- fix kernal size = 3 problem
- writing fine-tune part
- read the pytorch lightning pruning part
- change the code to before
- try the resample, padding and interplot method
- try resample + cropping
- do cropping and patch-based
- adding data augmentation fine tune
- adding one more layer without concat
- try dice and BCE with weight
- try denseUnet
- Be more careful with the learning rate
- try leave one out
- select value module type = 1
- write the predict part, and using the mp4 part
- fix mp4 problem
- update the mp4 quality
- when test, the label do not do the data augmentation resize part
- tune the data agumentation's parameters
- fix dice score = inf problem
- make the epoch more smaller / make the every_epoch_avl = fraction
- write test part on the 1069 data
- why there is a warning in mp4 part
- make it into a pip package
- look at the data after cropping and resize, and directly resize's difference?
- why every time train, the train loss would suddenly go up a lot after a long time?
- reading nnUnet again
- read about nnUnet learning rate