *f)
+{
+   int r;
+   stbi__context s;
+   long pos = ftell(f);
+   stbi__start_file(&s, f);
+   r = stbi__is_16_main(&s);
+   fseek(f,pos,SEEK_SET);
+   return r;
+}
+#endif // !STBI_NO_STDIO
+
+STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp)
+{
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__info_main(&s,x,y,comp);
+}
+
+STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp)
+{
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user);
+   return stbi__info_main(&s,x,y,comp);
+}
+
+STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len)
+{
+   stbi__context s;
+   stbi__start_mem(&s,buffer,len);
+   return stbi__is_16_main(&s);
+}
+
+STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user)
+{
+   stbi__context s;
+   stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user);
+   return stbi__is_16_main(&s);
+}
+
+#endif // STB_IMAGE_IMPLEMENTATION
+
+/*
+   revision history:
+      2.19  (2018-02-11) fix warning
+      2.18  (2018-01-30) fix warnings
+      2.17  (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug
+                         1-bit BMP
+                         *_is_16_bit api
+                         avoid warnings
+      2.16  (2017-07-23) all functions have 16-bit variants;
+                         STBI_NO_STDIO works again;
+                         compilation fixes;
+                         fix rounding in unpremultiply;
+                         optimize vertical flip;
+                         disable raw_len validation;
+                         documentation fixes
+      2.15  (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode;
+                         warning fixes; disable run-time SSE detection on gcc;
+                         uniform handling of optional "return" values;
+                         thread-safe initialization of zlib tables
+      2.14  (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs
+      2.13  (2016-11-29) add 16-bit API, only supported for PNG right now
+      2.12  (2016-04-02) fix typo in 2.11 PSD fix that caused crashes
+      2.11  (2016-04-02) allocate large structures on the stack
+                         remove white matting for transparent PSD
+                         fix reported channel count for PNG & BMP
+                         re-enable SSE2 in non-gcc 64-bit
+                         support RGB-formatted JPEG
+                         read 16-bit PNGs (only as 8-bit)
+      2.10  (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED
+      2.09  (2016-01-16) allow comments in PNM files
+                         16-bit-per-pixel TGA (not bit-per-component)
+                         info() for TGA could break due to .hdr handling
+                         info() for BMP to shares code instead of sloppy parse
+                         can use STBI_REALLOC_SIZED if allocator doesn't support realloc
+                         code cleanup
+      2.08  (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA
+      2.07  (2015-09-13) fix compiler warnings
+                         partial animated GIF support
+                         limited 16-bpc PSD support
+                         #ifdef unused functions
+                         bug with < 92 byte PIC,PNM,HDR,TGA
+      2.06  (2015-04-19) fix bug where PSD returns wrong '*comp' value
+      2.05  (2015-04-19) fix bug in progressive JPEG handling, fix warning
+      2.04  (2015-04-15) try to re-enable SIMD on MinGW 64-bit
+      2.03  (2015-04-12) extra corruption checking (mmozeiko)
+                         stbi_set_flip_vertically_on_load (nguillemot)
+                         fix NEON support; fix mingw support
+      2.02  (2015-01-19) fix incorrect assert, fix warning
+      2.01  (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2
+      2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG
+      2.00  (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg)
+                         progressive JPEG (stb)
+                         PGM/PPM support (Ken Miller)
+                         STBI_MALLOC,STBI_REALLOC,STBI_FREE
+                         GIF bugfix -- seemingly never worked
+                         STBI_NO_*, STBI_ONLY_*
+      1.48  (2014-12-14) fix incorrectly-named assert()
+      1.47  (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb)
+                         optimize PNG (ryg)
+                         fix bug in interlaced PNG with user-specified channel count (stb)
+      1.46  (2014-08-26)
+              fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG
+      1.45  (2014-08-16)
+              fix MSVC-ARM internal compiler error by wrapping malloc
+      1.44  (2014-08-07)
+              various warning fixes from Ronny Chevalier
+      1.43  (2014-07-15)
+              fix MSVC-only compiler problem in code changed in 1.42
+      1.42  (2014-07-09)
+              don't define _CRT_SECURE_NO_WARNINGS (affects user code)
+              fixes to stbi__cleanup_jpeg path
+              added STBI_ASSERT to avoid requiring assert.h
+      1.41  (2014-06-25)
+              fix search&replace from 1.36 that messed up comments/error messages
+      1.40  (2014-06-22)
+              fix gcc struct-initialization warning
+      1.39  (2014-06-15)
+              fix to TGA optimization when req_comp != number of components in TGA;
+              fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite)
+              add support for BMP version 5 (more ignored fields)
+      1.38  (2014-06-06)
+              suppress MSVC warnings on integer casts truncating values
+              fix accidental rename of 'skip' field of I/O
+      1.37  (2014-06-04)
+              remove duplicate typedef
+      1.36  (2014-06-03)
+              convert to header file single-file library
+              if de-iphone isn't set, load iphone images color-swapped instead of returning NULL
+      1.35  (2014-05-27)
+              various warnings
+              fix broken STBI_SIMD path
+              fix bug where stbi_load_from_file no longer left file pointer in correct place
+              fix broken non-easy path for 32-bit BMP (possibly never used)
+              TGA optimization by Arseny Kapoulkine
+      1.34  (unknown)
+              use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case
+      1.33  (2011-07-14)
+              make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements
+      1.32  (2011-07-13)
+              support for "info" function for all supported filetypes (SpartanJ)
+      1.31  (2011-06-20)
+              a few more leak fixes, bug in PNG handling (SpartanJ)
+      1.30  (2011-06-11)
+              added ability to load files via callbacks to accomidate custom input streams (Ben Wenger)
+              removed deprecated format-specific test/load functions
+              removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway
+              error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha)
+              fix inefficiency in decoding 32-bit BMP (David Woo)
+      1.29  (2010-08-16)
+              various warning fixes from Aurelien Pocheville
+      1.28  (2010-08-01)
+              fix bug in GIF palette transparency (SpartanJ)
+      1.27  (2010-08-01)
+              cast-to-stbi_uc to fix warnings
+      1.26  (2010-07-24)
+              fix bug in file buffering for PNG reported by SpartanJ
+      1.25  (2010-07-17)
+              refix trans_data warning (Won Chun)
+      1.24  (2010-07-12)
+              perf improvements reading from files on platforms with lock-heavy fgetc()
+              minor perf improvements for jpeg
+              deprecated type-specific functions so we'll get feedback if they're needed
+              attempt to fix trans_data warning (Won Chun)
+      1.23    fixed bug in iPhone support
+      1.22  (2010-07-10)
+              removed image *writing* support
+              stbi_info support from Jetro Lauha
+              GIF support from Jean-Marc Lienher
+              iPhone PNG-extensions from James Brown
+              warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva)
+      1.21    fix use of 'stbi_uc' in header (reported by jon blow)
+      1.20    added support for Softimage PIC, by Tom Seddon
+      1.19    bug in interlaced PNG corruption check (found by ryg)
+      1.18  (2008-08-02)
+              fix a threading bug (local mutable static)
+      1.17    support interlaced PNG
+      1.16    major bugfix - stbi__convert_format converted one too many pixels
+      1.15    initialize some fields for thread safety
+      1.14    fix threadsafe conversion bug
+              header-file-only version (#define STBI_HEADER_FILE_ONLY before including)
+      1.13    threadsafe
+      1.12    const qualifiers in the API
+      1.11    Support installable IDCT, colorspace conversion routines
+      1.10    Fixes for 64-bit (don't use "unsigned long")
+              optimized upsampling by Fabian "ryg" Giesen
+      1.09    Fix format-conversion for PSD code (bad global variables!)
+      1.08    Thatcher Ulrich's PSD code integrated by Nicolas Schulz
+      1.07    attempt to fix C++ warning/errors again
+      1.06    attempt to fix C++ warning/errors again
+      1.05    fix TGA loading to return correct *comp and use good luminance calc
+      1.04    default float alpha is 1, not 255; use 'void *' for stbi_image_free
+      1.03    bugfixes to STBI_NO_STDIO, STBI_NO_HDR
+      1.02    support for (subset of) HDR files, float interface for preferred access to them
+      1.01    fix bug: possible bug in handling right-side up bmps... not sure
+              fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all
+      1.00    interface to zlib that skips zlib header
+      0.99    correct handling of alpha in palette
+      0.98    TGA loader by lonesock; dynamically add loaders (untested)
+      0.97    jpeg errors on too large a file; also catch another malloc failure
+      0.96    fix detection of invalid v value - particleman@mollyrocket forum
+      0.95    during header scan, seek to markers in case of padding
+      0.94    STBI_NO_STDIO to disable stdio usage; rename all #defines the same
+      0.93    handle jpegtran output; verbose errors
+      0.92    read 4,8,16,24,32-bit BMP files of several formats
+      0.91    output 24-bit Windows 3.0 BMP files
+      0.90    fix a few more warnings; bump version number to approach 1.0
+      0.61    bugfixes due to Marc LeBlanc, Christopher Lloyd
+      0.60    fix compiling as c++
+      0.59    fix warnings: merge Dave Moore's -Wall fixes
+      0.58    fix bug: zlib uncompressed mode len/nlen was wrong endian
+      0.57    fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available
+      0.56    fix bug: zlib uncompressed mode len vs. nlen
+      0.55    fix bug: restart_interval not initialized to 0
+      0.54    allow NULL for 'int *comp'
+      0.53    fix bug in png 3->4; speedup png decoding
+      0.52    png handles req_comp=3,4 directly; minor cleanup; jpeg comments
+      0.51    obey req_comp requests, 1-component jpegs return as 1-component,
+              on 'test' only check type, not whether we support this variant
+      0.50  (2006-11-19)
+              first released version
+*/
+
+
+/*
+------------------------------------------------------------------------------
+This software is available under 2 licenses -- choose whichever you prefer.
+------------------------------------------------------------------------------
+ALTERNATIVE A - MIT License
+Copyright (c) 2017 Sean Barrett
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+------------------------------------------------------------------------------
+ALTERNATIVE B - Public Domain (www.unlicense.org)
+This is free and unencumbered software released into the public domain.
+Anyone is free to copy, modify, publish, use, compile, sell, or distribute this
+software, either in source code form or as a compiled binary, for any purpose,
+commercial or non-commercial, and by any means.
+In jurisdictions that recognize copyright laws, the author or authors of this
+software dedicate any and all copyright interest in the software to the public
+domain. We make this dedication for the benefit of the public at large and to
+the detriment of our heirs and successors. We intend this dedication to be an
+overt act of relinquishment in perpetuity of all present and future rights to
+this software under copyright law.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+------------------------------------------------------------------------------
+*/
diff --git a/hanzi_detection/src/stb_image_write.h b/hanzi_detection/src/stb_image_write.h
new file mode 100755
index 0000000..c05e958
--- /dev/null
+++ b/hanzi_detection/src/stb_image_write.h
@@ -0,0 +1,1568 @@
+/* stb_image_write - v1.09 - public domain - http://nothings.org/stb/stb_image_write.h
+   writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015
+                                     no warranty implied; use at your own risk
+
+   Before #including,
+
+       #define STB_IMAGE_WRITE_IMPLEMENTATION
+
+   in the file that you want to have the implementation.
+
+   Will probably not work correctly with strict-aliasing optimizations.
+
+   If using a modern Microsoft Compiler, non-safe versions of CRT calls may cause 
+   compilation warnings or even errors. To avoid this, also before #including,
+
+       #define STBI_MSC_SECURE_CRT
+
+ABOUT:
+
+   This header file is a library for writing images to C stdio. It could be
+   adapted to write to memory or a general streaming interface; let me know.
+
+   The PNG output is not optimal; it is 20-50% larger than the file
+   written by a decent optimizing implementation; though providing a custom
+   zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that.
+   This library is designed for source code compactness and simplicity,
+   not optimal image file size or run-time performance.
+
+BUILDING:
+
+   You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h.
+   You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace
+   malloc,realloc,free.
+   You can #define STBIW_MEMMOVE() to replace memmove()
+   You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function
+   for PNG compression (instead of the builtin one), it must have the following signature:
+   unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality);
+   The returned data will be freed with STBIW_FREE() (free() by default),
+   so it must be heap allocated with STBIW_MALLOC() (malloc() by default),
+
+USAGE:
+
+   There are five functions, one for each image file format:
+
+     int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes);
+     int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data);
+     int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data);
+     int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality);
