LibreDDColorl-restore

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.

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
• Revision: 060f67494e31883a4b13cb27f889f3154847ada4
• Source file: pytorch_model.bin, 911,914,869 bytes
• Source SHA-256: d81711971ec59200da26d5e8a1afae8dd3778d495ea8ad7a7dadc769f403f7e7
• Converted SHA-256: e6a4125ce726c256b8efaba8352ab4f369b04c0c452eb5ad08f8cb509ab4fa8b
• Architecture source: 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 and NOTICE. The ImageNet data caveat above is retained as provenance and is not erased by conversion.