Compute validation loss for Faster RCNN

vision
6

@mapostig No, I guess it’s not a good practice to use model.train() mode in evaluation. You can use the same custom dataset class to create a different dataset loader for your evaluation dataset.

for phase in ['train','val']:
    if phase == 'train':
        model.train()
        #Training Part with backprog
    else:
        model.eval()
        #Just a forward Pass.

Some layers like Dropout and batch Norm will behaves differently under model.eval().
Further you can look to this discussion.

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