How to set weights for 38 number of classes individually in Cross Entropy Loss, via pytorch

[KFrank]

Hello Fikrat!

@Fikrat

my target is load more weights for colourful pixels,but less weight for black pixel.

I answered this exact question in my reply to your previous post:

How to implement weighted Cross Entropy Loss for lane marking segmentation

Hi Fikrat! If your problem is one a binary segmentation, presumably black = background, color = foreground, then you should use BCEWithLogitsLoss and the pos_weight argument that you pass to its constructor. If it’s multi-class segmentation, e.g., black = background, some colors or patterns = lane 1, some other colors or patterns = lane 2, etc., then you should use CrossEntropyLoss and the weight argument passed to its constructor. Best. K. Frank

If this doesn’t work for you or your have further questions, it would
probably make the most sense to move the discussion back to that
earlier thread.

Best.

K. Frank