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refine torchvision_tutorial doc for windows
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intermediate_source/torchvision_tutorial.rst

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@@ -56,6 +56,11 @@ If your model returns the above methods, they will make it work for both
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training and evaluation, and will use the evaluation scripts from
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``pycocotools``.
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.. note ::
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For Windows, please install ``pycocotools`` from `gautamchitnis <https://github.com/gautamchitnis/cocoapi>`__ with command
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``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI``
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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]``.
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Additionally, if you want to use aspect ratio grouping during training

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