Hi @lauriat, I am getting similar blurry results for segmentation even when using a more complex network.

Is your prediction target only 3 channels (RGB)?. I initially tried the same approach where each of the 3 channels to predict were the original RGB channels and I think this might be one of the main reasons why. Loss function  BCELoss for segmentation expects each channel to be either 0 or 1 (binary) where 1 will be the location of the specific class or color in your case in the image, however, rgb images will contain values from 0 to 1 including fractions at each pixel in the image. I am now trying a different approach by breaking/splitting the image into multiple channels where each will represent a specific color to use these as prediction target. I have not yet obtained results but will notify you if I make some progress.