Because while training, it is a lot faster if I feed with a big batch size like 512.
But on production I wont always have 512 images so I may call it with single image, 4 images or any amount.
I wonder how ResNet implementation is doing it because with ResNet I can train with any batch size and call with any other batch size.
The workaround I have found for now is I train with 512 batch size and while calling on prod I fill the missing part with replicate data.
But this is not a good solution.
Thanks anyways;
I will dig into ResNet implementation later to see how it is doing.