ToughStoneX
commented
2 years ago Hi. Could provide more details in the training? For example, the curve of each term of loss in Tensorboard.
Here are some simple suggestions for debugging your code:
1, Try to replace the self-supervision loss with supervision loss in the train.py, and use train.sh to train the model. If this works fine, then the problem must locate in the self-supervision loss.
2, Try to use only the photometric consistency loss in the train.py, and use train.sh to train the model. If this works, then the problem must locate in the other losses in JDACS.
3, The rest can be done in the same manner, until you find what code leads to the corruption in self-supervision training.
4, My empirical experience is that the augmentation loss sometimes leads to corrupt in self-supervision training, because when the data-augmentation is too hard, the self-supervision may fall into trivial solution.
Be997398715
TWang1017
Hi, thanks for your great job! I'd like to replace cvp-mvsnet to patchmatchnet and train the new model. However I found the model loss can't converge to a low level(about 30), is there any suggestions for train a new model? The training strategies are same as train.sh your provided(the only one change is scale set from 2 to 3).