FaceXLib
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 โญ this repo. Thanks๐
Other recommended projects: โ
โจ Functions
| 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 | - |
๐ Demo and Tutorials
๐ง Dependencies and Installation
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.7
- Option: NVIDIA GPU + CUDA
Installation
pip install facexlibPre-trained models
It 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.
๐ License and Acknowledgement
This project is released under the MIT license.
๐ง Contact
If you have any question, open an issue or email [email protected].