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This causes discrepancies with the scores reported in the original papers (DeepAugment, AugMix, Standard RN-50). The ImageNet-C dataset already contains 224x224 images and hence only ToTensor() should be used for consistency.
Fixing prepr='none' in load_imagenetc should solve the issue (assuming all the models are capable of handling 224x224 images as input).
The text was updated successfully, but these errors were encountered:
I see that the ImageNet-C evaluation uses the preprocessing:
Resize(256)+CenterCrop(224)+ToTensor()
.robustbench/robustbench/data.py
Lines 146 to 154 in 61ce9e9
This causes discrepancies with the scores reported in the original papers (DeepAugment, AugMix, Standard RN-50). The ImageNet-C dataset already contains 224x224 images and hence only
ToTensor()
should be used for consistency.Fixing
prepr='none'
inload_imagenetc
should solve the issue (assuming all the models are capable of handling 224x224 images as input).The text was updated successfully, but these errors were encountered: