| CIFAR-100 (test) | Vision Transformer (ViT-H/14) | Accuracy94.55 | | 3,518 | 3d ago |
| CIFAR-10 (test) | Efficient Adaptive Ensembling | Accuracy99.612 | | 3,381 | 3d ago |
| ImageNet-1K 1.0 (val) | RevCol-H | Top-1 Accuracy90 | | 1,866 | 3d ago |
| ImageNet 1k (val) | CoCa | Top-1 Accuracy91 | | 1,453 | 2d ago |
| ImageNet (val) | VOLO-D5 | Top-1 Acc87.1 | | 1,206 | 3d ago |
| CIFAR-10 (test) | DeiT-B | Accuracy99.1 | | 906 | 3d ago |
| MNIST (test) | | Accuracy99.35 | | 882 | 3d ago |
| ImageNet 1K (val) | ViT-22B | Top-1 Accuracy89.5 | | 840 | 3d ago |
| ImageNet-1K | | Top-1 Acc91 | | 836 | 2d ago |
| ImageNet 1k (test) | Model Soups | Top-1 Accuracy91 | | 798 | 2d ago |
| ImageNet 1k (val) | DRiFt_Temps | Top-1 Acc94.8 | | 706 | 3d ago |
| CIFAR-100 (val) | Full fine-tuning | Accuracy91.8 | | 661 | 3d ago |
| CIFAR-100 | EVA-CLIP-18B | Top-1 Accuracy93.8 | | 622 | 2d ago |
| CIFAR10 (test) | WiSE-FT | Accuracy99.5 | | 585 | 3d ago |
| Fashion MNIST (test) | Zhong et al. | Accuracy96.35 | | 568 | 2d ago |
| ImageNet A | Best model on each test set (oracle) | Top-1 Acc94.47 | | 553 | 2d ago |
| Clothing1M (test) | SOMNet | Accuracy82.1 | | 546 | 3d ago |
| ImageNet-1k | ViT-e/14 | Top-1 Acc90.9 | | 524 | 3d ago |
| ImageNet-1K (val) | CoAtNet-7 | Top-1 Accuracy90.9 | | 512 | 3d ago |
| CIFAR-10 | | Accuracy100 | | 507 | 3d ago |
| EuroSAT | PaRaMS | Accuracy99.81 | | 497 | 2d ago |
| Food-101 | AG-Net | Accuracy99.3 | | 494 | 2d ago |
| DTD | iSICE | Accuracy88.9 | | 487 | 3d ago |
| ImageNet V2 | Best model on each test set (oracle) | Top-1 Acc84.84 | | 487 | 2d ago |
| Flowers102 | DoRA (r=8) | Accuracy99.3 | | 478 | 2d ago |
