Hi,
I have adapted a U-NEt gist 6 to reproduce the same problem that I am experimenting. The fact is that when classes in labels are encoded as binary channels (10 channels in the gist), we can use BCELoss. However, I am not sure on how to extract which class has the maximum probability from the model output when CrossEntropyLoss is used. Any hint?