Hey all! I’m working with a dataset for segmentation that has your typical RGB images as well as index-based masks (so the pixel values in the mask represent the classes at those positions), and I’m wondering what the best way to train this dataset would be with a simple U-Net. Would it be better to do standard CrossEntropy loss using the index masks, or would it be better to split the masks up into one hot vector format and use BCELoss with each channel? Or is there a better loss for this type of dataset entirely? I’ve seen dice loss, but no efficient implementations for multi-class scenarios.