+     int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data);
+
+     void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically
+
+   There are also five equivalent functions that use an arbitrary write function. You are
+   expected to open/close your file-equivalent before and after calling these:
+
+     int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data, int stride_in_bytes);
+     int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data);
+     int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data);
+     int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data);
+     int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality);
+
+   where the callback is:
+      void stbi_write_func(void *context, void *data, int size);
+
+   You can configure it with these global variables:
+      int stbi_write_tga_with_rle;             // defaults to true; set to 0 to disable RLE
+      int stbi_write_png_compression_level;    // defaults to 8; set to higher for more compression
+      int stbi_write_force_png_filter;         // defaults to -1; set to 0..5 to force a filter mode
+
+
+   You can define STBI_WRITE_NO_STDIO to disable the file variant of these
+   functions, so the library will not use stdio.h at all. However, this will
+   also disable HDR writing, because it requires stdio for formatted output.
+
+   Each function returns 0 on failure and non-0 on success.
+
+   The functions create an image file defined by the parameters. The image
+   is a rectangle of pixels stored from left-to-right, top-to-bottom.
+   Each pixel contains 'comp' channels of data stored interleaved with 8-bits
+   per channel, in the following order: 1=Y, 2=YA, 3=RGB, 4=RGBA. (Y is
+   monochrome color.) The rectangle is 'w' pixels wide and 'h' pixels tall.
+   The *data pointer points to the first byte of the top-left-most pixel.
+   For PNG, "stride_in_bytes" is the distance in bytes from the first byte of
+   a row of pixels to the first byte of the next row of pixels.
+
+   PNG creates output files with the same number of components as the input.
+   The BMP format expands Y to RGB in the file format and does not
+   output alpha.
+
+   PNG supports writing rectangles of data even when the bytes storing rows of
+   data are not consecutive in memory (e.g. sub-rectangles of a larger image),
+   by supplying the stride between the beginning of adjacent rows. The other
+   formats do not. (Thus you cannot write a native-format BMP through the BMP
+   writer, both because it is in BGR order and because it may have padding
+   at the end of the line.)
+
+   PNG allows you to set the deflate compression level by setting the global
+   variable 'stbi_write_png_compression_level' (it defaults to 8).
+
+   HDR expects linear float data. Since the format is always 32-bit rgb(e)
+   data, alpha (if provided) is discarded, and for monochrome data it is
+   replicated across all three channels.
+
+   TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed
+   data, set the global variable 'stbi_write_tga_with_rle' to 0.
+   
+   JPEG does ignore alpha channels in input data; quality is between 1 and 100.
+   Higher quality looks better but results in a bigger image.
+   JPEG baseline (no JPEG progressive).
+
+CREDITS:
+
+
+   Sean Barrett           -    PNG/BMP/TGA 
+   Baldur Karlsson        -    HDR
+   Jean-Sebastien Guay    -    TGA monochrome
+   Tim Kelsey             -    misc enhancements
+   Alan Hickman           -    TGA RLE
+   Emmanuel Julien        -    initial file IO callback implementation
+   Jon Olick              -    original jo_jpeg.cpp code
+   Daniel Gibson          -    integrate JPEG, allow external zlib
+   Aarni Koskela          -    allow choosing PNG filter
+
+   bugfixes:
+      github:Chribba
+      Guillaume Chereau
+      github:jry2
+      github:romigrou
+      Sergio Gonzalez
+      Jonas Karlsson
+      Filip Wasil
+      Thatcher Ulrich
+      github:poppolopoppo
+      Patrick Boettcher
+      github:xeekworx
+      Cap Petschulat
+      Simon Rodriguez
+      Ivan Tikhonov
+      github:ignotion
+      Adam Schackart
+
+LICENSE
+
+  See end of file for license information.
+
+*/
+
+#ifndef INCLUDE_STB_IMAGE_WRITE_H
+#define INCLUDE_STB_IMAGE_WRITE_H
+
+// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline'
+#ifndef STBIWDEF
+#ifdef STB_IMAGE_WRITE_STATIC
+#define STBIWDEF  static
+#else
+#ifdef __cplusplus
+#define STBIWDEF  extern "C"
+#else
+#define STBIWDEF  extern
+#endif
+#endif
+#endif
+
+#ifndef STB_IMAGE_WRITE_STATIC  // C++ forbids static forward declarations
+extern int stbi_write_tga_with_rle;
+extern int stbi_write_png_compression_level;
+extern int stbi_write_force_png_filter;
+#endif
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void  *data, int stride_in_bytes);
+STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void  *data);
+STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void  *data);
+STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data);
+STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void  *data, int quality);
+#endif
+
+typedef void stbi_write_func(void *context, void *data, int size);
+
+STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data, int stride_in_bytes);
+STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data);
+STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void  *data);
+STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data);
+STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void  *data, int quality);
+
+STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean);
+
+#endif//INCLUDE_STB_IMAGE_WRITE_H
+
+#ifdef STB_IMAGE_WRITE_IMPLEMENTATION
+
+#ifdef _WIN32
+   #ifndef _CRT_SECURE_NO_WARNINGS
+   #define _CRT_SECURE_NO_WARNINGS
+   #endif
+   #ifndef _CRT_NONSTDC_NO_DEPRECATE
+   #define _CRT_NONSTDC_NO_DEPRECATE
+   #endif
+#endif
+
+#ifndef STBI_WRITE_NO_STDIO
+#include <stdio.h>
+#endif // STBI_WRITE_NO_STDIO
+
+#include <stdarg.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+
+#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED))
+// ok
+#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED)
+// ok
+#else
+#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)."
+#endif
+
+#ifndef STBIW_MALLOC
+#define STBIW_MALLOC(sz)        malloc(sz)
+#define STBIW_REALLOC(p,newsz)  realloc(p,newsz)
+#define STBIW_FREE(p)           free(p)
+#endif
+
+#ifndef STBIW_REALLOC_SIZED
+#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz)
+#endif
+
+
+#ifndef STBIW_MEMMOVE
+#define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz)
+#endif
+
+
+#ifndef STBIW_ASSERT
+#include <assert.h>
+#define STBIW_ASSERT(x) assert(x)
+#endif
+
+#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff)
+
+#ifdef STB_IMAGE_WRITE_STATIC
+static int stbi__flip_vertically_on_write=0;
+static int stbi_write_png_compression_level = 8;
+static int stbi_write_tga_with_rle = 1;
+static int stbi_write_force_png_filter = -1;
+#else
+int stbi_write_png_compression_level = 8;
+int stbi__flip_vertically_on_write=0;
+int stbi_write_tga_with_rle = 1;
+int stbi_write_force_png_filter = -1;
+#endif
+
+STBIWDEF void stbi_flip_vertically_on_write(int flag)
+{
+   stbi__flip_vertically_on_write = flag;
+}
+
+typedef struct
+{
+   stbi_write_func *func;
+   void *context;
+} stbi__write_context;
+
+// initialize a callback-based context
+static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context)
+{
+   s->func    = c;
+   s->context = context;
+}
+
+#ifndef STBI_WRITE_NO_STDIO
+
+static void stbi__stdio_write(void *context, void *data, int size)
+{
+   fwrite(data,1,size,(FILE*) context);
+}
+
+static int stbi__start_write_file(stbi__write_context *s, const char *filename)
+{
+   FILE *f;
+#ifdef STBI_MSC_SECURE_CRT
+   if (fopen_s(&f, filename, "wb"))
+      f = NULL;
+#else
+   f = fopen(filename, "wb");
+#endif
+   stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f);
+   return f != NULL;
+}
+
+static void stbi__end_write_file(stbi__write_context *s)
+{
+   fclose((FILE *)s->context);
+}
+
+#endif // !STBI_WRITE_NO_STDIO
+
+typedef unsigned int stbiw_uint32;
+typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1];
+
+static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v)
+{
+   while (*fmt) {
+      switch (*fmt++) {
+         case ' ': break;
+         case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int));
+                     s->func(s->context,&x,1);
+                     break; }
+         case '2': { int x = va_arg(v,int);
+                     unsigned char b[2];
+                     b[0] = STBIW_UCHAR(x);
+                     b[1] = STBIW_UCHAR(x>>8);
+                     s->func(s->context,b,2);
+                     break; }
+         case '4': { stbiw_uint32 x = va_arg(v,int);
+                     unsigned char b[4];
+                     b[0]=STBIW_UCHAR(x);
+                     b[1]=STBIW_UCHAR(x>>8);
+                     b[2]=STBIW_UCHAR(x>>16);
+                     b[3]=STBIW_UCHAR(x>>24);
+                     s->func(s->context,b,4);
+                     break; }
+         default:
+            STBIW_ASSERT(0);
+            return;
+      }
+   }
+}
+
+static void stbiw__writef(stbi__write_context *s, const char *fmt, ...)
+{
+   va_list v;
+   va_start(v, fmt);
+   stbiw__writefv(s, fmt, v);
+   va_end(v);
+}
+
+static void stbiw__putc(stbi__write_context *s, unsigned char c)
+{
+   s->func(s->context, &c, 1);
+}
+
+static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c)
+{
+   unsigned char arr[3];
+   arr[0] = a, arr[1] = b, arr[2] = c;
+   s->func(s->context, arr, 3);
+}
+
+static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d)
+{
+   unsigned char bg[3] = { 255, 0, 255}, px[3];
+   int k;
+
+   if (write_alpha < 0)
+      s->func(s->context, &d[comp - 1], 1);
+
+   switch (comp) {
+      case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case
+      case 1:
+         if (expand_mono)
+            stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp
+         else
+            s->func(s->context, d, 1);  // monochrome TGA
+         break;
+      case 4:
+         if (!write_alpha) {
+            // composite against pink background
+            for (k = 0; k < 3; ++k)
+               px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255;
+            stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]);
+            break;
+         }
+         /* FALLTHROUGH */
+      case 3:
+         stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]);
+         break;
+   }
+   if (write_alpha > 0)
+      s->func(s->context, &d[comp - 1], 1);
+}
+
+static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono)
+{
+   stbiw_uint32 zero = 0;
+   int i,j, j_end;
+
+   if (y <= 0)
+      return;
+
+   if (stbi__flip_vertically_on_write)
+      vdir *= -1;
+
+   if (vdir < 0)
+      j_end = -1, j = y-1;
+   else
+      j_end =  y, j = 0;
+
+   for (; j != j_end; j += vdir) {
+      for (i=0; i < x; ++i) {
+         unsigned char *d = (unsigned char *) data + (j*x+i)*comp;
+         stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d);
+      }
+      s->func(s->context, &zero, scanline_pad);
+   }
+}
+
+static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...)
+{
+   if (y < 0 || x < 0) {
+      return 0;
+   } else {
+      va_list v;
+      va_start(v, fmt);
+      stbiw__writefv(s, fmt, v);
+      va_end(v);
+      stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono);
+      return 1;
+   }
+}
+
+static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data)
+{
+   int pad = (-x*3) & 3;
+   return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad,
+           "11 4 22 4" "4 44 22 444444",
+           'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40,  // file header
+            40, x,y, 1,24, 0,0,0,0,0,0);             // bitmap header
+}
+
+STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data)
+{
+   stbi__write_context s;
+   stbi__start_write_callbacks(&s, func, context);
+   return stbi_write_bmp_core(&s, x, y, comp, data);
+}
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data)
+{
+   stbi__write_context s;
+   if (stbi__start_write_file(&s,filename)) {
+      int r = stbi_write_bmp_core(&s, x, y, comp, data);
+      stbi__end_write_file(&s);
+      return r;
+   } else
+      return 0;
+}
+#endif //!STBI_WRITE_NO_STDIO
+
+static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data)
+{
+   int has_alpha = (comp == 2 || comp == 4);
+   int colorbytes = has_alpha ? comp-1 : comp;
+   int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3
+
+   if (y < 0 || x < 0)
+      return 0;
+
+   if (!stbi_write_tga_with_rle) {
+      return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0,
+         "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8);
+   } else {
+      int i,j,k;
+      int jend, jdir;
+
+      stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8);
+
+      if (stbi__flip_vertically_on_write) {
+         j = 0;
+         jend = y;
+         jdir = 1;
+      } else {
+         j = y-1;
+         jend = -1;
+         jdir = -1;
+      }
+      for (; j != jend; j += jdir) {
+         unsigned char *row = (unsigned char *) data + j * x * comp;
+         int len;
+
+         for (i = 0; i < x; i += len) {
+            unsigned char *begin = row + i * comp;
+            int diff = 1;
+            len = 1;
+
+            if (i < x - 1) {
+               ++len;
+               diff = memcmp(begin, row + (i + 1) * comp, comp);
+               if (diff) {
+                  const unsigned char *prev = begin;
+                  for (k = i + 2; k < x && len < 128; ++k) {
+                     if (memcmp(prev, row + k * comp, comp)) {
+                        prev += comp;
+                        ++len;
+                     } else {
+                        --len;
+                        break;
+                     }
+                  }
+               } else {
+                  for (k = i + 2; k < x && len < 128; ++k) {
+                     if (!memcmp(begin, row + k * comp, comp)) {
+                        ++len;
+                     } else {
+                        break;
+                     }
+                  }
+               }
+            }
+
+            if (diff) {
+               unsigned char header = STBIW_UCHAR(len - 1);
+               s->func(s->context, &header, 1);
+               for (k = 0; k < len; ++k) {
+                  stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp);
+               }
+            } else {
+               unsigned char header = STBIW_UCHAR(len - 129);
+               s->func(s->context, &header, 1);
+               stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin);
+            }
+         }
+      }
+   }
+   return 1;
+}
+
+STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data)
+{
