I agree that if you skip normalisation (to normal distribution) and feed the raw ImageNet dataset, the performance will be worse for ImageNet but I am not too sure whether is that true for any other dataset.



 ptrblck:

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).


(Test 1): To test the above.



 ptrblck:

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?


(Test 2): To test the above. My theory is that the ImageNet stats is useless because even if the dataset consist of similar categories of images to ImageNet, the custom stats of ImageNet does not in any way correlate due to the fact that it has way more categories (ex: 3 same categories vs 997 categories that are non-existent in your own dataset).
I will figure out how to best verify the 2 tests, get back to this post when I have done so.  Thank you for the reply, you are really active in this forum!