I’m using BCEWithLogitsLoss to optimise my model, and Dice Coefficient loss for evaluating train dice loss & test dice loss. However, although both my train BCE loss & train dice loss decrease after each epoch, my test dice loss doesn’t and plateaus early on. I have already tried batch norm and dropout, as well as experimented with both Adam and SGD using from 1 to 0.00001 as the learning rate. The only difference between experiments is that the value at which the loss plateaus is different, but all experiments plateau at approx the 1st epoch.
Notably, as my dataset size is extremely small (100 images in total), I opted for a 80-20 train-test split, and augmented the trainset (via random cropping, rotations, etc) to 640 images. Could my problem be related to the limited dataset size?
Here are my training graphs (first 30 epochs) for reference: