cszer

1. compress backbone predtrained on imagenet firstly after fine tune on imagenet , it's most computational part of every detector. I think it's more appropriate approach

overfitting

About ram, I add some changes to dataset (close all h5 using tables after training tensors are created + deepcopy to some variables) -> ram usage now not more than...

Thanks for answers!

I tested models on 224х224 torch.rand tensor

This due to mistakes in implementation. In paper mentioned that authors borrowed much code from RepVGG. At train time i think it is much more than vanilla ResNet but after...