Thanks for the link. I understand the part more as an explanation on how to use or fine-tune the pretrained model in order to get the same results. E.g. if you skip the normalization and just feed the raw ImageNet dataset into it, the performance will be worse.



 tlim:

This is where I am confused, I cannot figure out why would there be any potential benefit at all from the custom stat.


As already said, you can calculate the custom stats and it would be interesting to see, if you are seeing any benefits. Reusing the ImageNet stats seems to work in practice for “a lot” of use cases but the “lot” depends on your research area (e.g. see the point about medical data from before).



 tlim:

What does ImageNet mean and standard deviation has anything to do with our own datasets ?


The ImageNet dataset used to pretrain the torchvision models contains a variety of “natural” images from 1000 categories. If your custom image dataset contains similar images of e.g. dogs, cats, strawberries, etc. the same stats could work. Did you compute the stats from your images and compared them to the ImageNet ones? If so, how large was the difference?