Deep-Learning-with-PyTorch

Summary
403
• It is possible to train a segmentation model on image crops while validating on
whole-image slices. This flexibility can be important for class balancing.
• Loss weighting is an emphasis on the loss computed from certain classes or subsets
of the training data, to encourage the model to focus on the desired results.
It can complement class balancing and is a useful tool when trying to tweak
model training performance.
• TensorBoard can display 2D images generated during training and will save a
history of how those models changed over the training run. This can be used to
visually track changes to model output as training progresses.
• Model parameters can be saved to disk and loaded back to reconstitute a model
that was saved earlier. The exact model implementation can change as long as
there is a 1:1 mapping between old and new parameters.