Malav Bateriwala

Hey @sravanjosh07 , I dont think tf or pytorch solved it. So i just let it go.

Thanks. You are having issues while loading the data. ''for x, y in train_loader:'' The pytorch dataloader is not able to access the next batch or data in your input...

Check your input data shape. I think this error points to the 1st channel input data. Check how the data I have provided and try to replicate it to run...

Check the shape of the Input and the Broadcast shape. They need to be same. You are using a Gray scale image with 1 channel, change it to 3 channel...

Look into the Dice Score Metric: def dice_coeff(im1, im2, empty_score=1.0): im1 = np.asarray(im1).astype(np.bool) im2 = np.asarray(im2).astype(np.bool) if im1.shape != im2.shape: raise ValueError("Shape mismatch: im1 and im2 must have the same...

if you are using cv resized = cv2.resize(img, shape)

I think 50 epochs should be good enough to check if the results are coming in the right direction by checking the gen_images after each epoch. There is early stopping...

It can be due to data transformation uses a single channel. Let me know if you are still using it and facing this issue.

Check the data_loader inputs for transformation. The std and mean requires 3 channel. If you want to use the same transformation for labels then you will need to convert it...

It can be due to model not learning anything while using any RNN type model. | Does this happen while using nested Unet?