OOD Detection on NINCO
46.22FPR95RMDS
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| RMDSBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 46.22 | 87.31 | |
| EPDBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 48.06 | 85.74 | |
| MDSBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 48.76 | 86.52 | |
| Maha++Backbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 50.29 | 88.42 | |
| rMaha++Backbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 50.29 | 88.42 | |
| rMahaBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 50.94 | 88.28 | |
| X-MahaBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 51.75 | 88.71 | |
| MahaBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 51.89 | 87.95 | |
| FDBDModel=DINOv22026.05 | 54.41 | 81.52 | |
| KNNBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 54.73 | 82.25 | |
| SHEBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 56.01 | 84.18 | |
| rMaha++Backbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 56.21 | 86.32 | |
| ViMBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 57.45 | 84.64 | |
| OCOModel=DINOv22026.05 | 57.74 | 81.27 | |
| Maha++Backbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 57.79 | 85.87 | |
| OODDModel=DINOv22026.05 | 58.68 | 81.3 | |
| GENBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 59.31 | 82.51 | |
| CoRPModel=DINOv22026.05 | 59.36 | 80.31 | |
| MM++Backbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 60.07 | 85.55 | |
| OCOModel=ViT2026.05 | 60.77 | 81.84 | |
| SHEModel=DINOv22026.05 | 62.23 | 79.76 | |
| MSPBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 62.28 | 80.99 | |
| EnergyBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 62.35 | 75.62 | |
| ReActBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 62.35 | 76.17 | |
| NECOModel=DINOv22026.05 | 63.64 | 77.19 | |
| MaxLogitModel=DINOv22026.05 | 63.75 | 77.08 | |
| SCALEModel=DINOv22026.05 | 63.98 | 75.83 | |
| EnergyModel=DINOv22026.05 | 64.65 | 75.78 | |
| MaxLogitModel=ViT2026.05 | 66.19 | 79.92 | |
| SCALEModel=ViT2026.05 | 66.54 | 79.75 | |
| EnergyModel=ViT2026.05 | 67.39 | 79.17 | |
| OODDModel=ViT2026.05 | 67.52 | 77.12 | |
| FDBDModel=ViT2026.05 | 68.9 | 79.15 | |
| CoRPModel=ViT2026.05 | 70.42 | 72.63 | |
| rMahaBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 70.5 | 83.59 | |
| MSPBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 70.52 | 80.3 | |
| ReActBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 72.22 | 78.62 | |
| EnergyBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 72.42 | 77.1 | |
| ODINBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 72.8 | 71.31 | |
| MM++Backbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 72.87 | 82.06 | |
| NNguideModel=ViT2026.05 | 75.05 | 70.65 | |
| NNguideModel=DINOv22026.05 | 75.43 | 67.77 | |
| MSPBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 77.35 | 78.11 | |
| ReActBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 78.5 | 75.43 | |
| MahaBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 79.76 | 80.46 | |
| KNNBackbone=EVA02-S14, ID Dataset=ImageNet-LT2026.06 | 80.39 | 72.75 | |
| DICEBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 81.09 | 71.67 | |
| TempScaleBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 81.9 | 77.8 | |
| SHEModel=ViT2026.05 | 82.23 | 71.16 | |
| NECOModel=ViT2026.05 | 83.04 | 79.28 | |
| ODINBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 85.41 | 67.68 | |
| OpenMaxBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 88.36 | 78.68 | |
| MLSBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 92.98 | 72.4 | |
| EBOBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 94.16 | 66.02 | |
| KNNBackbone=Swin-T, In-Distribution (ID) Dataset=ImageNet-LT2026.06 | 95.3 | 43.16 | |
| ASHBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 95.4 | 52.52 | |
| GradNormBackbone=ViT-B/16, ID Accuracy=81.14%, ID Dataset=ImageNet-1K2026.04 | 95.81 | 64.4 |