Hello ,everyone, I was trying to do some segenmentation jobs with Unet. I have only 10 Images , and I use 8 of them as train Images, 2 of them as test. The images are greyscale images, and the mask(groundtruth) are binarized images(black stand for backgrounds, white for objects). I am using almost the most basic Unet. The test result is not so good but works, most of the pixels can be correctly predicted. So I decide to rotate the original Images and perform random crops on the original Images to enlarge the training set so that the test result can be improved. Unfortunatly, the network does not work after those operations. With random crops, the trained network cant even predict a single part(the predicted mask is all black). And with vertical or horizontal flip, the predicted result seems to be wrong, it is a combination of original mask and flipped mask. I have no idea what’s wrong with my implementation. Is Unet not working with those augmentations or something else?
Hope someone can give me some ideas.