Out-of-Distribution Detection on ImageNet-1k V2 (test)
21.97FPR@95NNGuide
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| NNGuideTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 21.97 | 94.17 | 93.44 | |
| ViMTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 28.02 | 92.8 | 92.09 | |
| NNGuideTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 30.78 | 91.7 | 90.08 | |
| GradNormTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 32.66 | 91.87 | 90.62 | |
| KLTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 32.69 | 89.74 | 87.87 | |
| EnergyTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 32.86 | 89.75 | 87.89 | |
| ViMTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 33.11 | 91.88 | 90.75 | |
| KNNTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 33.74 | 91.34 | 90.54 | |
| MaxLogitTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 34.17 | 89.67 | 87.97 | |
| MahalanobisTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 36.58 | 91.72 | 91.87 | |
| ViMTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 39.13 | 89.39 | 88.74 | |
| NNGuideTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 41.73 | 90.08 | 89.95 | |
| MahalanobisTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 42.99 | 90.29 | 90.76 | |
| SSDTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 43.21 | 90.36 | 90.09 | |
| SSDTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 44.19 | 88.73 | 88.65 | |
| KNNTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 44.57 | 89.22 | 88.64 | |
| KLTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 44.67 | 88.97 | 88.24 | |
| EnergyTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 44.68 | 88.97 | 88.24 | |
| MaxLogitTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 45.97 | 86.32 | 84.75 | |
| KLTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 47.03 | 85.91 | 84.41 | |
| EnergyTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 47.03 | 85.91 | 84.41 | |
| GradNormTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 47.62 | 86.07 | 83.75 | |
| MahalanobisTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 47.85 | 89.45 | 89.65 | |
| MaxLogitTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 47.97 | 88.54 | 88.01 | |
| MSPTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 48.99 | 86.38 | 85.93 | |
| MSPTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 54.61 | 84.93 | 84.74 | |
| MSPTraining scheme=From scratch, Model=ResNet-50, ID accuracy=74.432023.09 | 55.54 | 85.3 | 84.57 | |
| KNNTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 57.98 | 86.52 | 86.73 | |
| SSDTraining scheme=Transfer learning, Model=ViT, ID accuracy=81.162023.09 | 58.28 | 81.21 | 81.39 | |
| MahalanobisTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 65.27 | 79.37 | 78.87 | |
| NNGuideTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 67.8 | 79.4 | 78.93 | |
| KNNTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 75.49 | 74.33 | 75.2 | |
| ViMTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 76.9 | 72.32 | 72.16 | |
| SSDTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 77.24 | 69.32 | 67.66 | |
| MSPTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 79.4 | 76.64 | 77.14 | |
| MaxLogitTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 80.19 | 75.17 | 75.06 | |
| GradNormTraining scheme=Transfer learning, Model=RegNet, ID accuracy=82.792023.09 | 84.47 | 61.02 | 59.56 | |
| KLTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 89.57 | 66.17 | 67.92 | |
| EnergyTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 89.57 | 66.17 | 67.92 | |
| GradNormTraining scheme=From scratch, Model=MobileNet, ID accuracy=67.782023.09 | 89.98 | 64.5 | 65.27 |