+   stbi__write_context s;
+   stbi__start_write_callbacks(&s, func, context);
+   return stbi_write_tga_core(&s, x, y, comp, (void *) data);
+}
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data)
+{
+   stbi__write_context s;
+   if (stbi__start_write_file(&s,filename)) {
+      int r = stbi_write_tga_core(&s, x, y, comp, (void *) data);
+      stbi__end_write_file(&s);
+      return r;
+   } else
+      return 0;
+}
+#endif
+
+// *************************************************************************************************
+// Radiance RGBE HDR writer
+// by Baldur Karlsson
+
+#define stbiw__max(a, b)  ((a) > (b) ? (a) : (b))
+
+void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear)
+{
+   int exponent;
+   float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2]));
+
+   if (maxcomp < 1e-32f) {
+      rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0;
+   } else {
+      float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp;
+
+      rgbe[0] = (unsigned char)(linear[0] * normalize);
+      rgbe[1] = (unsigned char)(linear[1] * normalize);
+      rgbe[2] = (unsigned char)(linear[2] * normalize);
+      rgbe[3] = (unsigned char)(exponent + 128);
+   }
+}
+
+void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte)
+{
+   unsigned char lengthbyte = STBIW_UCHAR(length+128);
+   STBIW_ASSERT(length+128 <= 255);
+   s->func(s->context, &lengthbyte, 1);
+   s->func(s->context, &databyte, 1);
+}
+
+void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data)
+{
+   unsigned char lengthbyte = STBIW_UCHAR(length);
+   STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code
+   s->func(s->context, &lengthbyte, 1);
+   s->func(s->context, data, length);
+}
+
+void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline)
+{
+   unsigned char scanlineheader[4] = { 2, 2, 0, 0 };
+   unsigned char rgbe[4];
+   float linear[3];
+   int x;
+
+   scanlineheader[2] = (width&0xff00)>>8;
+   scanlineheader[3] = (width&0x00ff);
+
+   /* skip RLE for images too small or large */
+   if (width < 8 || width >= 32768) {
+      for (x=0; x < width; x++) {
+         switch (ncomp) {
+            case 4: /* fallthrough */
+            case 3: linear[2] = scanline[x*ncomp + 2];
+                    linear[1] = scanline[x*ncomp + 1];
+                    linear[0] = scanline[x*ncomp + 0];
+                    break;
+            default:
+                    linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0];
+                    break;
+         }
+         stbiw__linear_to_rgbe(rgbe, linear);
+         s->func(s->context, rgbe, 4);
+      }
+   } else {
+      int c,r;
+      /* encode into scratch buffer */
+      for (x=0; x < width; x++) {
+         switch(ncomp) {
+            case 4: /* fallthrough */
+            case 3: linear[2] = scanline[x*ncomp + 2];
+                    linear[1] = scanline[x*ncomp + 1];
+                    linear[0] = scanline[x*ncomp + 0];
+                    break;
+            default:
+                    linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0];
+                    break;
+         }
+         stbiw__linear_to_rgbe(rgbe, linear);
+         scratch[x + width*0] = rgbe[0];
+         scratch[x + width*1] = rgbe[1];
+         scratch[x + width*2] = rgbe[2];
+         scratch[x + width*3] = rgbe[3];
+      }
+
+      s->func(s->context, scanlineheader, 4);
+
+      /* RLE each component separately */
+      for (c=0; c < 4; c++) {
+         unsigned char *comp = &scratch[width*c];
+
+         x = 0;
+         while (x < width) {
+            // find first run
+            r = x;
+            while (r+2 < width) {
+               if (comp[r] == comp[r+1] && comp[r] == comp[r+2])
+                  break;
+               ++r;
+            }
+            if (r+2 >= width)
+               r = width;
+            // dump up to first run
+            while (x < r) {
+               int len = r-x;
+               if (len > 128) len = 128;
+               stbiw__write_dump_data(s, len, &comp[x]);
+               x += len;
+            }
+            // if there's a run, output it
+            if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd
+               // find next byte after run
+               while (r < width && comp[r] == comp[x])
+                  ++r;
+               // output run up to r
+               while (x < r) {
+                  int len = r-x;
+                  if (len > 127) len = 127;
+                  stbiw__write_run_data(s, len, comp[x]);
+                  x += len;
+               }
+            }
+         }
+      }
+   }
+}
+
+static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data)
+{
+   if (y <= 0 || x <= 0 || data == NULL)
+      return 0;
+   else {
+      // Each component is stored separately. Allocate scratch space for full output scanline.
+      unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4);
+      int i, len;
+      char buffer[128];
+      char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n";
+      s->func(s->context, header, sizeof(header)-1);
+
+#ifdef STBI_MSC_SECURE_CRT
+      len = sprintf_s(buffer, "EXPOSURE=          1.0000000000000\n\n-Y %d +X %d\n", y, x);
+#else
+      len = sprintf(buffer, "EXPOSURE=          1.0000000000000\n\n-Y %d +X %d\n", y, x);
+#endif
+      s->func(s->context, buffer, len);
+
+      for(i=0; i < y; i++)
+         stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)*x);
+      STBIW_FREE(scratch);
+      return 1;
+   }
+}
+
+STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data)
+{
+   stbi__write_context s;
+   stbi__start_write_callbacks(&s, func, context);
+   return stbi_write_hdr_core(&s, x, y, comp, (float *) data);
+}
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data)
+{
+   stbi__write_context s;
+   if (stbi__start_write_file(&s,filename)) {
+      int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data);
+      stbi__end_write_file(&s);
+      return r;
+   } else
+      return 0;
+}
+#endif // STBI_WRITE_NO_STDIO
+
+
+//////////////////////////////////////////////////////////////////////////////
+//
+// PNG writer
+//
+
+#ifndef STBIW_ZLIB_COMPRESS
+// stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size()
+#define stbiw__sbraw(a) ((int *) (a) - 2)
+#define stbiw__sbm(a)   stbiw__sbraw(a)[0]
+#define stbiw__sbn(a)   stbiw__sbraw(a)[1]
+
+#define stbiw__sbneedgrow(a,n)  ((a)==0 || stbiw__sbn(a)+n >= stbiw__sbm(a))
+#define stbiw__sbmaybegrow(a,n) (stbiw__sbneedgrow(a,(n)) ? stbiw__sbgrow(a,n) : 0)
+#define stbiw__sbgrow(a,n)  stbiw__sbgrowf((void **) &(a), (n), sizeof(*(a)))
+
+#define stbiw__sbpush(a, v)      (stbiw__sbmaybegrow(a,1), (a)[stbiw__sbn(a)++] = (v))
+#define stbiw__sbcount(a)        ((a) ? stbiw__sbn(a) : 0)
+#define stbiw__sbfree(a)         ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0)
+
+static void *stbiw__sbgrowf(void **arr, int increment, int itemsize)
+{
+   int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1;
+   void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2);
+   STBIW_ASSERT(p);
+   if (p) {
+      if (!*arr) ((int *) p)[1] = 0;
+      *arr = (void *) ((int *) p + 2);
+      stbiw__sbm(*arr) = m;
+   }
+   return *arr;
+}
+
+static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount)
+{
+   while (*bitcount >= 8) {
+      stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer));
+      *bitbuffer >>= 8;
+      *bitcount -= 8;
+   }
+   return data;
+}
+
+static int stbiw__zlib_bitrev(int code, int codebits)
+{
+   int res=0;
+   while (codebits--) {
+      res = (res << 1) | (code & 1);
+      code >>= 1;
+   }
+   return res;
+}
+
+static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit)
+{
+   int i;
+   for (i=0; i < limit && i < 258; ++i)
+      if (a[i] != b[i]) break;
+   return i;
+}
+
+static unsigned int stbiw__zhash(unsigned char *data)
+{
+   stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16);
+   hash ^= hash << 3;
+   hash += hash >> 5;
+   hash ^= hash << 4;
+   hash += hash >> 17;
+   hash ^= hash << 25;
+   hash += hash >> 6;
+   return hash;
+}
+
+#define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount))
+#define stbiw__zlib_add(code,codebits) \
+      (bitbuf |= (code) << bitcount, bitcount += (codebits), stbiw__zlib_flush())
+#define stbiw__zlib_huffa(b,c)  stbiw__zlib_add(stbiw__zlib_bitrev(b,c),c)
+// default huffman tables
+#define stbiw__zlib_huff1(n)  stbiw__zlib_huffa(0x30 + (n), 8)
+#define stbiw__zlib_huff2(n)  stbiw__zlib_huffa(0x190 + (n)-144, 9)
+#define stbiw__zlib_huff3(n)  stbiw__zlib_huffa(0 + (n)-256,7)
+#define stbiw__zlib_huff4(n)  stbiw__zlib_huffa(0xc0 + (n)-280,8)
+#define stbiw__zlib_huff(n)  ((n) <= 143 ? stbiw__zlib_huff1(n) : (n) <= 255 ? stbiw__zlib_huff2(n) : (n) <= 279 ? stbiw__zlib_huff3(n) : stbiw__zlib_huff4(n))
+#define stbiw__zlib_huffb(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : stbiw__zlib_huff2(n))
+
+#define stbiw__ZHASH   16384
+
+#endif // STBIW_ZLIB_COMPRESS
+
+unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality)
+{
+#ifdef STBIW_ZLIB_COMPRESS
+   // user provided a zlib compress implementation, use that
+   return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality);
+#else // use builtin
+   static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 };
+   static unsigned char  lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4,  4,  5,  5,  5,  5,  0 };
+   static unsigned short distc[]   = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 };
+   static unsigned char  disteb[]  = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 };
+   unsigned int bitbuf=0;
+   int i,j, bitcount=0;
+   unsigned char *out = NULL;
+   unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**));
+   if (hash_table == NULL)
+      return NULL;
+   if (quality < 5) quality = 5;
+
+   stbiw__sbpush(out, 0x78);   // DEFLATE 32K window
+   stbiw__sbpush(out, 0x5e);   // FLEVEL = 1
+   stbiw__zlib_add(1,1);  // BFINAL = 1
+   stbiw__zlib_add(1,2);  // BTYPE = 1 -- fixed huffman
+
+   for (i=0; i < stbiw__ZHASH; ++i)
+      hash_table[i] = NULL;
+
+   i=0;
+   while (i < data_len-3) {
+      // hash next 3 bytes of data to be compressed
+      int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3;
+      unsigned char *bestloc = 0;
+      unsigned char **hlist = hash_table[h];
+      int n = stbiw__sbcount(hlist);
+      for (j=0; j < n; ++j) {
+         if (hlist[j]-data > i-32768) { // if entry lies within window
+            int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i);
+            if (d >= best) best=d,bestloc=hlist[j];
+         }
+      }
+      // when hash table entry is too long, delete half the entries
+      if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) {
+         STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality);
+         stbiw__sbn(hash_table[h]) = quality;
+      }
+      stbiw__sbpush(hash_table[h],data+i);
+
+      if (bestloc) {
+         // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal
+         h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1);
+         hlist = hash_table[h];
+         n = stbiw__sbcount(hlist);
+         for (j=0; j < n; ++j) {
+            if (hlist[j]-data > i-32767) {
+               int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1);
+               if (e > best) { // if next match is better, bail on current match
+                  bestloc = NULL;
+                  break;
+               }
+            }
+         }
+      }
+
+      if (bestloc) {
+         int d = (int) (data+i - bestloc); // distance back
+         STBIW_ASSERT(d <= 32767 && best <= 258);
+         for (j=0; best > lengthc[j+1]-1; ++j);
+         stbiw__zlib_huff(j+257);
+         if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]);
+         for (j=0; d > distc[j+1]-1; ++j);
+         stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5);
+         if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]);
+         i += best;
+      } else {
+         stbiw__zlib_huffb(data[i]);
+         ++i;
+      }
+   }
+   // write out final bytes
+   for (;i < data_len; ++i)
+      stbiw__zlib_huffb(data[i]);
+   stbiw__zlib_huff(256); // end of block
+   // pad with 0 bits to byte boundary
+   while (bitcount)
+      stbiw__zlib_add(0,1);
+
+   for (i=0; i < stbiw__ZHASH; ++i)
+      (void) stbiw__sbfree(hash_table[i]);
+   STBIW_FREE(hash_table);
+
+   {
+      // compute adler32 on input
+      unsigned int s1=1, s2=0;
+      int blocklen = (int) (data_len % 5552);
+      j=0;
+      while (j < data_len) {
+         for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1;
+         s1 %= 65521, s2 %= 65521;
+         j += blocklen;
+         blocklen = 5552;
+      }
+      stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8));
+      stbiw__sbpush(out, STBIW_UCHAR(s2));
+      stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8));
+      stbiw__sbpush(out, STBIW_UCHAR(s1));
+   }
+   *out_len = stbiw__sbn(out);
+   // make returned pointer freeable
+   STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len);
+   return (unsigned char *) stbiw__sbraw(out);
+#endif // STBIW_ZLIB_COMPRESS
+}
+
+static unsigned int stbiw__crc32(unsigned char *buffer, int len)
+{
+   static unsigned int crc_table[256] =
+   {
+      0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3,
+      0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91,
+      0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7,
+      0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5,
+      0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B,
+      0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59,
+      0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F,
+      0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D,
+      0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433,
+      0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01,
+      0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457,
+      0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65,
+      0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB,
+      0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9,
+      0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F,
+      0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD,
+      0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683,
+      0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1,
+      0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7,
+      0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5,
+      0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B,
+      0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79,
+      0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F,
+      0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D,
+      0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713,
