Hi Frank,
Thanks for your thorough answer!
One thing I don’t understand is this:
You say that:

Each “pixel” of your output tensor (your
model’s prediction) will be a “raw-score” logit that runs from -inf
to +inf. As you can see your model’s predictions that get fed into
your BCEWithLogits and your targets that also get fed into your
loss function are indeed of the same shape.

So, I suppose that after the loss has been calculated, we do some backpropagation and optimizer step based on this loss, and this improves our model. But if our model outputs these “logits” - then how can we end with something that looks like our label image/mask (0s and 1s), so that we can actually plot the “image” output and see for ourselves how close it is to the labels?
Hope I’m making sense.
Sincerely, Hubert.