Comments (2)
Hi, I have figured out that the learning rate scheduler "CosineAnnealingLR" has an impact on both training and testing accuracy, which can make the model more converged at the end of the training.
After I added it back to my code, I can achieve about 93% on ResNet18, so maybe you try that.
from pytorch-cifar.
Same here. I also got about 85% on the test set with ResNet 18. I haven't figured out the reason yet.
Have you addressed this issue? Thanks
from pytorch-cifar.
Related Issues (20)
- pytorch-cifar复现问题 HOT 4
- MobileNetV2 training does not converge HOT 1
- The test set is being used as validation set HOT 6
- 对于精度有疑问 HOT 2
- pre-train weights
- request: epochs numbers to converge in readme.md HOT 1
- ResNet18 performs much better than expected! HOT 5
- how to train my own dataset and classes? HOT 1
- [Question] efficientnet performance HOT 1
- Error when loading the weights on CPU but trained on GPU HOT 1
- Errors when testing on CPU HOT 11
- checkpoint
- ValueError: not enough values to unpack (expected 2, got 0) HOT 3
- load state_dict HOT 1
- Performance degradation when loading a model after saving it HOT 2
- A problem in ShuffleNet
- How long will it take you to train the cifar10 model? HOT 1
- Overfitting
- Adding more flags to main.py
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from pytorch-cifar.