Image Classification on CIFAR-10 resized to 224 × 224 (test)
97.4AccuracyBEiT large
Evaluation Results
| Method | Links | |
|---|---|---|
| BEiT largeepsilon (ε)=8, pretrained_dataset=ImageNet21k, parameters=303M, clipping_method=mixed ghost clipping2022.05 | 97.4 | |
| BEiT largeepsilon (ε)=4, pretrained_dataset=ImageNet21k, parameters=303M, clipping_method=mixed ghost clipping2022.05 | 97.2 | |
| BEiT largeepsilon (ε)=2, pretrained_dataset=ImageNet21k, parameters=303M, clipping_method=mixed ghost clipping2022.05 | 97.1 | |
| BEiT largeepsilon (ε)=1, pretrained_dataset=ImageNet21k, parameters=303M, clipping_method=mixed ghost clipping2022.05 | 96.7 | |
| De et al.epsilon (ε)=8, pretrained_dataset=ImageNet1k2022.05 | 96.6 | |
| CrossViT baseepsilon (ε)=8, pretrained_dataset=ImageNet1k, parameters=104M, clipping_method=mixed ghost clipping2022.05 | 96.5 | |
| CrossViT baseepsilon (ε)=4, pretrained_dataset=ImageNet1k, parameters=104M, clipping_method=mixed ghost clipping2022.05 | 96.2 | |
| De et al.epsilon (ε)=4, pretrained_dataset=ImageNet1k2022.05 | 96.1 | |
| CrossViT baseepsilon (ε)=2, pretrained_dataset=ImageNet1k, parameters=104M, clipping_method=mixed ghost clipping2022.05 | 96.1 | |
| CrossViT baseepsilon (ε)=1, pretrained_dataset=ImageNet1k, parameters=104M, clipping_method=mixed ghost clipping2022.05 | 95.5 | |
| De et al.epsilon (ε)=2, pretrained_dataset=ImageNet1k2022.05 | 95.4 | |
| Yu et al.epsilon (ε)=2, pretrained_dataset=ImageNet1k2022.05 | 94.8 | |
| De et al.epsilon (ε)=1, pretrained_dataset=ImageNet1k2022.05 | 94.8 | |
| Yu et al.epsilon (ε)=1, pretrained_dataset=ImageNet1k2022.05 | 94.3 | |
| Tramer et al.epsilon (ε)=2, pretrained_dataset=ImageNet1k2022.05 | 92.7 |