GeoSET / baselines /sar2opt /e3diff /README.md
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# E3Diff · SAR2Opt
E3Diff (GRSL'24) retrained by the GeoSET authors on the SAR2Opt split. These are the weights behind
the E3Diff row for SAR2Opt in Table 2 of the GeoSET paper
([arXiv:2609.37496](https://arxiv.org/abs/2609.37496)).
- Method: Qin, Zou, Li and Zhang, "Efficient end-to-end diffusion model for one-step SAR-to-optical translation", GRSL 2024.
- Implementation: [DeepSARRS/E3Diff](https://github.com/DeepSARRS/E3Diff) at commit `38601093ab8f8e4b478144621f20890b100a3b74` (stage 2, initialised from the DDPM (SR3) stage-1 weights).
- Training and inference code: [`baselines/e3diff`](https://github.com/KAIST-VICLab/GeoSET/tree/main/baselines/e3diff) in the GeoSET repository.
## Files
| File | Bytes | SHA-256 | Role |
|---|---:|---|---|
| `gen.pth` | 768,426,944 | `565c45c53434666fa0f62dbdbfa772742ae8b72eb204b324b05953bdf246d623` | One-step generator UNet (E3Diff stage 2), state dict at iteration 310,000; 3-channel output, [SAR-PPB, SAR-canny, SAR] condition |
## Results (GeoSET paper, Table 2)
SAR2Opt test split, 627 pairs, centre 512 × 512 crop.
| FID↓ | DISTS↓ | KID↓ | DINO↑ | LPIPS↓ | SSIM↑ | PSNR↑ |
|---:|---:|---:|---:|---:|---:|---:|
| 104.7 | 0.232 | 0.0306 | 0.541 | 0.529 | 0.249 | 16.09 |
## Inference
```bash
git clone https://github.com/KAIST-VICLab/GeoSET && cd GeoSET
export BASELINES_ROOT=/path/to/upstream_code DATA_ROOT=/path/to/data WORK_DIR=/path/to/work CKPT_ROOT=/path/to/checkpoints
bash baselines/e3diff/fetch.sh
# vision-aided-loss (CLIP discriminator, built by the model): see baselines/e3diff/README.md
python baselines/data/diff_data.py e3diff sar2opt
hf download JeonghyeokDo/GeoSET --include "baselines/sar2opt/e3diff/*" --local-dir hf
mkdir -p $CKPT_ROOT/e3diff/sar2opt && cp hf/baselines/sar2opt/e3diff/gen.pth $CKPT_ROOT/e3diff/sar2opt/
bash baselines/e3diff/test.sh sar2opt # writes $WORK_DIR/results/e3diff/sar2opt/<stem>.png
python scripts/evaluate.py --dataset sar2opt --pred-dir $WORK_DIR/results/e3diff/sar2opt
```
## Licence
The E3Diff repository contains no licence file (its bundled `SoftPool/` directory is MIT). These weights were trained by the GeoSET authors with that code; the GeoSET repository distributes only its changes to the code.