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The paper referenced by Martin Thoma is the go-to for semantic segmentation. However I will also like to add the Panoptic Segmentation metric as an aggregated method to measure both the detection task and segmentation task of the model. It is a very well-known and widely used metric since it is the standard metric for COCO dataset (segmentation) This is the ...


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You can create a mapping from classes to colors for a simple one is: y # y.shape = (W, H, n_classes) _, y_color = y.max(dim=-1, keepdim=True) / n_classes # y_color.shape = (W, H, 1) y_color = torch.cat([y_color] * 3, dim=-1) # y_color.shape = (W, H, 3) (using pytorch like code) This mapping is visualizable, of course, you may get nicer visualizations if ...


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