document normalization for pre-trained models

README.rst

@@ -271,6 +271,15 @@ These can be constructed by passing ``pretrained=True``:
     squeezenet = models.squeezenet1_0(pretrained=True)
 
 
+All pre-trained models expect input images normalized in the same way, i.e.
+mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected
+to be atleast 224.
+
+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>
+
 Transforms
 ==========
 
@@ -395,7 +404,7 @@ normalize the image.
 scale_each=True will scale each image in the batch of images separately rather than
 computing the (min, max) over all images.
 
-[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>
 
 save\_image(tensor, filename, nrow=8, padding=2, normalize=False, range=None, scale\_each=False)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~