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About the training loss #21
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I was training a face recognition with SAM (backbone is ResNet, and the loss is arcface). Firstly, the backbone load a pretrianed model, and then train the classifier while freeze the backbone. Finally, I train the whole model with SAM. But something wired happens:
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Most likely, the BN freezing won't make a significant difference, so I would advise you to not focus on that until you fix the convergence issue. I guess the losses should be of similar magnitude, but I don't see a problem if one is slightly larger than the other one. Does your model converge with a standard optimizer? Have you tried different hyperparameters? |
My mode will converge with standard optimizer, but not with sam.
Before the second forward, I will save the |
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
During the training process, is the first loss larger than the second loss? But my situation is the opposite.
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