The images have to be loaded in to a range of [0, 1] and then
normalized using `mean=[0.485, 0.456, 0.406]` and `std=[0.229, 0.224, 0.225]`
An example of such normalization can be found in `the imagenet example here` <https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>
An example of such normalization can be found in the imagenet example `here <https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>`__
Transforms
==========
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@@ -410,7 +410,7 @@ computing the ``(min, max)`` over all images.
``pad_value=<float>`` sets the value for the padded pixels.
`Example usage is given in this notebook` <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>
Example usage is given in this `notebook <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`__