yeah,after conjecture and observe many times, I argue that the network is able to catogorize those background pixel to the background,but unable to distinguish pixel inside the foreground area,I really don’t know why.
the new model is totally re-initlialized so it descent again, with no memory about the old model.
I  split the dataset into train/test by 9:1, numerically count 2700:300, and the loss is approaching 0.05,
the old model is approaching 0.019,for those checkerboard artifacts influence the loss descent.
now the new model’s loss is influenced by those even pixels ,they only occur inside the foreground area.now the loss is hardly descent more.