We usually extract the feature just the relu layer before the pooling layer in vgg19, but which layer I should pick for extracting the feature from Resnet 50? Maybe if you can give me some suggestions on how I should do it in code and picking layer in Resnet50? I am a newbie who is still keeping Deep learning in Pytorch in 2 months.
Pytorch VGG19 allows to access with index for extracting the feature layer, but Resnet does not. I can try using for loop, but I am not sure it will work or not.
Here is the project that I want to extract the feature to redraw, but it is not working great that I just use 3 layers out of 5 relu layers in vgg19. I am planning to train it again in Resnet50 on Colab.