---
license: mit
datasets:
  - doem1997/openearthmap-sar
language:
  - en
pipeline_tag: image-to-image
library_name: diffusers
tags:
  - remote-sensing
---

> [!WARNING] we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn

# CUT-OpenEarthMap-SAR

CUT (Contrastive Unpaired Translation) models for SAR ↔ optical image translation. Trained on OpenEarthMap data with anti-aliased ResNet generators.

## Model variants

| Model | Direction | Epoch |
|-------|-----------|-------|
| `opt2sar` | Optical → SAR | 20 |
| `sar2opt` | SAR → Optical | 15 |
| `seman2opt` | Semantic → Optical | 25 |
| `seman2opt_pesudo` | Semantic (pseudo) → Optical | 195 |
| `seman2sar` | Semantic → SAR | 25 |
| `seman2sar_pesudo` | Semantic (pseudo) → SAR | 200 |

## Usage

Use with [pytorch-image-translation-models](https://github.com/Bili-Sakura/pytorch-image-translation-models) and the `openearthmap_sar` community pipeline:

```python
from PIL import Image

from examples.community.openearthmap_sar import load_openearthmap_sar_pipeline

pipeline = load_openearthmap_sar_pipeline(
    checkpoint_dir="/path/to/CUT-OpenEarthMap-SAR",
    model_name="sar2opt",  # choose one: opt2sar, sar2opt, seman2opt, seman2opt_pesudo, seman2sar, seman2sar_pesudo
    device="cuda",
)

source = Image.open("/path/to/sar.png").convert("RGB")
output = pipeline(source_image=source, output_type="pil")
output.images[0].save("cut_sar2opt.png")
```

**CLI:**

```bash
python -m examples.community.openearthmap_sar \
    --checkpoint-dir BiliSakura/CUT-OpenEarthMap-SAR \
    --model sar2opt \
    --input sar.png \
    --output out.png
```

> Pass `source_image` as `PIL.Image`. The generator uses anti-aliased down/upsampling to match the original CUT training.

## Repository layout

```
{model_name}/
  generator/
    config.json
    diffusion_pytorch_model.safetensors
```

## Architecture

| Parameter | Value |
|-----------|-------|
| `in_channels` | 3 |
| `out_channels` | 3 |
| `base_filters` | 64 |
| `n_blocks` | 9 |
| `norm` | InstanceNorm |

## Citation

**CUT (architecture):**

```bibtex
@inproceedings{park2020cut,
  title={Contrastive Learning for Unpaired Image-to-Image Translation},
  author={Park, Taesung and Efros, Alexei A and Zhang, Richard and Zhu, Jun-Yan},
  booktitle={ECCV},
  year={2020}
}
```

**OpenEarthMap-SAR (dataset & baseline):**

```bibtex
@ARTICLE{11303033,
  author={Xia, Junshi and Chen, Hongruixuan and Broni-Bediako, Clifford and Wei, Yimin and Song, Jian and Yokoya, Naoto},
  journal={IEEE Geoscience and Remote Sensing Magazine},
  title={OpenEarthMap-SAR: A benchmark synthetic aperture radar dataset for global high-resolution land cover mapping [Software and Data Sets]},
  year={2025},
  volume={13},
  number={4},
  pages={476-487},
  keywords={Translation;Semantic segmentation;Source coding;Urban planning;Land surface;Geoscience and remote sensing;Benchmark testing;Software;Sustainable development;Synthetic aperture radar},
  doi={10.1109/MGRS.2025.3599512}
}
```

## Credits

Models trained on the [OpenEarthMap-SAR](https://github.com/cliffbb/OpenEarthMap-SAR) benchmark dataset. Thanks to the authors for the dataset and CUT baseline.