Basic face library
Project description
FaceXLib
English | 简体中文 GitHub | Gitee码云
facexlib aims at providing ready-to-use face-related functions based on current STOA 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 :star: this repo. Thanks:blush:
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.
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.
:e-mail: Contact
If you have any question, open an issue or email xintao.wang@outlook.com
.
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