Classification on PIE (Accuracy Metrics)
91.27Optimal Accuracy (PIE)CoDe-MAE
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CoDe-MAEBackbone=HiViT-B, Pre-training Dataset=OSPretrain-1M, HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 91.27 | 88.78 | |
| DOFABackbone=DOFA-B, Pre-training Dataset=DOFA-MM (8M), HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 88.19 | 86.13 | |
| SatViTBackbone=ViT-B, Pre-training Dataset=Sentinel-1/2 (2.6M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 86.05 | 83.82 | |
| MaRSBackbone=Swin V2-B, Pre-training Dataset=MaRS-16M (32M), HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 85.93 | 79.18 | |
| DINO-MMBackbone=ViT-S, Pre-training Dataset=BEN-MM (620K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 84.7 | 84.66 | |
| CROMABackbone=ViT-B, Pre-training Dataset=SSL4EO (2M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 83.03 | 81.99 | |
| FG-MAEBackbone=ViT-B, Pre-training Dataset=SSL4EO (2M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 81.95 | 81.59 | |
| SwinSSLBackbone=Swin-T, Pre-training Dataset=SEN12MS (360K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 80.12 | 79.43 | |
| DeCURBackbone=ViT-S, Pre-training Dataset=SSL4EO (502K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 79.2 | 79.59 |