How do I derive weights for CrossEntropy Loss on my custom dataset?

vision
1

I have five classes + 1 background for my semantic segmentation. My mask is like the one below:
It has a ton of background and classes are rarely seen on both image.
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I’ve seen codes using CrossEntropyLoss with designated weights but usually they are using available datasets such as CityScapes so they have the weights on them.

How do I derive CrossEntropyLoss weights for my dataset?

2

This should work:
(Average number of pixels per image) / (Average number of pixels of a class per image)

Haven’t worked a lot on segmentation, so take the suggestion lightly

3

are weights in the losses BCE and CE designated PER minibatch? or is it computed on the whole dataset and assigned before the training?

4

Usually, it is computed per dataset.

You can do per batch as well, but that would result in a very noisy training process, since the class priorities for the network might change per batch.