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| Metric | Value |
|---|---|
| mFscore | 58.37% |
| mIoU | 44.35% |
| OA | 67.10% |
| Kappa | 60.02% |
| mPrecision | 52.78% |
| mRecall | 70.72% |
| Class | IoU |
|---|---|
| Corn | 76.76% |
| Winter rapeseed | 74.17% |
| Beet | 73.32% |
| Soft winter wheat | 72.22% |
| Soybeans | 66.81% |
| Meadow | 56.56% |
| Winter barley | 56.56% |
| Sunflower | 53.94% |
| Background | 48.39% |
| Winter durum wheat | 43.30% |
| Potatoes | 37.33% |
| Grapevine | 36.16% |
| Spring barley | 30.51% |
| Winter triticale | 25.01% |
| Leguminous fodder | 23.17% |
| Fruits/veg/flowers | 22.27% |
| Mixed cereal | 17.63% |
| Orchard | 15.25% |
| Sorghum | 13.29% |
1python train.py \
2 --data_root /path/to/PASTIS \
3 --fold 1 \
4 --small_model \
5 --epochs 100 \
6 --batch_size 16 \
7 --lr 5e-5 \
8 --weight_decay 0.051@article{li2025agrifm,
2 title={AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping},
3 author={Li, Wenyuan and others},
4 journal={Remote Sensing of Environment},
5 year={2025}
6}