Accuracy Estimation on Multi-Dataset Transfer (MNIST, USPS, SVHN, COCO, PASCAL, ImageNet)
3.58MAEMetaEvaluator
Evaluation Results
| Method | Links | |
|---|---|---|
| MetaEvaluatorUnseen model architecture=ResNeXt-50-32x4d2026.05 | 3.58 | |
| MetaEvaluatorUnseen model architecture=ConvNeXt-Tiny2026.05 | 3.61 | |
| MetaEvaluatorUnseen model architecture=RegNetY-8GF2026.05 | 3.74 | |
| MetaEvaluatorUnseen model architecture=Avg.2026.05 | 3.76 | |
| MetaEvaluatorUnseen model architecture=ViT-Tiny2026.05 | 3.89 | |
| MetaEvaluatorUnseen model architecture=DeiT-Small2026.05 | 3.97 | |
| SelfTrainEnsUnseen model architecture=RegNetY-8GF2026.05 | 9.28 | |
| SelfTrainEnsUnseen model architecture=ResNeXt-50-32x4d2026.05 | 10.02 | |
| SelfTrainEnsUnseen model architecture=ConvNeXt-Tiny2026.05 | 10.11 | |
| SelfTrainEnsUnseen model architecture=ViT-Tiny2026.05 | 10.46 | |
| SelfTrainEnsUnseen model architecture=DeiT-Small2026.05 | 10.63 | |
| SelfTrainEnsUnseen model architecture=Avg.2026.05 | 11.3 | |
| AutoEvalUnseen model architecture=ResNeXt-50-32x4d2026.05 | 11.44 | |
| AutoEvalUnseen model architecture=ConvNeXt-Tiny2026.05 | 11.58 | |
| AutoEvalUnseen model architecture=RegNetY-8GF2026.05 | 11.79 | |
| AutoEvalUnseen model architecture=Avg.2026.05 | 11.8 | |
| AutoEvalUnseen model architecture=ViT-Tiny2026.05 | 12.01 | |
| AutoEvalUnseen model architecture=DeiT-Small2026.05 | 12.18 | |
| PseudoAutoEvalUnseen model architecture=ResNeXt-50-32x4d2026.05 | 13.67 | |
| PseudoAutoEvalUnseen model architecture=ConvNeXt-Tiny2026.05 | 13.81 | |
| PseudoAutoEvalUnseen model architecture=Avg.2026.05 | 14.01 | |
| PseudoAutoEvalUnseen model architecture=RegNetY-8GF2026.05 | 14.02 | |
| PseudoAutoEvalUnseen model architecture=ViT-Tiny2026.05 | 14.19 | |
| PseudoAutoEvalUnseen model architecture=DeiT-Small2026.05 | 14.37 | |
| AGDUnseen model architecture=ResNeXt-50-32x4d2026.05 | 15.11 | |
| AGDUnseen model architecture=ConvNeXt-Tiny2026.05 | 15.32 | |
| AGDUnseen model architecture=Avg.2026.05 | 15.55 | |
| AGDUnseen model architecture=RegNetY-8GF2026.05 | 15.59 | |
| AGDUnseen model architecture=ViT-Tiny2026.05 | 15.74 | |
| AGDUnseen model architecture=DeiT-Small2026.05 | 15.97 | |
| DoCUnseen model architecture=ResNeXt-50-32x4d2026.05 | 16.03 | |
| DoCUnseen model architecture=ConvNeXt-Tiny2026.05 | 16.27 | |
| DoCUnseen model architecture=RegNetY-8GF2026.05 | 16.54 | |
| DoCUnseen model architecture=Avg.2026.05 | 16.55 | |
| DoCUnseen model architecture=ViT-Tiny2026.05 | 16.88 | |
| DoCUnseen model architecture=DeiT-Small2026.05 | 17.02 | |
| ATCUnseen model architecture=ResNeXt-50-32x4d2026.05 | 17.02 | |
| ATCUnseen model architecture=ConvNeXt-Tiny2026.05 | 17.19 | |
| ATCUnseen model architecture=RegNetY-8GF2026.05 | 17.46 | |
| ATCUnseen model architecture=Avg.2026.05 | 17.49 | |
| ATCUnseen model architecture=ViT-Tiny2026.05 | 17.83 | |
| ATCUnseen model architecture=DeiT-Small2026.05 | 17.96 |