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Semantic Segmentation On Spacenet 1
평가 지표
Mean IoU
평가 결과
이 벤치마크에서 각 모델의 성능 결과
| Paper Title | ||
|---|---|---|
| MAE+MTP(ViT-L) | 79.69 | MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining |
| MAE+MTP(ViT-B+RVSA) | 79.63 | MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining |
| MAE+MTP(ViT-L+RVSA) | 79.54 | MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining |
| SelectiveMAE+ViT-B | 79.50 | Scaling Efficient Masked Image Modeling on Large Remote Sensing Dataset |
| IMP+MTP(InternImage-XL) | 79.16 | MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining |
| PSANet w/ ResNet50 - FMoW self-supervised pre-training w/ MoCo-V2 + Temporal Positives | 78.48 | Geography-Aware Self-Supervised Learning |
| PSANet w/ ResNet50 backbone - FMoW self-supervised pre-training w/ MoCo-V2 | 78.05 | Geography-Aware Self-Supervised Learning |
| PSANet w/ ResNet50 backbone - FMoW pretrained | 75.57 | Geography-Aware Self-Supervised Learning |
| PSANet w/ ResNet50 backbone - ImageNet pretrained | 75.23 | Geography-Aware Self-Supervised Learning |
| PSANet w/ ResNet50 backbone | 74.93 | Geography-Aware Self-Supervised Learning |
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