facexlib aims at providing ready-to-use face-related functions based on current SOTA open-source methods.
Only PyTorch reference codes are available. For training or fine-tuning, please refer to their original repositories listed below.
Note that we just provide a collection of these algorithms. You need to refer to their original LICENCEs for your intended use.
If facexlib is helpful in your projects, please help to ⭐ this repo. Thanks😊
Other recommended projects:
| Function | Sources | Original LICENSE |
|---|---|---|
| Detection | Pytorch_Retinaface | MIT |
| Alignment | AdaptiveWingLoss | Apache 2.0 |
| Recognition | InsightFace_Pytorch | MIT |
| Parsing | face-parsing.PyTorch | MIT |
| Matting | MODNet | CC 4.0 |
| Headpose | deep-head-pose | Apache 2.0 |
| Tracking | SORT | GPL 3.0 |
| Assessment | hyperIQA | - |
| Utils | Face Restoration Helper | - |
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.7
- Option: NVIDIA GPU + CUDA
pip install facexlibIt will automatically download pre-trained models at the first inference.
If your network is not stable, you can download in advance (may with other download tools), and put them in the folder: PACKAGE_ROOT_PATH/facexlib/weights.
This project is released under the MIT license.
If you have any question, open an issue or email [email protected].
