From 958aa33b7a92df79e094bdd1ddeb2e92ba466184 Mon Sep 17 00:00:00 2001 From: Yang Gu Date: Thu, 9 Jul 2020 15:04:57 +0800 Subject: [PATCH] refine torchvision_tutorial doc for windows --- intermediate_source/torchvision_tutorial.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst index 93fcfd3d247..703deeab11c 100644 --- a/intermediate_source/torchvision_tutorial.rst +++ b/intermediate_source/torchvision_tutorial.rst @@ -56,6 +56,11 @@ If your model returns the above methods, they will make it work for both training and evaluation, and will use the evaluation scripts from ``pycocotools``. +.. note :: + For Windows, please install ``pycocotools`` from `gautamchitnis `__ with command + + ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI`` + One note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class, you should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can define ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has booth classes, your ``labels`` tensor should look like ``[1,2]``. Additionally, if you want to use aspect ratio grouping during training