OOD Detection on iNaturalist OOD
99.16AUROCGradPCA
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| GradPCABackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 99.16 | 3.74 | |
| MahalanobisBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 99.09 | 4.75 | |
| EnergyBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 97.9 | 11.29 | |
| ASH-SBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 97.87 | 11.49 | |
| KNNBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 97.63 | 12.48 | |
| Max logitsBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 97.54 | 13.27 | |
| ODINBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 97.54 | 13.27 | |
| ASH-BBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 97.32 | 14.21 | |
| ReActBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 97.29 | 14.9 | |
| DICEBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 96.73 | 15.2 | |
| DICE + ReActBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 96.24 | 18.64 | |
| ReActBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 96.22 | 20.38 | |
| CADetTraining=Supervised, Backbone=ResNet502022.10 | 95.28 | — | |
| MSPBackbone=ViT-B/16, In-Distribution Dataset=ImageNet-1k2025.05 | 94.92 | 22.05 | |
| DICEBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 94.49 | 25.63 | |
| ASH-BBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 94.28 | 31.46 | |
| DICE + ReActBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 93.57 | 32.3 | |
| ASH-PBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 92.51 | 44.57 | |
| ASH-SBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 91.94 | 39.1 | |
| ReActBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 91.53 | 42.4 | |
| DICEBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 90.83 | 43.09 | |
| KL MatchingTraining=Supervised, Backbone=ResNet502022.10 | 90.48 | — | |
| ASH-PBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 90.46 | 54.92 | |
| Energy scoreBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 89.95 | 55.72 | |
| ODINBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 89.66 | 47.66 | |
| MahalanobisTraining=Supervised, Backbone=ResNet502022.10 | 89.48 | — | |
| ViMTraining=Supervised, Backbone=ResNet502022.10 | 89.26 | — | |
| Energy scoreBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 88.91 | 59.5 | |
| MSPTraining=Supervised, Backbone=ResNet502022.10 | 88.58 | — | |
| Softmax scoreBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 87.74 | 54.99 | |
| ODINBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 87.62 | 55.39 | |
| ReActTraining=Supervised, Backbone=ResNet502022.10 | 87.27 | — | |
| ODINTraining=Supervised, Backbone=ResNet502022.10 | 86.48 | — | |
| MaxLogitTraining=Supervised, Backbone=ResNet502022.10 | 86.42 | — | |
| Softmax scoreBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 85.32 | 64.29 | |
| ResidualTraining=Supervised, Backbone=ResNet502022.10 | 84.63 | — | |
| CADetTraining=Self-supervised (contrastive), Backbone=ResNet502022.10 | 83.42 | — | |
| MahalanobisBackbone=MobileNet, In-Distribution Dataset=ImageNet-1k2022.09 | 81 | 62.11 | |
| EnergyTraining=Supervised, Backbone=ResNet502022.10 | 80.5 | — | |
| MahalanobisBackbone=ResNet, In-Distribution Dataset=ImageNet-1k2022.09 | 52.65 | 97 |