+      0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21,
+      0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777,
+      0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45,
+      0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB,
+      0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9,
+      0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF,
+      0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D
+   };
+
+   unsigned int crc = ~0u;
+   int i;
+   for (i=0; i < len; ++i)
+      crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)];
+   return ~crc;
+}
+
+#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4)
+#define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v));
+#define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3])
+
+static void stbiw__wpcrc(unsigned char **data, int len)
+{
+   unsigned int crc = stbiw__crc32(*data - len - 4, len+4);
+   stbiw__wp32(*data, crc);
+}
+
+static unsigned char stbiw__paeth(int a, int b, int c)
+{
+   int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c);
+   if (pa <= pb && pa <= pc) return STBIW_UCHAR(a);
+   if (pb <= pc) return STBIW_UCHAR(b);
+   return STBIW_UCHAR(c);
+}
+
+// @OPTIMIZE: provide an option that always forces left-predict or paeth predict
+static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer)
+{
+   static int mapping[] = { 0,1,2,3,4 };
+   static int firstmap[] = { 0,1,0,5,6 };
+   int *mymap = (y != 0) ? mapping : firstmap;
+   int i;
+   int type = mymap[filter_type];
+   unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y);
+   int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes;
+   for (i = 0; i < n; ++i) {
+      switch (type) {
+         case 0: line_buffer[i] = z[i]; break;
+         case 1: line_buffer[i] = z[i]; break;
+         case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break;
+         case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break;
+         case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break;
+         case 5: line_buffer[i] = z[i]; break;
+         case 6: line_buffer[i] = z[i]; break;
+      }
+   }
+   for (i=n; i < width*n; ++i) {
+      switch (type) {
+         case 0: line_buffer[i] = z[i]; break;
+         case 1: line_buffer[i] = z[i] - z[i-n]; break;
+         case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break;
+         case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break;
+         case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break;
+         case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break;
+         case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break;
+      }
+   }
+}
+
+unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len)
+{
+   int force_filter = stbi_write_force_png_filter;
+   int ctype[5] = { -1, 0, 4, 2, 6 };
+   unsigned char sig[8] = { 137,80,78,71,13,10,26,10 };
+   unsigned char *out,*o, *filt, *zlib;
+   signed char *line_buffer;
+   int j,zlen;
+
+   if (stride_bytes == 0)
+      stride_bytes = x * n;
+
+   if (force_filter >= 5) {
+      force_filter = -1;
+   }
+
+   filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0;
+   line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; }
+   for (j=0; j < y; ++j) {
+      int filter_type;
+      if (force_filter > -1) {
+         filter_type = force_filter;
+         stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, force_filter, line_buffer);
+      } else { // Estimate the best filter by running through all of them:
+         int best_filter = 0, best_filter_val = 0x7fffffff, est, i;
+         for (filter_type = 0; filter_type < 5; filter_type++) {
+            stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, filter_type, line_buffer);
+
+            // Estimate the entropy of the line using this filter; the less, the better.
+            est = 0;
+            for (i = 0; i < x*n; ++i) {
+               est += abs((signed char) line_buffer[i]);
+            }
+            if (est < best_filter_val) {
+               best_filter_val = est;
+               best_filter = filter_type;
+            }
+         }
+         if (filter_type != best_filter) {  // If the last iteration already got us the best filter, don't redo it
+            stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, best_filter, line_buffer);
+            filter_type = best_filter;
+         }
+      }
+      // when we get here, filter_type contains the filter type, and line_buffer contains the data
+      filt[j*(x*n+1)] = (unsigned char) filter_type;
+      STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n);
+   }
+   STBIW_FREE(line_buffer);
+   zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level);
+   STBIW_FREE(filt);
+   if (!zlib) return 0;
+
+   // each tag requires 12 bytes of overhead
+   out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12);
+   if (!out) return 0;
+   *out_len = 8 + 12+13 + 12+zlen + 12;
+
+   o=out;
+   STBIW_MEMMOVE(o,sig,8); o+= 8;
+   stbiw__wp32(o, 13); // header length
+   stbiw__wptag(o, "IHDR");
+   stbiw__wp32(o, x);
+   stbiw__wp32(o, y);
+   *o++ = 8;
+   *o++ = STBIW_UCHAR(ctype[n]);
+   *o++ = 0;
+   *o++ = 0;
+   *o++ = 0;
+   stbiw__wpcrc(&o,13);
+
+   stbiw__wp32(o, zlen);
+   stbiw__wptag(o, "IDAT");
+   STBIW_MEMMOVE(o, zlib, zlen);
+   o += zlen;
+   STBIW_FREE(zlib);
+   stbiw__wpcrc(&o, zlen);
+
+   stbiw__wp32(o,0);
+   stbiw__wptag(o, "IEND");
+   stbiw__wpcrc(&o,0);
+
+   STBIW_ASSERT(o == out + *out_len);
+
+   return out;
+}
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes)
+{
+   FILE *f;
+   int len;
+   unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len);
+   if (png == NULL) return 0;
+#ifdef STBI_MSC_SECURE_CRT
+   if (fopen_s(&f, filename, "wb"))
+      f = NULL;
+#else
+   f = fopen(filename, "wb");
+#endif
+   if (!f) { STBIW_FREE(png); return 0; }
+   fwrite(png, 1, len, f);
+   fclose(f);
+   STBIW_FREE(png);
+   return 1;
+}
+#endif
+
+STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes)
+{
+   int len;
+   unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len);
+   if (png == NULL) return 0;
+   func(context, png, len);
+   STBIW_FREE(png);
+   return 1;
+}
+
+
+/* ***************************************************************************
+ *
+ * JPEG writer
+ *
+ * This is based on Jon Olick's jo_jpeg.cpp:
+ * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html
+ */
+
+static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18,
+      24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 };
+
+static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) {
+   int bitBuf = *bitBufP, bitCnt = *bitCntP;
+   bitCnt += bs[1];
+   bitBuf |= bs[0] << (24 - bitCnt);
+   while(bitCnt >= 8) {
+      unsigned char c = (bitBuf >> 16) & 255;
+      stbiw__putc(s, c);
+      if(c == 255) {
+         stbiw__putc(s, 0);
+      }
+      bitBuf <<= 8;
+      bitCnt -= 8;
+   }
+   *bitBufP = bitBuf;
+   *bitCntP = bitCnt;
+}
+
+static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) {
+   float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p;
+   float z1, z2, z3, z4, z5, z11, z13;
+
+   float tmp0 = d0 + d7;
+   float tmp7 = d0 - d7;
+   float tmp1 = d1 + d6;
+   float tmp6 = d1 - d6;
+   float tmp2 = d2 + d5;
+   float tmp5 = d2 - d5;
+   float tmp3 = d3 + d4;
+   float tmp4 = d3 - d4;
+
+   // Even part
+   float tmp10 = tmp0 + tmp3;   // phase 2
+   float tmp13 = tmp0 - tmp3;
+   float tmp11 = tmp1 + tmp2;
+   float tmp12 = tmp1 - tmp2;
+
+   d0 = tmp10 + tmp11;       // phase 3
+   d4 = tmp10 - tmp11;
+
+   z1 = (tmp12 + tmp13) * 0.707106781f; // c4
+   d2 = tmp13 + z1;       // phase 5
+   d6 = tmp13 - z1;
+
+   // Odd part
+   tmp10 = tmp4 + tmp5;       // phase 2
+   tmp11 = tmp5 + tmp6;
+   tmp12 = tmp6 + tmp7;
+
+   // The rotator is modified from fig 4-8 to avoid extra negations.
+   z5 = (tmp10 - tmp12) * 0.382683433f; // c6
+   z2 = tmp10 * 0.541196100f + z5; // c2-c6
+   z4 = tmp12 * 1.306562965f + z5; // c2+c6
+   z3 = tmp11 * 0.707106781f; // c4
+
+   z11 = tmp7 + z3;      // phase 5
+   z13 = tmp7 - z3;
+
+   *d5p = z13 + z2;         // phase 6
+   *d3p = z13 - z2;
+   *d1p = z11 + z4;
+   *d7p = z11 - z4;
+
+   *d0p = d0;  *d2p = d2;  *d4p = d4;  *d6p = d6;
+}
+
+static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) {
+   int tmp1 = val < 0 ? -val : val;
+   val = val < 0 ? val-1 : val;
+   bits[1] = 1;
+   while(tmp1 >>= 1) {
+      ++bits[1];
+   }
+   bits[0] = val & ((1<<bits[1])-1);
+}
+
+static int stbiw__jpg_processDU(stbi__write_context *s, int *bitBuf, int *bitCnt, float *CDU, float *fdtbl, int DC, const unsigned short HTDC[256][2], const unsigned short HTAC[256][2]) {
+   const unsigned short EOB[2] = { HTAC[0x00][0], HTAC[0x00][1] };
+   const unsigned short M16zeroes[2] = { HTAC[0xF0][0], HTAC[0xF0][1] };
+   int dataOff, i, diff, end0pos;
+   int DU[64];
+
+   // DCT rows
+   for(dataOff=0; dataOff<64; dataOff+=8) {
+      stbiw__jpg_DCT(&CDU[dataOff], &CDU[dataOff+1], &CDU[dataOff+2], &CDU[dataOff+3], &CDU[dataOff+4], &CDU[dataOff+5], &CDU[dataOff+6], &CDU[dataOff+7]);
+   }
+   // DCT columns
+   for(dataOff=0; dataOff<8; ++dataOff) {
+      stbiw__jpg_DCT(&CDU[dataOff], &CDU[dataOff+8], &CDU[dataOff+16], &CDU[dataOff+24], &CDU[dataOff+32], &CDU[dataOff+40], &CDU[dataOff+48], &CDU[dataOff+56]);
+   }
+   // Quantize/descale/zigzag the coefficients
+   for(i=0; i<64; ++i) {
+      float v = CDU[i]*fdtbl[i];
+      // DU[stbiw__jpg_ZigZag[i]] = (int)(v < 0 ? ceilf(v - 0.5f) : floorf(v + 0.5f));
+      // ceilf() and floorf() are C99, not C89, but I /think/ they're not needed here anyway?
+      DU[stbiw__jpg_ZigZag[i]] = (int)(v < 0 ? v - 0.5f : v + 0.5f);
+   }
+
+   // Encode DC
+   diff = DU[0] - DC;
+   if (diff == 0) {
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTDC[0]);
+   } else {
+      unsigned short bits[2];
+      stbiw__jpg_calcBits(diff, bits);
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTDC[bits[1]]);
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits);
+   }
+   // Encode ACs
+   end0pos = 63;
+   for(; (end0pos>0)&&(DU[end0pos]==0); --end0pos) {
+   }
+   // end0pos = first element in reverse order !=0
+   if(end0pos == 0) {
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB);
+      return DU[0];
+   }
+   for(i = 1; i <= end0pos; ++i) {
+      int startpos = i;
+      int nrzeroes;
+      unsigned short bits[2];
+      for (; DU[i]==0 && i<=end0pos; ++i) {
+      }
+      nrzeroes = i-startpos;
+      if ( nrzeroes >= 16 ) {
+         int lng = nrzeroes>>4;
+         int nrmarker;
+         for (nrmarker=1; nrmarker <= lng; ++nrmarker)
+            stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes);
+         nrzeroes &= 15;
+      }
+      stbiw__jpg_calcBits(DU[i], bits);
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]);
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits);
+   }
+   if(end0pos != 63) {
+      stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB);
+   }
+   return DU[0];
+}
+
+static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) {
+   // Constants that don't pollute global namespace
+   static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0};
+   static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11};
+   static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d};
+   static const unsigned char std_ac_luminance_values[] = {
+      0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,
+      0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,
+      0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,
+      0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
+      0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,
+      0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,
+      0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa
+   };
+   static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0};
+   static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11};
+   static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77};
+   static const unsigned char std_ac_chrominance_values[] = {
+      0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,
+      0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,
+      0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,
+      0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
+      0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,
+      0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,
+      0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa
+   };
+   // Huffman tables
+   static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}};
+   static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}};
+   static const unsigned short YAC_HT[256][2] = {
+      {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0}
+   };
+   static const unsigned short UVAC_HT[256][2] = {
+      {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0},
+      {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0}
+   };
+   static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22,
+                             37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99};
+   static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99,
+                              99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99};
+   static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, 
+                                 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f };
+
+   int row, col, i, k;
+   float fdtbl_Y[64], fdtbl_UV[64];
+   unsigned char YTable[64], UVTable[64];
+
+   if(!data || !width || !height || comp > 4 || comp < 1) {
+      return 0;
+   }
+
+   quality = quality ? quality : 90;
+   quality = quality < 1 ? 1 : quality > 100 ? 100 : quality;
+   quality = quality < 50 ? 5000 / quality : 200 - quality * 2;
+
+   for(i = 0; i < 64; ++i) {
+      int uvti, yti = (YQT[i]*quality+50)/100;
+      YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti);
+      uvti = (UVQT[i]*quality+50)/100;
+      UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti);
+   }
+
+   for(row = 0, k = 0; row < 8; ++row) {
+      for(col = 0; col < 8; ++col, ++k) {
+         fdtbl_Y[k]  = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]);
+         fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]);
+      }
+   }
+
+   // Write Headers
+   {
+      static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 };
+      static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 };
+      const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width),
+                                      3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 };
+      s->func(s->context, (void*)head0, sizeof(head0));
+      s->func(s->context, (void*)YTable, sizeof(YTable));
+      stbiw__putc(s, 1);
+      s->func(s->context, UVTable, sizeof(UVTable));
