Comments (3)
hendrycks
commented on July 21, 2024
Here's the training log of a model that trained for 90 epochs.
https://drive.google.com/file/d/1CGHCaK-ol6UjVsBFo-87m18WA6sq25qJ/view?usp=sharing
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seominseok0429
commented on July 21, 2024
Thank you for your kind reply. Do you ever get a lot of performance instability in the early stages of learning?
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hendrycks
commented on July 21, 2024
No. Do you have better luck with this code? #3 (comment)
If so, that that suggests the main code might have an issue, but the code I linked to I have certainly used without instability.
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Related Issues (20)
- Reproduce of Baseline
- A question in "third_party/WideResNet_pytorch/wideresnet.py out = self.relu2(self.bn2(self.conv1(out))) HOT 2
- `depth` is constant each `width` HOT 2
- There is a big gap between the results of the code and the results in the paper on CIFAR10-C HOT 2
- What about the performance on regular classification problems? HOT 5
- KL divergenve HOT 1
- Issue loading pretrained weights HOT 3
- mean and std for every channel is 0.5 instead of (0.5071, 0.4867. 0.4408) as mean and (0.2657, 0.2565, 0.2761) as std HOT 2
- Jensen–Shannon divergence HOT 2
- Corruption acc. of a Resnet50 trained on cifar10 with augmix HOT 2
- augmentations used in augmix HOT 3
- command for imagenet evaluation
- Use combined with Object Detection
- none
- Nan loss for ResNext backbone trained on cifar 100 HOT 1
- torch version 1.2.0 not compatible with GPU version HOT 1
- Little difference between with/without JSD loss HOT 1
- Testing with the best model HOT 4
- Repeated Evaluation in ImageNet HOT 1
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