Semantic Segmentation on SAR water detection dataset (test)
80.09PrecisionUPerNet
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| UPerNetBackbone=DI3CL-ResNet502025.11 | 80.09 | 71.49 | 75.55 | 60.7 | |
| UPerNetBackbone=DI3CL-ResNet1012025.11 | 79.48 | 71.37 | 75.21 | 60.26 | |
| PSPNetBackbone=IMP-ResNet502025.11 | 78.85 | 71.8 | 75.16 | 60.2 | |
| FCNBackbone=IMP-ResNet502025.11 | 76.6 | 67.84 | 73.09 | 57.59 | |
| UPerNetBackbone=SAR-JEPA+ViT-B2025.11 | 75.99 | 71.36 | 73.6 | 58.23 | |
| FarSegBackbone=IMP-ResNet502025.11 | 75.89 | 71.36 | 73.55 | 58.17 | |
| UPerNetBackbone=SMLFR+ConvNeXt-B2025.11 | 75.18 | 69.18 | 72.05 | 56.37 | |
| DANetBackbone=IMP-ResNet502025.11 | 74.23 | 70.56 | 72.35 | 56.68 | |
| UPerNetBackbone=SeCo-ResNet502025.11 | 73.14 | 68.86 | 70.94 | 54.96 | |
| UPerNetBackbone=IMP-ResNet502025.11 | 72.38 | 72.2 | 72.29 | 56.6 | |
| UPerNetBackbone=SARATR-X+HiViT-B2025.11 | 72.23 | 75.23 | 73.7 | 58.35 | |
| UPerNetBackbone=RSP-ResNet502025.11 | 71.93 | 72.15 | 72.04 | 56.3 | |
| DeepLabV3+Backbone=IMP-ResNet502025.11 | 70.67 | 74.47 | 72.52 | 56.89 |