+      s->func(s->context, (void*)head1, sizeof(head1));
+      s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1);
+      s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values));
+      stbiw__putc(s, 0x10); // HTYACinfo
+      s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1);
+      s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values));
+      stbiw__putc(s, 1); // HTUDCinfo
+      s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1);
+      s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values));
+      stbiw__putc(s, 0x11); // HTUACinfo
+      s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1);
+      s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values));
+      s->func(s->context, (void*)head2, sizeof(head2));
+   }
+
+   // Encode 8x8 macroblocks
+   {
+      static const unsigned short fillBits[] = {0x7F, 7};
+      const unsigned char *imageData = (const unsigned char *)data;
+      int DCY=0, DCU=0, DCV=0;
+      int bitBuf=0, bitCnt=0;
+      // comp == 2 is grey+alpha (alpha is ignored)
+      int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0;
+      int x, y, pos;
+      for(y = 0; y < height; y += 8) {
+         for(x = 0; x < width; x += 8) {
+            float YDU[64], UDU[64], VDU[64];
+            for(row = y, pos = 0; row < y+8; ++row) {
+               for(col = x; col < x+8; ++col, ++pos) {
+                  int p = (stbi__flip_vertically_on_write ? height-1-row : row)*width*comp + col*comp;
+                  float r, g, b;
+                  if(row >= height) {
+                     p -= width*comp*(row+1 - height);
+                  }
+                  if(col >= width) {
+                     p -= comp*(col+1 - width);
+                  }
+
+                  r = imageData[p+0];
+                  g = imageData[p+ofsG];
+                  b = imageData[p+ofsB];
+                  YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128;
+                  UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b;
+                  VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b;
+               }
+            }
+
+            DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT);
+            DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT);
+            DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT);
+         }
+      }
+
+      // Do the bit alignment of the EOI marker
+      stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits);
+   }
+
+   // EOI
+   stbiw__putc(s, 0xFF);
+   stbiw__putc(s, 0xD9);
+
+   return 1;
+}
+
+STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality)
+{
+   stbi__write_context s;
+   stbi__start_write_callbacks(&s, func, context);
+   return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality);
+}
+
+
+#ifndef STBI_WRITE_NO_STDIO
+STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality)
+{
+   stbi__write_context s;
+   if (stbi__start_write_file(&s,filename)) {
+      int r = stbi_write_jpg_core(&s, x, y, comp, data, quality);
+      stbi__end_write_file(&s);
+      return r;
+   } else
+      return 0;
+}
+#endif
+
+#endif // STB_IMAGE_WRITE_IMPLEMENTATION
+
+/* Revision history
+      1.09  (2018-02-11)
+             fix typo in zlib quality API, improve STB_I_W_STATIC in C++
+      1.08  (2018-01-29)
+             add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter
+      1.07  (2017-07-24)
+             doc fix
+      1.06 (2017-07-23)
+             writing JPEG (using Jon Olick's code)
+      1.05   ???
+      1.04 (2017-03-03)
+             monochrome BMP expansion
+      1.03   ???
+      1.02 (2016-04-02)
+             avoid allocating large structures on the stack
+      1.01 (2016-01-16)
+             STBIW_REALLOC_SIZED: support allocators with no realloc support
+             avoid race-condition in crc initialization
+             minor compile issues
+      1.00 (2015-09-14)
+             installable file IO function
+      0.99 (2015-09-13)
+             warning fixes; TGA rle support
+      0.98 (2015-04-08)
+             added STBIW_MALLOC, STBIW_ASSERT etc
+      0.97 (2015-01-18)
+             fixed HDR asserts, rewrote HDR rle logic
+      0.96 (2015-01-17)
+             add HDR output
+             fix monochrome BMP
+      0.95 (2014-08-17)
+		       add monochrome TGA output
+      0.94 (2014-05-31)
+             rename private functions to avoid conflicts with stb_image.h
+      0.93 (2014-05-27)
+             warning fixes
+      0.92 (2010-08-01)
+             casts to unsigned char to fix warnings
+      0.91 (2010-07-17)
+             first public release
+      0.90   first internal release
+*/
+
+/*
+------------------------------------------------------------------------------
+This software is available under 2 licenses -- choose whichever you prefer.
+------------------------------------------------------------------------------
+ALTERNATIVE A - MIT License
+Copyright (c) 2017 Sean Barrett
+Permission is hereby granted, free of charge, to any person obtaining a copy of 
+this software and associated documentation files (the "Software"), to deal in 
+the Software without restriction, including without limitation the rights to 
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies 
+of the Software, and to permit persons to whom the Software is furnished to do 
+so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all 
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 
+SOFTWARE.
+------------------------------------------------------------------------------
+ALTERNATIVE B - Public Domain (www.unlicense.org)
+This is free and unencumbered software released into the public domain.
+Anyone is free to copy, modify, publish, use, compile, sell, or distribute this 
+software, either in source code form or as a compiled binary, for any purpose, 
+commercial or non-commercial, and by any means.
+In jurisdictions that recognize copyright laws, the author or authors of this 
+software dedicate any and all copyright interest in the software to the public 
+domain. We make this dedication for the benefit of the public at large and to 
+the detriment of our heirs and successors. We intend this dedication to be an 
+overt act of relinquishment in perpetuity of all present and future rights to 
+this software under copyright law.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
+AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN 
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION 
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+------------------------------------------------------------------------------
+*/
diff --git a/hanzi_detection/src/tree.c b/hanzi_detection/src/tree.c
new file mode 100755
index 0000000..67b6d43
--- /dev/null
+++ b/hanzi_detection/src/tree.c
@@ -0,0 +1,139 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include "tree.h"
+#include "utils.h"
+#include "data.h"
+
+void change_leaves(tree *t, char *leaf_list)
+{
+    list *llist = get_paths(leaf_list);
+    char **leaves = (char **)list_to_array(llist);
+    int n = llist->size;
+    int i,j;
+    int found = 0;
+    for(i = 0; i < t->n; ++i){
+        t->leaf[i] = 0;
+        for(j = 0; j < n; ++j){
+            if (0==strcmp(t->name[i], leaves[j])){
+                t->leaf[i] = 1;
+                ++found;
+                break;
+            }
+        }
+    }
+    fprintf(stderr, "Found %d leaves.\n", found);
+}
+
+float get_hierarchy_probability(float *x, tree *hier, int c, int stride)
+{
+    float p = 1;
+    while(c >= 0){
+        p = p * x[c*stride];
+        c = hier->parent[c];
+    }
+    return p;
+}
+
+void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride)
+{
+    int j;
+    for(j = 0; j < n; ++j){
+        int parent = hier->parent[j];
+        if(parent >= 0){
+            predictions[j*stride] *= predictions[parent*stride]; 
+        }
+    }
+    if(only_leaves){
+        for(j = 0; j < n; ++j){
+            if(!hier->leaf[j]) predictions[j*stride] = 0;
+        }
+    }
+}
+
+int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride)
+{
+    float p = 1;
+    int group = 0;
+    int i;
+    while(1){
+        float max = 0;
+        int max_i = 0;
+
+        for(i = 0; i < hier->group_size[group]; ++i){
+            int index = i + hier->group_offset[group];
+            float val = predictions[(i + hier->group_offset[group])*stride];
+            if(val > max){
+                max_i = index;
+                max = val;
+            }
+        }
+        if(p*max > thresh){
+            p = p*max;
+            group = hier->child[max_i];
+            if(hier->child[max_i] < 0) return max_i;
+        } else if (group == 0){
+            return max_i;
+        } else {
+            return hier->parent[hier->group_offset[group]];
+        }
+    }
+    return 0;
+}
+
+tree *read_tree(char *filename)
+{
+    tree t = {0};
+    FILE *fp = fopen(filename, "r");
+
+    char *line;
+    int last_parent = -1;
+    int group_size = 0;
+    int groups = 0;
+    int n = 0;
+    while((line=fgetl(fp)) != 0){
+        char *id = calloc(256, sizeof(char));
+        int parent = -1;
+        sscanf(line, "%s %d", id, &parent);
+        t.parent = realloc(t.parent, (n+1)*sizeof(int));
+        t.parent[n] = parent;
+
+        t.child = realloc(t.child, (n+1)*sizeof(int));
+        t.child[n] = -1;
+
+        t.name = realloc(t.name, (n+1)*sizeof(char *));
+        t.name[n] = id;
+        if(parent != last_parent){
+            ++groups;
+            t.group_offset = realloc(t.group_offset, groups * sizeof(int));
+            t.group_offset[groups - 1] = n - group_size;
+            t.group_size = realloc(t.group_size, groups * sizeof(int));
+            t.group_size[groups - 1] = group_size;
+            group_size = 0;
+            last_parent = parent;
+        }
+        t.group = realloc(t.group, (n+1)*sizeof(int));
+        t.group[n] = groups;
+        if (parent >= 0) {
+            t.child[parent] = groups;
+        }
+        ++n;
+        ++group_size;
+    }
+    ++groups;
+    t.group_offset = realloc(t.group_offset, groups * sizeof(int));
+    t.group_offset[groups - 1] = n - group_size;
+    t.group_size = realloc(t.group_size, groups * sizeof(int));
+    t.group_size[groups - 1] = group_size;
+    t.n = n;
+    t.groups = groups;
+    t.leaf = calloc(n, sizeof(int));
+    int i;
+    for(i = 0; i < n; ++i) t.leaf[i] = 1;
+    for(i = 0; i < n; ++i) if(t.parent[i] >= 0) t.leaf[t.parent[i]] = 0;
+
+    fclose(fp);
+    tree *tree_ptr = calloc(1, sizeof(tree));
+    *tree_ptr = t;
+    //error(0);
+    return tree_ptr;
+}
diff --git a/hanzi_detection/src/tree.h b/hanzi_detection/src/tree.h
new file mode 100755
index 0000000..3802b8e
--- /dev/null
+++ b/hanzi_detection/src/tree.h
@@ -0,0 +1,8 @@
+#ifndef TREE_H
+#define TREE_H
+#include "darknet.h"
+
+int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride);
+float get_hierarchy_probability(float *x, tree *hier, int c, int stride);
+
+#endif
diff --git a/hanzi_detection/src/upsample_layer.c b/hanzi_detection/src/upsample_layer.c
new file mode 100755
index 0000000..605f21f
--- /dev/null
+++ b/hanzi_detection/src/upsample_layer.c
@@ -0,0 +1,106 @@
+#include "upsample_layer.h"
+#include "cuda.h"
+#include "blas.h"
+
+#include <stdio.h>
+
+layer make_upsample_layer(int batch, int w, int h, int c, int stride)
+{
+    layer l = {0};
+    l.type = UPSAMPLE;
+    l.batch = batch;
+    l.w = w;
+    l.h = h;
+    l.c = c;
+    l.out_w = w*stride;
+    l.out_h = h*stride;
+    l.out_c = c;
+    if(stride < 0){
+        stride = -stride;
+        l.reverse=1;
+        l.out_w = w/stride;
+        l.out_h = h/stride;
+    }
+    l.stride = stride;
+    l.outputs = l.out_w*l.out_h*l.out_c;
+    l.inputs = l.w*l.h*l.c;
+    l.delta =  calloc(l.outputs*batch, sizeof(float));
+    l.output = calloc(l.outputs*batch, sizeof(float));;
+
+    l.forward = forward_upsample_layer;
+    l.backward = backward_upsample_layer;
+    #ifdef GPU
+    l.forward_gpu = forward_upsample_layer_gpu;
+    l.backward_gpu = backward_upsample_layer_gpu;
+
+    l.delta_gpu =  cuda_make_array(l.delta, l.outputs*batch);
+    l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
+    #endif
+    if(l.reverse) fprintf(stderr, "downsample         %2dx  %4d x%4d x%4d   ->  %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
+    else fprintf(stderr, "upsample           %2dx  %4d x%4d x%4d   ->  %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
+    return l;
+}
+
+void resize_upsample_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+    l->out_w = w*l->stride;
+    l->out_h = h*l->stride;
+    if(l->reverse){
+        l->out_w = w/l->stride;
+        l->out_h = h/l->stride;
+    }
+    l->outputs = l->out_w*l->out_h*l->out_c;
+    l->inputs = l->h*l->w*l->c;
+    l->delta =  realloc(l->delta, l->outputs*l->batch*sizeof(float));
+    l->output = realloc(l->output, l->outputs*l->batch*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->output_gpu);
+    cuda_free(l->delta_gpu);
+    l->output_gpu  = cuda_make_array(l->output, l->outputs*l->batch);
+    l->delta_gpu   = cuda_make_array(l->delta,  l->outputs*l->batch);
+#endif
+    
+}
+
+void forward_upsample_layer(const layer l, network net)
+{
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+    if(l.reverse){
+        upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input);
+    }else{
+        upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output);
+    }
+}
+
+void backward_upsample_layer(const layer l, network net)
+{
+    if(l.reverse){
+        upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta);
+    }else{
+        upsample_cpu(net.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta);
+    }
+}
+
+#ifdef GPU
+void forward_upsample_layer_gpu(const layer l, network net)
+{
+    fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
+    if(l.reverse){
+        upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input_gpu);
+    }else{
+        upsample_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu);
+    }
+}
+
+void backward_upsample_layer_gpu(const layer l, network net)
+{
+    if(l.reverse){
+        upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta_gpu);
+    }else{
+        upsample_gpu(net.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu);
+    }
+}
+#endif
diff --git a/hanzi_detection/src/upsample_layer.h b/hanzi_detection/src/upsample_layer.h
new file mode 100755
index 0000000..86790d1
--- /dev/null
+++ b/hanzi_detection/src/upsample_layer.h
@@ -0,0 +1,15 @@
+#ifndef UPSAMPLE_LAYER_H
+#define UPSAMPLE_LAYER_H
+#include "darknet.h"
+
+layer make_upsample_layer(int batch, int w, int h, int c, int stride);
+void forward_upsample_layer(const layer l, network net);
+void backward_upsample_layer(const layer l, network net);
+void resize_upsample_layer(layer *l, int w, int h);
