---
license: apache-2.0
library_name: libreyolo
pipeline_tag: image-to-image
tags:
  - colorization
  - image-restoration
  - ddcolor
  - libreyolo
---

# LibreDDColorl-restore

DDColor automatic image colorization with the larger ConvNeXt-L encoder,
converted for LibreYOLO's existing `restore` task. The network predicts Lab
chroma at 512 square and reconstructs RGB on the source canvas using the
original luminance plane.

> Checkpoint license and training-data terms are separate. The publisher
> declares this exact artifact Apache-2.0. It was trained on ImageNet and has
> ImageNet-22K initialization lineage; ImageNet's access agreement limits
> dataset use to non-commercial research and education. No ImageNet data is
> included here. DDColor's Artistic checkpoint is intentionally excluded
> because it also uses undisclosed private data.

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreDDColorl-restore.pt")
result = model("black-and-white.jpg")
result.restored.save("colorized.png")
```

## Provenance

- Source repository: [piddnad/ddcolor_modelscope](https://huggingface.co/piddnad/ddcolor_modelscope)
- Revision: `060f67494e31883a4b13cb27f889f3154847ada4`
- Source file: `pytorch_model.bin`, 911,914,869 bytes
- Source SHA-256: `d81711971ec59200da26d5e8a1afae8dd3778d495ea8ad7a7dadc769f403f7e7`
- Converted SHA-256: `e6a4125ce726c256b8efaba8352ab4f369b04c0c452eb5ad08f8cb509ab4fa8b`
- Architecture source: [piddnad/DDColor](https://github.com/piddnad/DDColor) at `2adb63f2656ac41cbdf7b894cddd94121a3faf13`

Learned tensors are unchanged. Conversion adds LibreYOLO v1 checkpoint
metadata. Network parity is exact (`max_abs_diff=0`), and the complete
OpenCV Lab pipeline is pixel-identical to the pinned reference.

## License

The exact source artifact is publisher-declared Apache-2.0. See
[`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE). The ImageNet data caveat
above is retained as provenance and is not erased by conversion.