Hi, community,
I am trying to train my Unet model with a multiclass dataset. My ground-truth are grayscale, not normalized and my masks are grayscale in range (0-3) with pixels equal to each class (0=background, 1=tissue1, 2=tissue2, 3=tissue3). In this case, should I make a mapping of the classes? How can I use the softmax and argmax in the evaluation?
I am asking this because my model is predicting very badly. With Adam optimizer, he is all black until epoch 900, at 900 he predicts the background and the perfect format of the tissue3 (but with the pixels value changed, as pixel=1). With SGD optimizer he stars with a reasonable segmentation, but in the 200 epoch, he is all background. I have no idea what is happening. I hope someone could help me.
Best Regards,
Giulia