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after working with your code, I might have found a critical bug during training/testing.
As far as I understood, the classification weights close to 0 should be clamped to be at least 1e-3. This is done in main.py:236, train.py:91 and test.py:49.
The problem here is, that even if weights are bigger than 1e-3, they will get reduced by 1e-3. Since this is done in the loops over the train/test loaders, the weights will be reduced many times, obviously leading to wrong classification results.
I especially noticed the effects in the test.py as my accuracy dropped dramatically. I'm currently extending PIPNet to segmentation, and my mIoUs dropped to near chance. I haven't looked into the effects for your default PIPNet configuration for classification.
Hope this helps!
Best
Max
The text was updated successfully, but these errors were encountered:
Hi Meike,
after working with your code, I might have found a critical bug during training/testing.
As far as I understood, the classification weights close to 0 should be clamped to be at least 1e-3. This is done in main.py:236, train.py:91 and test.py:49.
The problem here is, that even if weights are bigger than 1e-3, they will get reduced by 1e-3. Since this is done in the loops over the train/test loaders, the weights will be reduced many times, obviously leading to wrong classification results.
I especially noticed the effects in the test.py as my accuracy dropped dramatically. I'm currently extending PIPNet to segmentation, and my mIoUs dropped to near chance. I haven't looked into the effects for your default PIPNet configuration for classification.
Hope this helps!
Best
Max
The text was updated successfully, but these errors were encountered: