Out-of-Distribution Detection on NINCO (test)
67.48AUROCOCO
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| OCOBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 67.48 | — | — | 71.41 | |
| OCOBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 66.99 | — | — | 74.19 | |
| FDBDBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 65.26 | — | — | 70.15 | |
| MaxLogitBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 64.5 | — | — | 78.15 | |
| CoRPBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 64.23 | — | — | 74.37 | |
| FDBDBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 64.18 | — | — | 79.58 | |
| NECOBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 64.07 | — | — | 78.74 | |
| SCALEBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 63.58 | — | — | 78.49 | |
| EnergyBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 63.5 | — | — | 78.94 | |
| OODDBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 61.6 | — | — | 78.45 | |
| MaxLogitBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 61.16 | — | — | 76.42 | |
| CoRPBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 61.02 | — | — | 80.04 | |
| NECOBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 60.99 | — | — | 76.48 | |
| EnergyBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 59.53 | — | — | 77 | |
| SCALEBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 59.17 | — | — | 76.75 | |
| OODDBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 59.13 | — | — | 70.35 | |
| SHEBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 58.78 | — | — | 88.36 | |
| NNguideBackbone=ViT, Training Dataset=ImageNet-1k2026.05 | 58.25 | — | — | 83.9 | |
| NNguideBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 52.53 | — | — | 84.14 | |
| SHEBackbone=DINOv2, Training Dataset=ImageNet-1k2026.05 | 48.23 | — | — | 92.94 | |
| RMDS2026.03 | 0.675 | 0.7028 | 0.882 | — | |
| KLM2026.03 | 0.6726 | 0.7414 | 0.922 | — | |
| ASH2026.03 | 0.6638 | 0.7074 | 0.873 | — | |
| MSP2026.03 | 0.6493 | 0.7237 | 0.783 | — | |
| TempScale2026.03 | 0.646 | 0.7155 | 0.761 | — | |
| ReAct2026.03 | 0.6451 | 0.7297 | 0.849 | — | |
| SHE2026.03 | 0.6427 | 0.8047 | 0.844 | — | |
| GradNorm2026.03 | 0.6406 | 0.8545 | 0.869 | — | |
| RAS HL2026.03 | 0.6406 | 0.7048 | 0.758 | — | |
| RAS2026.03 | 0.6317 | 0.7097 | 0.732 | — | |
| GEN2026.03 | 0.6273 | 0.7238 | 0.714 | — | |
| MLS2026.03 | 0.6143 | 0.7543 | 0.692 | — | |
| Gram2026.03 | 0.6063 | 0.8624 | 0.767 | — | |
| ODIN2026.03 | 0.6059 | 0.804 | 0.691 | — | |
| DICE2026.03 | 0.6029 | 0.7987 | 0.733 | — | |
| OpenMax2026.03 | 0.6028 | 0.7641 | 0.719 | — | |
| EBO2026.03 | 0.6028 | 0.7615 | 0.668 | — | |
| KNN2026.03 | 0.5986 | 0.7477 | 0.662 | — | |
| VIM2026.03 | 0.5912 | 0.7785 | 0.661 | — | |
| MDS2026.03 | 0.5283 | 0.8689 | 0.581 | — | |
| MDSEns2026.03 | 0.478 | 0.9356 | 0.514 | — | |
| RankFeat2026.03 | 0.4037 | 0.9621 | 0.467 | — |