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---
license: apache-2.0
library_name: libreyolo
pipeline_tag: image-to-image
tags:
- colorization
- image-restoration
- ddcolor
- libreyolo
---
# LibreDDColort-restore
DDColor automatic image colorization with the ConvNeXt-T 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("LibreDDColort-restore.pt")
result = model("black-and-white.jpg")
result.restored.save("colorized.png")
```
## Provenance
- Source repository: [piddnad/ddcolor_paper_tiny](https://huggingface.co/piddnad/ddcolor_paper_tiny)
- Revision: `cf9fd99c1d7472689ec7413441c1b799a51866a3`
- Source file: `pytorch_model.bin`, 220,372,845 bytes
- Source SHA-256: `8a1277bc90a1bfbb6d2d83933a9a6bc821931879ca93e26e4fcec12165d41fce`
- Converted SHA-256: `044254d616df5cc6935669ea05cf641dbd9af5b53a04096449b820d4e64c4a3d`
- 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.