Classification on DDHR-SK
84.86Accuracy (Optimal)CoDe-MAE
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CoDe-MAEBackbone=HiViT-B, Pre-training Dataset=OSPretrain-1M, HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 84.86 | 89.2 | |
| MaRSBackbone=Swin V2-B, Pre-training Dataset=MaRS-16M (32M), HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 81.86 | 76.27 | |
| DOFABackbone=DOFA-B, Pre-training Dataset=DOFA-MM (8M), HR=true, Evaluation Protocol=10-shot linear probing2026.04 | 81.57 | 87.13 | |
| DINO-MMBackbone=ViT-S, Pre-training Dataset=BEN-MM (620K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 75.17 | 85.52 | |
| SatViTBackbone=ViT-B, Pre-training Dataset=Sentinel-1/2 (2.6M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 74.33 | 83.16 | |
| DeCURBackbone=ViT-S, Pre-training Dataset=SSL4EO (502K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 72.63 | 81.69 | |
| CROMABackbone=ViT-B, Pre-training Dataset=SSL4EO (2M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 71.69 | 84.25 | |
| FG-MAEBackbone=ViT-B, Pre-training Dataset=SSL4EO (2M), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 60.86 | 81.56 | |
| SwinSSLBackbone=Swin-T, Pre-training Dataset=SEN12MS (360K), HR=false, Evaluation Protocol=10-shot linear probing2026.04 | 59.06 | 76.62 |