Skip to content

Latest commit

 

History

History
 
 

VOLO

README.md

VOLO: Vision Outlooker for Visual Recognition, arxiv

PaddlePaddle training/validation code and pretrained models for VOLO.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

VOLO Model Overview

Update

  • Update (2021-09-27): More weights are uploaded.
  • Update (2021-08-11): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
volo_d1_224 84.12 96.78 26.6M 6.6G 224 1.0 bicubic google/baidu(xaim)
volo_d1_384 85.24 97.21 26.6M 19.5G 384 1.0 bicubic google/baidu(rr7p)
volo_d2_224 85.11 97.19 58.6M 13.7G 224 1.0 bicubic google/baidu(d82f)
volo_d2_384 86.04 97.57 58.6M 40.7G 384 1.0 bicubic google/baidu(9cf3)
volo_d3_224 85.41 97.26 86.2M 19.8G 224 1.0 bicubic google/baidu(a5a4)
volo_d3_448 86.50 97.71 86.2M 80.3G 448 1.0 bicubic google/baidu(uudu)
volo_d4_224 85.89 97.54 192.8M 42.9G 224 1.0 bicubic google/baidu(vcf2)
volo_d4_448 86.70 97.85 192.8M 172.5G 448 1.0 bicubic google/baidu(nd4n)
volo_d5_224 86.08 97.58 295.3M 70.6G 224 1.0 bicubic google/baidu(ymdg)
volo_d5_448 86.92 97.88 295.3M 283.8G 448 1.0 bicubic google/baidu(qfcc)
volo_d5_512 87.05 97.97 295.3M 371.3G 512 1.15 bicubic google/baidu(353h)

*The results are evaluated on ImageNet2012 validation set.

Notebooks

We provide a few notebooks in aistudio to help you get started:

*(coming soon)*

Requirements

Data

ImageNet2012 dataset is used in the following folder structure:

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume the downloaded weight file is stored in ./volo_d5_224.pdparams, to use the volo_d5_224 model in python:

from config import get_config
from volo import build_volo as build_model
# config files in ./configs/
config = get_config('./configs/volo_d5_224.yaml')
# build model
model = build_model(config)
# load pretrained weights, .pdparams is NOT needed
model_state_dict = paddle.load('./volo_d5_224')
model.set_dict(model_state_dict)

Evaluation

To evaluate VOLO model performance on ImageNet2012 with a single GPU, run the following script using command line:

sh run_eval.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
    -cfg='./configs/volo_d5_224.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./volo_d5_224'
Run evaluation using multi-GPUs: 
sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/volo_d5_224.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./volo_d5_224'

Training

To train the VOLO model on ImageNet2012 with single GPU, run the following script using command line:

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
  -cfg='./configs/volo_d5_224.yaml' \
  -dataset='imagenet2012' \
  -batch_size=32 \
  -data_path='/dataset/imagenet' \
Run evaluation using multi-GPUs: 
sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/volo_d5_224.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \

Visualization Attention Map

(coming soon)

Reference

@article{yuan2021volo,
  title={Volo: Vision outlooker for visual recognition},
  author={Yuan, Li and Hou, Qibin and Jiang, Zihang and Feng, Jiashi and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2106.13112},
  year={2021}
}