+
+#ifdef GPU
+void forward_upsample_layer_gpu(const layer l, network net);
+void backward_upsample_layer_gpu(const layer l, network net);
+#endif
+
+#endif
diff --git a/hanzi_detection/src/utils.c b/hanzi_detection/src/utils.c
new file mode 100755
index 0000000..626b467
--- /dev/null
+++ b/hanzi_detection/src/utils.c
@@ -0,0 +1,726 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include <assert.h>
+#include <unistd.h>
+#include <float.h>
+#include <limits.h>
+#include <time.h>
+#include <sys/time.h>
+
+#include "utils.h"
+
+
+/*
+// old timing. is it better? who knows!!
+double get_wall_time()
+{
+    struct timeval time;
+    if (gettimeofday(&time,NULL)){
+        return 0;
+    }
+    return (double)time.tv_sec + (double)time.tv_usec * .000001;
+}
+*/
+
+double what_time_is_it_now()
+{
+    struct timeval time;
+    if (gettimeofday(&time,NULL)){
+        return 0;
+    }
+    return (double)time.tv_sec + (double)time.tv_usec * .000001;
+}
+
+int *read_intlist(char *gpu_list, int *ngpus, int d)
+{
+    int *gpus = 0;
+    if(gpu_list){
+        int len = strlen(gpu_list);
+        *ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++*ngpus;
+        }
+        gpus = calloc(*ngpus, sizeof(int));
+        for(i = 0; i < *ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpus = calloc(1, sizeof(float));
+        *gpus = d;
+        *ngpus = 1;
+    }
+    return gpus;
+}
+
+int *read_map(char *filename)
+{
+    int n = 0;
+    int *map = 0;
+    char *str;
+    FILE *file = fopen(filename, "r");
+    if(!file) file_error(filename);
+    while((str=fgetl(file))){
+        ++n;
+        map = realloc(map, n*sizeof(int));
+        map[n-1] = atoi(str);
+    }
+    return map;
+}
+
+void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections)
+{
+    size_t i;
+    for(i = 0; i < sections; ++i){
+        size_t start = n*i/sections;
+        size_t end = n*(i+1)/sections;
+        size_t num = end-start;
+        shuffle(arr+(start*size), num, size);
+    }
+}
+
+void shuffle(void *arr, size_t n, size_t size)
+{
+    size_t i;
+    void *swp = calloc(1, size);
+    for(i = 0; i < n-1; ++i){
+        size_t j = i + rand()/(RAND_MAX / (n-i)+1);
+        memcpy(swp,          arr+(j*size), size);
+        memcpy(arr+(j*size), arr+(i*size), size);
+        memcpy(arr+(i*size), swp,          size);
+    }
+}
+
+int *random_index_order(int min, int max)
+{
+    int *inds = calloc(max-min, sizeof(int));
+    int i;
+    for(i = min; i < max; ++i){
+        inds[i] = i;
+    }
+    for(i = min; i < max-1; ++i){
+        int swap = inds[i];
+        int index = i + rand()%(max-i);
+        inds[i] = inds[index];
+        inds[index] = swap;
+    }
+    return inds;
+}
+
+void del_arg(int argc, char **argv, int index)
+{
+    int i;
+    for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
+    argv[i] = 0;
+}
+
+int find_arg(int argc, char* argv[], char *arg)
+{
+    int i;
+    for(i = 0; i < argc; ++i) {
+        if(!argv[i]) continue;
+        if(0==strcmp(argv[i], arg)) {
+            del_arg(argc, argv, i);
+            return 1;
+        }
+    }
+    return 0;
+}
+
+int find_int_arg(int argc, char **argv, char *arg, int def)
+{
+    int i;
+    for(i = 0; i < argc-1; ++i){
+        if(!argv[i]) continue;
+        if(0==strcmp(argv[i], arg)){
+            def = atoi(argv[i+1]);
+            del_arg(argc, argv, i);
+            del_arg(argc, argv, i);
+            break;
+        }
+    }
+    return def;
+}
+
+float find_float_arg(int argc, char **argv, char *arg, float def)
+{
+    int i;
+    for(i = 0; i < argc-1; ++i){
+        if(!argv[i]) continue;
+        if(0==strcmp(argv[i], arg)){
+            def = atof(argv[i+1]);
+            del_arg(argc, argv, i);
+            del_arg(argc, argv, i);
+            break;
+        }
+    }
+    return def;
+}
+
+char *find_char_arg(int argc, char **argv, char *arg, char *def)
+{
+    int i;
+    for(i = 0; i < argc-1; ++i){
+        if(!argv[i]) continue;
+        if(0==strcmp(argv[i], arg)){
+            def = argv[i+1];
+            del_arg(argc, argv, i);
+            del_arg(argc, argv, i);
+            break;
+        }
+    }
+    return def;
+}
+
+
+char *basecfg(char *cfgfile)
+{
+    char *c = cfgfile;
+    char *next;
+    while((next = strchr(c, '/')))
+    {
+        c = next+1;
+    }
+    c = copy_string(c);
+    next = strchr(c, '.');
+    if (next) *next = 0;
+    return c;
+}
+
+int alphanum_to_int(char c)
+{
+    return (c < 58) ? c - 48 : c-87;
+}
+char int_to_alphanum(int i)
+{
+    if (i == 36) return '.';
+    return (i < 10) ? i + 48 : i + 87;
+}
+
+void pm(int M, int N, float *A)
+{
+    int i,j;
+    for(i =0 ; i < M; ++i){
+        printf("%d ", i+1);
+        for(j = 0; j < N; ++j){
+            printf("%2.4f, ", A[i*N+j]);
+        }
+        printf("\n");
+    }
+    printf("\n");
+}
+
+void find_replace(char *str, char *orig, char *rep, char *output)
+{
+    char buffer[4096] = {0};
+    char *p;
+
+    sprintf(buffer, "%s", str);
+    if(!(p = strstr(buffer, orig))){  // Is 'orig' even in 'str'?
+        sprintf(output, "%s", str);
+        return;
+    }
+
+    *p = '\0';
+
+    sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig));
+}
+
+float sec(clock_t clocks)
+{
+    return (float)clocks/CLOCKS_PER_SEC;
+}
+
+void top_k(float *a, int n, int k, int *index)
+{
+    int i,j;
+    for(j = 0; j < k; ++j) index[j] = -1;
+    for(i = 0; i < n; ++i){
+        int curr = i;
+        for(j = 0; j < k; ++j){
+            if((index[j] < 0) || a[curr] > a[index[j]]){
+                int swap = curr;
+                curr = index[j];
+                index[j] = swap;
+            }
+        }
+    }
+}
+
+void error(const char *s)
+{
+    perror(s);
+    assert(0);
+    exit(-1);
+}
+
+unsigned char *read_file(char *filename)
+{
+    FILE *fp = fopen(filename, "rb");
+    size_t size;
+
+    fseek(fp, 0, SEEK_END); 
+    size = ftell(fp);
+    fseek(fp, 0, SEEK_SET); 
+
+    unsigned char *text = calloc(size+1, sizeof(char));
+    fread(text, 1, size, fp);
+    fclose(fp);
+    return text;
+}
+
+void malloc_error()
+{
+    fprintf(stderr, "Malloc error\n");
+    exit(-1);
+}
+
+void file_error(char *s)
+{
+    fprintf(stderr, "Couldn't open file: %s\n", s);
+    exit(0);
+}
+
+list *split_str(char *s, char delim)
+{
+    size_t i;
+    size_t len = strlen(s);
+    list *l = make_list();
+    list_insert(l, s);
+    for(i = 0; i < len; ++i){
+        if(s[i] == delim){
+            s[i] = '\0';
+            list_insert(l, &(s[i+1]));
+        }
+    }
+    return l;
+}
+
+void strip(char *s)
+{
+    size_t i;
+    size_t len = strlen(s);
+    size_t offset = 0;
+    for(i = 0; i < len; ++i){
+        char c = s[i];
+        if(c==' '||c=='\t'||c=='\n') ++offset;
+        else s[i-offset] = c;
+    }
+    s[len-offset] = '\0';
+}
+
+void strip_char(char *s, char bad)
+{
+    size_t i;
+    size_t len = strlen(s);
+    size_t offset = 0;
+    for(i = 0; i < len; ++i){
+        char c = s[i];
+        if(c==bad) ++offset;
+        else s[i-offset] = c;
+    }
+    s[len-offset] = '\0';
+}
+
+void free_ptrs(void **ptrs, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i) free(ptrs[i]);
+    free(ptrs);
+}
+
+char *fgetl(FILE *fp)
+{
+    if(feof(fp)) return 0;
+    size_t size = 512;
+    char *line = malloc(size*sizeof(char));
+    if(!fgets(line, size, fp)){
+        free(line);
+        return 0;
+    }
+
+    size_t curr = strlen(line);
+
+    while((line[curr-1] != '\n') && !feof(fp)){
+        if(curr == size-1){
+            size *= 2;
+            line = realloc(line, size*sizeof(char));
+            if(!line) {
+                printf("%ld\n", size);
+                malloc_error();
+            }
+        }
+        size_t readsize = size-curr;
+        if(readsize > INT_MAX) readsize = INT_MAX-1;
+        fgets(&line[curr], readsize, fp);
+        curr = strlen(line);
+    }
+    if(line[curr-1] == '\n') line[curr-1] = '\0';
+
+    return line;
+}
+
+int read_int(int fd)
+{
+    int n = 0;
+    int next = read(fd, &n, sizeof(int));
+    if(next <= 0) return -1;
+    return n;
+}
+
+void write_int(int fd, int n)
+{
+    int next = write(fd, &n, sizeof(int));
+    if(next <= 0) error("read failed");
+}
+
+int read_all_fail(int fd, char *buffer, size_t bytes)
+{
+    size_t n = 0;
+    while(n < bytes){
+        int next = read(fd, buffer + n, bytes-n);
+        if(next <= 0) return 1;
+        n += next;
+    }
+    return 0;
+}
+
+int write_all_fail(int fd, char *buffer, size_t bytes)
+{
+    size_t n = 0;
+    while(n < bytes){
+        size_t next = write(fd, buffer + n, bytes-n);
+        if(next <= 0) return 1;
+        n += next;
+    }
+    return 0;
+}
+
+void read_all(int fd, char *buffer, size_t bytes)
+{
+    size_t n = 0;
+    while(n < bytes){
+        int next = read(fd, buffer + n, bytes-n);
+        if(next <= 0) error("read failed");
+        n += next;
+    }
+}
+
+void write_all(int fd, char *buffer, size_t bytes)
+{
+    size_t n = 0;
+    while(n < bytes){
+        size_t next = write(fd, buffer + n, bytes-n);
+        if(next <= 0) error("write failed");
+        n += next;
+    }
+}
+
+
+char *copy_string(char *s)
+{
+    char *copy = malloc(strlen(s)+1);
+    strncpy(copy, s, strlen(s)+1);
+    return copy;
+}
+
+list *parse_csv_line(char *line)
+{
+    list *l = make_list();
+    char *c, *p;
+    int in = 0;
+    for(c = line, p = line; *c != '\0'; ++c){
+        if(*c == '"') in = !in;
+        else if(*c == ',' && !in){
+            *c = '\0';
+            list_insert(l, copy_string(p));
+            p = c+1;
+        }
+    }
+    list_insert(l, copy_string(p));
+    return l;
+}
+
+int count_fields(char *line)
+{
+    int count = 0;
+    int done = 0;
+    char *c;
+    for(c = line; !done; ++c){
+        done = (*c == '\0');
+        if(*c == ',' || done) ++count;
+    }
+    return count;
+}
+
+float *parse_fields(char *line, int n)
+{
+    float *field = calloc(n, sizeof(float));
+    char *c, *p, *end;
+    int count = 0;
+    int done = 0;
+    for(c = line, p = line; !done; ++c){
+        done = (*c == '\0');
+        if(*c == ',' || done){
+            *c = '\0';
+            field[count] = strtod(p, &end);
+            if(p == c) field[count] = nan("");
+            if(end != c && (end != c-1 || *end != '\r')) field[count] = nan(""); //DOS file formats!
+            p = c+1;
+            ++count;
+        }
+    }
+    return field;
+}
+
+float sum_array(float *a, int n)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i) sum += a[i];
+    return sum;
+}
+
+float mean_array(float *a, int n)
+{
+    return sum_array(a,n)/n;
+}
+
+void mean_arrays(float **a, int n, int els, float *avg)
+{
+    int i;
+    int j;
+    memset(avg, 0, els*sizeof(float));
+    for(j = 0; j < n; ++j){
+        for(i = 0; i < els; ++i){
+            avg[i] += a[j][i];
+        }
+    }
+    for(i = 0; i < els; ++i){
+        avg[i] /= n;
+    }
+}
+
+void print_statistics(float *a, int n)
+{
+    float m = mean_array(a, n);
+    float v = variance_array(a, n);
+    printf("MSE: %.6f, Mean: %.6f, Variance: %.6f\n", mse_array(a, n), m, v);
+}
+
+float variance_array(float *a, int n)
+{
+    int i;
+    float sum = 0;
+    float mean = mean_array(a, n);
+    for(i = 0; i < n; ++i) sum += (a[i] - mean)*(a[i]-mean);
+    float variance = sum/n;
+    return variance;
+}
+
+int constrain_int(int a, int min, int max)
+{
+    if (a < min) return min;
+    if (a > max) return max;
+    return a;
+}
+
+float constrain(float min, float max, float a)
+{
+    if (a < min) return min;
+    if (a > max) return max;
+    return a;
+}
+
+float dist_array(float *a, float *b, int n, int sub)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; i += sub) sum += pow(a[i]-b[i], 2);
+    return sqrt(sum);
+}
+
+float mse_array(float *a, int n)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i) sum += a[i]*a[i];
+    return sqrt(sum/n);
+}
+
+void normalize_array(float *a, int n)
+{
+    int i;
+    float mu = mean_array(a,n);
+    float sigma = sqrt(variance_array(a,n));
+    for(i = 0; i < n; ++i){
+        a[i] = (a[i] - mu)/sigma;
+    }
+    mu = mean_array(a,n);
+    sigma = sqrt(variance_array(a,n));
+}
+
+void translate_array(float *a, int n, float s)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        a[i] += s;
+    }
+}
+
+float mag_array(float *a, int n)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i){
+        sum += a[i]*a[i];   
+    }
+    return sqrt(sum);
+}
+
+void scale_array(float *a, int n, float s)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        a[i] *= s;
+    }
+}
+
+int sample_array(float *a, int n)
+{
+    float sum = sum_array(a, n);
+    scale_array(a, n, 1./sum);
+    float r = rand_uniform(0, 1);
+    int i;
+    for(i = 0; i < n; ++i){
+        r = r - a[i];
+        if (r <= 0) return i;
+    }
+    return n-1;
+}
+
+int max_int_index(int *a, int n)
+{
+    if(n <= 0) return -1;
+    int i, max_i = 0;
+    int max = a[0];
+    for(i = 1; i < n; ++i){
+        if(a[i] > max){
+            max = a[i];
+            max_i = i;
+        }
+    }
+    return max_i;
+}
+
+int max_index(float *a, int n)
+{
+    if(n <= 0) return -1;
+    int i, max_i = 0;
+    float max = a[0];
+    for(i = 1; i < n; ++i){
+        if(a[i] > max){
+            max = a[i];
+            max_i = i;
+        }
+    }
+    return max_i;
+}
+
+int int_index(int *a, int val, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        if(a[i] == val) return i;
+    }
+    return -1;
+}
+
+int rand_int(int min, int max)
+{
+    if (max < min){
+        int s = min;
+        min = max;
+        max = s;
+    }
+    int r = (rand()%(max - min + 1)) + min;
+    return r;
+}
+
+// From http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
+float rand_normal()
+{
+    static int haveSpare = 0;
+    static double rand1, rand2;
+
+    if(haveSpare)
+    {
+        haveSpare = 0;
+        return sqrt(rand1) * sin(rand2);
+    }
+
+    haveSpare = 1;
+
+    rand1 = rand() / ((double) RAND_MAX);
+    if(rand1 < 1e-100) rand1 = 1e-100;
+    rand1 = -2 * log(rand1);
+    rand2 = (rand() / ((double) RAND_MAX)) * TWO_PI;
+
+    return sqrt(rand1) * cos(rand2);
+}
+
+/*
+   float rand_normal()
+   {
+   int n = 12;
+   int i;
+   float sum= 0;
+   for(i = 0; i < n; ++i) sum += (float)rand()/RAND_MAX;
+   return sum-n/2.;
+   }
+ */
+
+size_t rand_size_t()
+{
+    return  ((size_t)(rand()&0xff) << 56) | 
+        ((size_t)(rand()&0xff) << 48) |
+        ((size_t)(rand()&0xff) << 40) |
+        ((size_t)(rand()&0xff) << 32) |
+        ((size_t)(rand()&0xff) << 24) |
+        ((size_t)(rand()&0xff) << 16) |
+        ((size_t)(rand()&0xff) << 8) |
+        ((size_t)(rand()&0xff) << 0);
+}
+
+float rand_uniform(float min, float max)
+{
+    if(max < min){
+        float swap = min;
+        min = max;
+        max = swap;
+    }
