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Image Classification on ImageNet-100C (test)

23.671mCEREVa
22.339631.3265540.313549.30045Sep 23, 2025

Evaluation results

MethodLinks
REVaBackbone=Swin_V2_B2025.0923.671————————————————————
AugMixBackbone=Swin_V2_B2025.0927.543————————————————————
StandardBackbone=Swin_V2_B2025.0930.123————————————————————
REVaBackbone=ResNet-182025.0939.379————————————————————
AugMixBackbone=ResNet-182025.0946.536————————————————————
StandardBackbone=ResNet-182025.0956.956————————————————————
AugMixBackbone=ResNet-182021.12————————————————————60.7
DABackbone=ResNet-182021.12————————————————————67.7
OursEvaluation Protocol=Mean top-1 accuracy across 5 corruption levels2021.10—56.8850.5244.3750.6943.6960.461.0875.6556.8154.1156.7472.5754.251.739.862.6665.9559.5266.28—
PRIMEBackbone=ResNet-182021.12————————————————————71.6
PRIMEBackbone=ResNet-18, Training Epochs=1.5x2021.12————————————————————72.5
StandardBackbone=ResNet-182021.12————————————————————49.7
Supervised BaselineEvaluation Protocol=Mean top-1 accuracy across 5 corruption levels2021.10—61.7649.1559.385958.6167.8577.7573.0655.8351.0553.7686.0167.5659.5853.9764.3182.1671.962.65—