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I just cloned this repo and try to run codes with provided instructions. (the code is not modified.)
Environment: cuda 11.3, python 3.9.6, pytorch 1.9.0, torchvision 0.10.0, installed via miniconda.
I run python train_mnist.py --fname ./new_result.pth to get a model,
and then run python evaluate_mnist.py --fname ./new_result.pth to evaluate the robustness.
and run python evaluate_mnist.py --fname ./new_result.pth --attack none to evaluate the clean accuracy.
The result shows that robustness=0.00% and accuracy=97.71%, meaning the trained model is not robust at all.
However, using your pretrained model in models/fgsm.pth brings a robust model. (robustness=88.38% and accuracy=98.50%)
Could you provide any comment on how to reproduce your pretrained results?
The text was updated successfully, but these errors were encountered:
I just cloned this repo and try to run codes with provided instructions. (the code is not modified.)
Environment:
cuda 11.3, python 3.9.6, pytorch 1.9.0, torchvision 0.10.0
, installed via miniconda.I run
python train_mnist.py --fname ./new_result.pth
to get a model,and then run
python evaluate_mnist.py --fname ./new_result.pth
to evaluate the robustness.and run
python evaluate_mnist.py --fname ./new_result.pth --attack none
to evaluate the clean accuracy.The result shows that
robustness=0.00%
andaccuracy=97.71%
, meaning the trained model is not robust at all.However, using your pretrained model in
models/fgsm.pth
brings a robust model. (robustness=88.38%
andaccuracy=98.50%
)Could you provide any comment on how to reproduce your pretrained results?
The text was updated successfully, but these errors were encountered: