Fork of richzhang/colorization
Differences between original repository and fork:
- Compatibility with PyTorch >=2.4. (🔥)
- Original pretrained models and converted ONNX models from GitHub releases page. (🔥)
- Model conversion to ONNX format using the export.py file. (🔥)
- Installation with updated requirements.txt file.
- Additional command line options for specifying model weights in the demo_release.py file.
pip install -r requirements.txt
Name | Model Size (MB) | Link | SHA-256 |
---|---|---|---|
Colorization ECCV 16 | 123.0 123.0 |
PyTorch, ONNX | 9b330a0bae53f4ded77b1e23defbf78beaa09c10ebc4c4999e8e4f4a160b93f9 b4a4cecae9e84e776d665e85774815b0bb43de382813b02fb13144f8fd5d6c83 |
Colorization SIGGRAPH 17 | 130.5 129.9 |
PyTorch, ONNX | df00044c0a4d7c3edcecf6f75437ce346a66e7a42612d9b968e1a7e17dbc6f66 7db825910668ee321327d2e6b446e57cbc9c066e196e8be0e152bf76e1206eb7 |
python demo_release.py --eccv16_weights colorization-eccv-16.pth --siggraph17_weights colorization-siggraph-17.pth -i imgs/ansel_adams3.jpg
pip install onnx
python export.py --weights colorization-eccv-16.pth --net_type eccv16
python export.py --weights colorization-siggraph-17.pth --net_type siggraph17