+    return ((float)rand()/RAND_MAX * (max - min)) + min;
+}
+
+float rand_scale(float s)
+{
+    float scale = rand_uniform(1, s);
+    if(rand()%2) return scale;
+    return 1./scale;
+}
+
+float **one_hot_encode(float *a, int n, int k)
+{
+    int i;
+    float **t = calloc(n, sizeof(float*));
+    for(i = 0; i < n; ++i){
+        t[i] = calloc(k, sizeof(float));
+        int index = (int)a[i];
+        t[i][index] = 1;
+    }
+    return t;
+}
+
diff --git a/hanzi_detection/src/utils.h b/hanzi_detection/src/utils.h
new file mode 100755
index 0000000..ef24da7
--- /dev/null
+++ b/hanzi_detection/src/utils.h
@@ -0,0 +1,53 @@
+#ifndef UTILS_H
+#define UTILS_H
+#include <stdio.h>
+#include <time.h>
+#include "darknet.h"
+#include "list.h"
+
+#define TIME(a) \
+    do { \
+    double start = what_time_is_it_now(); \
+    a; \
+    printf("%s took: %f seconds\n", #a, what_time_is_it_now() - start); \
+    } while (0)
+
+#define TWO_PI 6.2831853071795864769252866f
+
+double what_time_is_it_now();
+void shuffle(void *arr, size_t n, size_t size);
+void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections);
+void free_ptrs(void **ptrs, int n);
+int alphanum_to_int(char c);
+char int_to_alphanum(int i);
+int read_int(int fd);
+void write_int(int fd, int n);
+void read_all(int fd, char *buffer, size_t bytes);
+void write_all(int fd, char *buffer, size_t bytes);
+int read_all_fail(int fd, char *buffer, size_t bytes);
+int write_all_fail(int fd, char *buffer, size_t bytes);
+void find_replace(char *str, char *orig, char *rep, char *output);
+void malloc_error();
+void file_error(char *s);
+void strip(char *s);
+void strip_char(char *s, char bad);
+list *split_str(char *s, char delim);
+char *fgetl(FILE *fp);
+list *parse_csv_line(char *line);
+char *copy_string(char *s);
+int count_fields(char *line);
+float *parse_fields(char *line, int n);
+void translate_array(float *a, int n, float s);
+float constrain(float min, float max, float a);
+int constrain_int(int a, int min, int max);
+float rand_scale(float s);
+int rand_int(int min, int max);
+void mean_arrays(float **a, int n, int els, float *avg);
+float dist_array(float *a, float *b, int n, int sub);
+float **one_hot_encode(float *a, int n, int k);
+float sec(clock_t clocks);
+void print_statistics(float *a, int n);
+int int_index(int *a, int val, int n);
+
+#endif
+
diff --git a/hanzi_detection/src/yolo_layer.c b/hanzi_detection/src/yolo_layer.c
new file mode 100755
index 0000000..c338036
--- /dev/null
+++ b/hanzi_detection/src/yolo_layer.c
@@ -0,0 +1,374 @@
+#include "yolo_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes)
+{
+    int i;
+    layer l = {0};
+    l.type = YOLO;
+
+    l.n = n;
+    l.total = total;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = n*(classes + 4 + 1);
+    l.out_w = l.w;
+    l.out_h = l.h;
+    l.out_c = l.c;
+    l.classes = classes;
+    l.cost = calloc(1, sizeof(float));
+    l.biases = calloc(total*2, sizeof(float));
+    if(mask) l.mask = mask;
+    else{
+        l.mask = calloc(n, sizeof(int));
+        for(i = 0; i < n; ++i){
+            l.mask[i] = i;
+        }
+    }
+    l.bias_updates = calloc(n*2, sizeof(float));
+    l.outputs = h*w*n*(classes + 4 + 1);
+    l.inputs = l.outputs;
+    l.truths = 90*(4 + 1);
+    l.delta = calloc(batch*l.outputs, sizeof(float));
+    l.output = calloc(batch*l.outputs, sizeof(float));
+    for(i = 0; i < total*2; ++i){
+        l.biases[i] = .5;
+    }
+
+    l.forward = forward_yolo_layer;
+    l.backward = backward_yolo_layer;
+#ifdef GPU
+    l.forward_gpu = forward_yolo_layer_gpu;
+    l.backward_gpu = backward_yolo_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+    fprintf(stderr, "yolo\n");
+    srand(0);
+
+    return l;
+}
+
+void resize_yolo_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->outputs = h*w*l->n*(l->classes + 4 + 1);
+    l->inputs = l->outputs;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =     cuda_make_array(l->delta, l->batch*l->outputs);
+    l->output_gpu =    cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
+
+box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride)
+{
+    box b;
+    b.x = (i + x[index + 0*stride]) / lw;
+    b.y = (j + x[index + 1*stride]) / lh;
+    b.w = exp(x[index + 2*stride]) * biases[2*n]   / w;
+    b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
+    return b;
+}
+
+float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride)
+{
+    box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride);
+    float iou = box_iou(pred, truth);
+
+    float tx = (truth.x*lw - i);
+    float ty = (truth.y*lh - j);
+    float tw = log(truth.w*w / biases[2*n]);
+    float th = log(truth.h*h / biases[2*n + 1]);
+
+    delta[index + 0*stride] = scale * (tx - x[index + 0*stride]);
+    delta[index + 1*stride] = scale * (ty - x[index + 1*stride]);
+    delta[index + 2*stride] = scale * (tw - x[index + 2*stride]);
+    delta[index + 3*stride] = scale * (th - x[index + 3*stride]);
+    return iou;
+}
+
+
+void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat)
+{
+    int n;
+    if (delta[index]){
+        delta[index + stride*class] = 1 - output[index + stride*class];
+        if(avg_cat) *avg_cat += output[index + stride*class];
+        return;
+    }
+    for(n = 0; n < classes; ++n){
+        delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n];
+        if(n == class && avg_cat) *avg_cat += output[index + stride*n];
+    }
+}
+
+static int entry_index(layer l, int batch, int location, int entry)
+{
+    int n =   location / (l.w*l.h);
+    int loc = location % (l.w*l.h);
+    return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc;
+}
+
+void forward_yolo_layer(const layer l, network net)
+{
+    int i,j,b,t,n;
+    memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+
+#ifndef GPU
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int index = entry_index(l, b, n*l.w*l.h, 0);
+            activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
+            index = entry_index(l, b, n*l.w*l.h, 4);
+            activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC);
+        }
+    }
+#endif
+
+    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+    if(!net.train) return;
+    float avg_iou = 0;
+    float recall = 0;
+    float recall75 = 0;
+    float avg_cat = 0;
+    float avg_obj = 0;
+    float avg_anyobj = 0;
+    int count = 0;
+    int class_count = 0;
+    *(l.cost) = 0;
+    for (b = 0; b < l.batch; ++b) {
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                    box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h);
+                    float best_iou = 0;
+                    int best_t = 0;
+                    for(t = 0; t < l.max_boxes; ++t){
+                        box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1);
+                        if(!truth.x) break;
+                        float iou = box_iou(pred, truth);
+                        if (iou > best_iou) {
+                            best_iou = iou;
+                            best_t = t;
+                        }
+                    }
+                    int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
+                    avg_anyobj += l.output[obj_index];
+                    l.delta[obj_index] = 0 - l.output[obj_index];
+                    if (best_iou > l.ignore_thresh) {
+                        l.delta[obj_index] = 0;
+                    }
+                    if (best_iou > l.truth_thresh) {
+                        l.delta[obj_index] = 1 - l.output[obj_index];
+
+                        int class = net.truth[best_t*(4 + 1) + b*l.truths + 4];
+                        if (l.map) class = l.map[class];
+                        int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
+                        delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0);
+                        box truth = float_to_box(net.truth + best_t*(4 + 1) + b*l.truths, 1);
+                        delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
+                    }
+                }
+            }
+        }
+        for(t = 0; t < l.max_boxes; ++t){
+            box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1);
+
+            if(!truth.x) break;
+            float best_iou = 0;
+            int best_n = 0;
+            i = (truth.x * l.w);
+            j = (truth.y * l.h);
+            box truth_shift = truth;
+            truth_shift.x = truth_shift.y = 0;
+            for(n = 0; n < l.total; ++n){
+                box pred = {0};
+                pred.w = l.biases[2*n]/net.w;
+                pred.h = l.biases[2*n+1]/net.h;
+                float iou = box_iou(pred, truth_shift);
+                if (iou > best_iou){
+                    best_iou = iou;
+                    best_n = n;
+                }
+            }
+
+            int mask_n = int_index(l.mask, best_n, l.n);
+            if(mask_n >= 0){
+                int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
+                float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
+
+                int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
+                avg_obj += l.output[obj_index];
+                l.delta[obj_index] = 1 - l.output[obj_index];
+
+                int class = net.truth[t*(4 + 1) + b*l.truths + 4];
+                if (l.map) class = l.map[class];
+                int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
+                delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat);
+
+                ++count;
+                ++class_count;
+                if(iou > .5) recall += 1;
+                if(iou > .75) recall75 += 1;
+                avg_iou += iou;
+            }
+        }
+    }
+    *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+    printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count);
+}
+
+void backward_yolo_layer(const layer l, network net)
+{
+   axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
+}
+
+void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
+{
+    int i;
+    int new_w=0;
+    int new_h=0;
+    if (((float)netw/w) < ((float)neth/h)) {
+        new_w = netw;
+        new_h = (h * netw)/w;
+    } else {
+        new_h = neth;
+        new_w = (w * neth)/h;
+    }
+    for (i = 0; i < n; ++i){
+        box b = dets[i].bbox;
+        b.x =  (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); 
+        b.y =  (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); 
+        b.w *= (float)netw/new_w;
+        b.h *= (float)neth/new_h;
+        if(!relative){
+            b.x *= w;
+            b.w *= w;
+            b.y *= h;
+            b.h *= h;
+        }
+        dets[i].bbox = b;
+    }
+}
+
+int yolo_num_detections(layer l, float thresh)
+{
+    int i, n;
+    int count = 0;
+    for (i = 0; i < l.w*l.h; ++i){
+        for(n = 0; n < l.n; ++n){
+            int obj_index  = entry_index(l, 0, n*l.w*l.h + i, 4);
+            if(l.output[obj_index] > thresh){
+                ++count;
+            }
+        }
+    }
+    return count;
+}
+
+void avg_flipped_yolo(layer l)
+{
+    int i,j,n,z;
+    float *flip = l.output + l.outputs;
+    for (j = 0; j < l.h; ++j) {
+        for (i = 0; i < l.w/2; ++i) {
+            for (n = 0; n < l.n; ++n) {
+                for(z = 0; z < l.classes + 4 + 1; ++z){
+                    int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
+                    int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
+                    float swap = flip[i1];
+                    flip[i1] = flip[i2];
+                    flip[i2] = swap;
+                    if(z == 0){
+                        flip[i1] = -flip[i1];
+                        flip[i2] = -flip[i2];
+                    }
+                }
+            }
+        }
+    }
+    for(i = 0; i < l.outputs; ++i){
+        l.output[i] = (l.output[i] + flip[i])/2.;
+    }
+}
+
+int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets)
+{
+    int i,j,n;
+    float *predictions = l.output;
+    if (l.batch == 2) avg_flipped_yolo(l);
+    int count = 0;
+    for (i = 0; i < l.w*l.h; ++i){
+        int row = i / l.w;
+        int col = i % l.w;
+        for(n = 0; n < l.n; ++n){
+            int obj_index  = entry_index(l, 0, n*l.w*l.h + i, 4);
+            float objectness = predictions[obj_index];
+            if(objectness <= thresh) continue;
+            int box_index  = entry_index(l, 0, n*l.w*l.h + i, 0);
+            dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h);
+            dets[count].objectness = objectness;
+            dets[count].classes = l.classes;
+            for(j = 0; j < l.classes; ++j){
+                int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j);
+                float prob = objectness*predictions[class_index];
+                dets[count].prob[j] = (prob > thresh) ? prob : 0;
+            }
+            ++count;
+        }
+    }
+    correct_yolo_boxes(dets, count, w, h, netw, neth, relative);
+    return count;
+}
+
+#ifdef GPU
+
+void forward_yolo_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
+    int b, n;
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int index = entry_index(l, b, n*l.w*l.h, 0);
+            activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
+            index = entry_index(l, b, n*l.w*l.h, 4);
+            activate_array_gpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC);
+        }
+    }
+    if(!net.train || l.onlyforward){
+        cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
+        return;
+    }
+
+    cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
+    forward_yolo_layer(l, net);
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+}
+
+void backward_yolo_layer_gpu(const layer l, network net)
+{
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+
diff --git a/hanzi_detection/src/yolo_layer.h b/hanzi_detection/src/yolo_layer.h
new file mode 100755
index 0000000..d2a0243
--- /dev/null
+++ b/hanzi_detection/src/yolo_layer.h
@@ -0,0 +1,19 @@
+#ifndef YOLO_LAYER_H
+#define YOLO_LAYER_H
+
+#include "darknet.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes);
+void forward_yolo_layer(const layer l, network net);
+void backward_yolo_layer(const layer l, network net);
+void resize_yolo_layer(layer *l, int w, int h);
+int yolo_num_detections(layer l, float thresh);
+
+#ifdef GPU
+void forward_yolo_layer_gpu(const layer l, network net);
+void backward_yolo_layer_gpu(layer l, network net);
+#endif
+
+#endif
diff --git "a/media/0b7bbb3595309f7f9123704ef354a52d\346\227\205_u65c5.jpg" "b/media/0b7bbb3595309f7f9123704ef354a52d\346\227\205_u65c5.jpg"
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diff --git a/nine b/nine
new file mode 160000
index 0000000..4a03d40
--- /dev/null
+++ b/nine
@@ -0,0 +1 @@
+Subproject commit 4a03d405982aa1e1e911eac42b0ffce29cc8c8ef

