Awesome
FaceXLib
facexlib aims at providing ready-to-use face-related functions based on current SOTA open-source methods. <br> Only PyTorch reference codes are available. For training or fine-tuning, please refer to their original repositories listed below. <br> 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 :star: this repo. Thanks:blush: <br> Other recommended projects: :arrow_forward: Real-ESRGAN :arrow_forward: GFPGAN :arrow_forward: BasicSR
:sparkles: 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 | - |
:eyes: Demo and Tutorials
:wrench: Dependencies and Installation
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.7
- Option: NVIDIA GPU + CUDA
Installation
pip install facexlib
Pre-trained models
It will automatically download pre-trained models at the first inference. <br>
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.
:scroll: License and Acknowledgement
This project is released under the MIT license. <br>
:e-mail: Contact
If you have any question, open an issue or email xintao.wang@outlook.com.