Image Classification on SVIRO (test)
99.98Top-1 AccuracyNEPENTHE
Evaluation results
| Method | Links | ||
|---|---|---|---|
| NEPENTHEModel=MobileNet-V22024.04 | 99.98 | — | |
| DenseModel=MobileNet-V22024.04 | 99.95 | — | |
| IMPModel=MobileNet-V22024.04 | 99.95 | — | |
| DenseModel=Swin-T2024.04 | 99.95 | — | |
| DenseModel=ResNet-182024.04 | 99.93 | — | |
| Group lassoModel=MobileNet-V22024.04 | 99.93 | — | |
| IMPModel=Swin-T2024.04 | 99.75 | — | |
| NEPENTHEModel=Swin-T2024.04 | 99.75 | — | |
| Smallest weightsModel=Swin-T2024.04 | 99.7 | — | |
| Group lassoModel=Swin-T2024.04 | 99.69 | — | |
| EGPModel=Swin-T2024.04 | 99.64 | — | |
| NEPENTHEModel=ResNet-182024.04 | 99.61 | — | |
| Group lassoModel=ResNet-182024.04 | 99.57 | — | |
| Smallest gradientsModel=Swin-T2024.04 | 99.55 | — | |
| IMPModel=ResNet-182024.04 | 99.45 | — | |
| EGPModel=ResNet-182024.04 | 98.66 | — | |
| Smallest weightsModel=ResNet-182024.04 | 35.55 | — | |
| Smallest gradientsModel=ResNet-182024.04 | 35.55 | — | |
| Smallest weightsModel=MobileNet-V22024.04 | 35.55 | — | |
| Smallest gradientsModel=MobileNet-V22024.04 | 35.55 | — | |
| EGPModel=MobileNet-V22024.04 | 35.05 | — |