From 0f8c5c81ca31e844a65a55e79b64a8a6e8a5778f Mon Sep 17 00:00:00 2001
From: huaiyukeji <huaiyukeji@gmail.com>
Date: Sun, 17 Dec 2023 22:22:32 +0800
Subject: [PATCH 2/3] docs: readme

---
 README.md | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index fb5146d..5dbf2ac 100644
--- a/README.md
+++ b/README.md
@@ -30,13 +30,17 @@
 
 ---
 
+## 联系方式
+
+[TG电报群](https://t.me/+Wj3ALdyYXV1lNmM1)
+
 
 
 ## 待更新内容
 
 如果对您有用，请给个star:star::star::star:谢谢。点赞可让我看到大家的关注情况，持续更新的动力，谢谢支持。
 
-刚建了个群，随缘加加！不能分享的代码在群里交流！群号：:rocket:`1040784971`:rocket:
+
 
 许多内容待整理。
 
@@ -86,11 +90,3 @@
 
 
 
-
-## 联系方式
-欢迎技术交流：`huaiyukeji@gmail.com`
-
-
-
-TG电报群：https://t.me/joinchat/99xuS4HiOIc2YTU1(老群已解散)
-

From 3d9448ee32e7c7ded4ce3465465ac527eae51493 Mon Sep 17 00:00:00 2001
From: huaiyukeji <huaiyukeji@gmail.com>
Date: Mon, 18 Dec 2023 19:09:03 +0800
Subject: [PATCH 3/3] docs: readme

---
 IMG_1766.JPG | Bin 219722 -> 0 bytes
 README.md    |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 IMG_1766.JPG

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diff --git a/README.md b/README.md
index 5dbf2ac..e322afc 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@
 
 ## 联系方式
 
-[TG电报群](https://t.me/+Wj3ALdyYXV1lNmM1)
+TG电报 `@heeetui`