Feature Importance Assessment on ImageNet 1k (val)
34.71Insertion ScoreGeneric
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| GenericVariant=Swin, Model=Swin2-B2025.07 | 34.71 | 47.16 | |
| GenericVariant=Swin, Model=Swin-B2025.07 | 33.76 | 42.94 | |
| BeyondVariant=Swin, Model=Swin2-B2025.07 | 31.32 | 49.44 | |
| ViT-CXVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 26.11 | 59.6 | |
| IIAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 25.34 | 47.5 | |
| Chefer-LRPModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 24.82 | 47.04 | |
| IIAModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 24.53 | 43.63 | |
| Chefer-LRPModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 24.44 | 42.8 | |
| BeyondVariant=Swin, Model=Swin-B2025.07 | 23.73 | 50.39 | |
| GenericVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 22.1 | 62.73 | |
| ViT-CXModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 21.58 | 54.06 | |
| IBAVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 21.01 | 65.6 | |
| Chefer-LRPVariant=ViT, Model=ViT-L-16/2242025.07 | 20.97 | 53.81 | |
| IBAVariant=Swin, Model=Swin2-B2025.07 | 20.94 | 53.85 | |
| IBAVariant=ViT, Model=ViT-L-16/2242025.07 | 20.88 | 54.85 | |
| GenericVariant=ViT, Model=ViT-L-16/2242025.07 | 20.17 | 55.54 | |
| IIAVariant=ViT, Model=ViT-L-16/2242025.07 | 20.14 | 55.22 | |
| ViT-CXModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 20.01 | 45.8 | |
| ViT-CXVariant=ViT, Model=ViT-L-16/2242025.07 | 19.76 | 57.38 | |
| BeyondModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 19.75 | 51.82 | |
| BeyondVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 19.34 | 64.27 | |
| BeyondVariant=ViT, Model=ViT-L-16/2242025.07 | 18.99 | 56.55 | |
| ViT-CXVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 18.88 | 50.4 | |
| CoIBAVariant=Swin, Model=Swin2-B2025.07 | 18.88 | 55.77 | |
| ViT-CXVariant=DeiT, Model=DeiT-B-16/2242025.07 | 18.21 | 46.97 | |
| IBAVariant=Swin, Model=Swin-B2025.07 | 18.03 | 52.81 | |
| GenericVariant=ViT, Model=ViT-B-16/2242025.07 | 17.92 | 57.03 | |
| IIAVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 17.75 | 55.93 | |
| Chefer-LRPVariant=ViT, Model=ViT-B-16/2242025.07 | 17.67 | 56.69 | |
| CoIBAVariant=DeiT, Model=DeiT3-L-16/2242025.07 | 17.64 | 67.2 | |
| ViT-CXVariant=DeiT, Model=DeiT-S-16/2242025.07 | 17.57 | 44.43 | |
| IBAVariant=ViT, Model=ViT-B-16/2242025.07 | 17.23 | 59.06 | |
| CoIBAVariant=Swin, Model=Swin-B2025.07 | 17.09 | 54.8 | |
| ViT-CXVariant=ViT, Model=ViT-B-16/2242025.07 | 16.94 | 58.17 | |
| IIAVariant=ViT, Model=ViT-B-16/2242025.07 | 16.56 | 58.7 | |
| BeyondModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 16.23 | 48.09 | |
| BeyondVariant=ViT, Model=ViT-B-16/2242025.07 | 15.84 | 60.37 | |
| GenericVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 15.7 | 52.42 | |
| IBAModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 15.66 | 48.9 | |
| CoIBAVariant=ViT, Model=ViT-L-16/2242025.07 | 15.4 | 61.14 | |
| IIAVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 15.38 | 52.7 | |
| GenericVariant=DeiT, Model=DeiT-B-16/2242025.07 | 15.1 | 49.41 | |
| IBAVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 15.07 | 53.32 | |
| ViT-CXVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 14.98 | 56.65 | |
| BeyondVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 14.71 | 54.07 | |
| Chefer-LRPVariant=DeiT, Model=DeiT-B-16/2242025.07 | 14.63 | 48.79 | |
| GenericVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 14.56 | 56.08 | |
| BeyondVariant=DeiT, Model=DeiT-B-16/2242025.07 | 14.45 | 48.88 | |
| ViT-CXModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 14.09 | 45.55 | |
| IBAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 13.96 | 59.87 | |
| IBAVariant=DeiT, Model=DeiT-B-16/2242025.07 | 13.95 | 50.18 | |
| CoIBAModel=ViT-B-16/224, Training Scheme=MAE2025.07 | 13.77 | 53.42 | |
| IIAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 13.69 | 57.31 | |
| IIAVariant=DeiT, Model=DeiT-B-16/2242025.07 | 13.69 | 50.51 | |
| IBAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 13.56 | 58.64 | |
| CoIBAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.07 | 13.51 | 62.45 | |
| BeyondVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 13.5 | 58.47 | |
| CoIBAVariant=ViT, Model=ViT-B-16/2242025.07 | 13.01 | 62.58 | |
| CoIBAVariant=DeiT, Model=DeiT3-B-16/2242025.07 | 12.97 | 56.54 | |
| Chefer-LRPVariant=DeiT, Model=DeiT-S-16/2242025.07 | 12.76 | 47.51 | |
| GenericVariant=DeiT, Model=DeiT-S-16/2242025.07 | 12.1 | 48.64 | |
| ViT-CXVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 12.08 | 45.09 | |
| CoIBAVariant=DeiT, Model=DeiT-B-16/2242025.07 | 11.79 | 53.96 | |
| IBAVariant=DeiT, Model=DeiT-S-16/2242025.07 | 11.43 | 47.87 | |
| BeyondVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 11.26 | 44.07 | |
| BeyondVariant=DeiT, Model=DeiT-S-16/2242025.07 | 11.13 | 48.88 | |
| CoIBAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.07 | 11.08 | 63.35 | |
| IIAVariant=DeiT, Model=DeiT-S-16/2242025.07 | 10.89 | 49.76 | |
| CoIBAVariant=DeiT, Model=DeiT-S-16/2242025.07 | 9.55 | 53.38 | |
| GenericVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 9 | 48.95 | |
| IBAModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 8.91 | 49.45 | |
| IIAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 8.5 | 49.75 | |
| BeyondModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 8.12 | 50.23 | |
| IBAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 8.02 | 49.22 | |
| Chefer-LRPModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 7.62 | 50.42 | |
| IIAModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 7.46 | 50.52 | |
| CoIBAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07 | 6.97 | 54.23 | |
| CoIBAModel=ViT-B-16/224, Training Scheme=Dino2025.07 | 6.83 | 53.68 |