Hyperparameters for training Resnet18 on CIFAR10?
kshitijsachan opened this issue · comments
Thanks for making this benchmark! You have provided great documentation.
In the trojanzoo paper, you have a table showing that you trained resnet18 on CIFAR10 with 95.37% accuracy? Do you have the hyper parameters for that training run? (e.g. did you use resnet18
, resnet18_ap_comp
, resnet18_comp
, or resnet18_s
, etc.) I've tried a few different configurations and have been unable to train a model with >90% accuracy.
I've also had trouble reproducing your results in Table 10 of the paper. I am generally getting better performance for STRIP and much worse performance on NEO than you reported, but I would guess this is because I am using different hyper parameters/models. Perhaps you still have the pretrained attacked model weights that you could share? Thanks.
python ./examples/train.py --color --verbose 1 --dataset cifar10 --model resnet18_comp --lr_scheduler --cutout --save