facexlib 0.2.0.2

Basic face library

FaceXLib

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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.

: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 [e-mail].