but instead my first model turned out better even when I added modifcatory blocks, which were meant to improve it. Is that normal? I was using a UNet, a kind of fully convolutional neural network, for semantic segmentation, and I added squeeze-and-excitation and residual blocks to my refined version. However, my basic one turned out better with a higher dice score on the test set.