FaRL for Facial Representation Learning

This repo hosts official implementation of our paper "General Facial Representation Learning in a Visual-Linguistic Manner".

Introduction

FaRL offers powerful pre-training transformer backbones for face analysis tasks. Its pre-training combines both the image-text contrastive learning and the masked image modeling.

After the pre-training, the image encoder can be utilized for various downstream face tasks.

Setup Downstream Training

Different pre-trained transformer backbones can be downloaded as below.

Model Name	Pre-training Data	Link
FaRL-Base-Patch16-LAIONFace20M-ep16 (used in paper)	LAION Face 20M	OneDrive, BLOB
FaRL-Base-Patch16-LAIONFace20M-ep64	LAION Face 20M	BLOB
FaRL-Base-Patch16-LAIONFace50M-ep16	LAION Face 50M	OneDrive, BLOB

Download these models to ./blob/checkpoint/.

All downstream trainings require 8 NVIDIA V100 GPUs (32G). Before setting up, install these packages:

• PyTorch 1.7.0
• MMCV-Full 1.3.14

Then, install the rest dependencies with pip install -r ./requirement.txt.

Please refer to ./DS_DATA.md to prepare the training and testing data for downstream tasks.

Now you can launch the trainings with following command template.

python -m blueprint.run farl/experiments/{task}/{train_config_file}.yaml --exp_name farl --blob_root ./blob

The repo has included some config files under ./farl/experiments/ that perform finetuning for face parsing and face alignment.

Performance

The following table illustrates their performances reported in the paper (Paper) or reproduced using this repo (Rep). There are small differences between their performances due to code refactorization.

File Name	Task	Benchmark	Metric	Score (Paper/Rep)	Logs (Paper/Rep)
face_parsing/ train_celebm_farl-b-ep16-448_refinebb.yaml	Face Parsing	CelebAMask-HQ	F1-mean ⇑	89.56/89.65	Paper, Rep
face_parsing/ train_lapa_farl-b-ep16_448_refinebb.yaml	Face Parsing	LaPa	F1-mean ⇑	93.88/93.86	Paper, Rep
face_alignment/ train_aflw19_farl-b-ep16_448_refinebb.yaml	Face Alignment	AFLW-19 (Full)	NME_diag ⇓	0.943/0.943	Paper, Rep
face_alignment/ train_ibug300w_farl-b-ep16_448_refinebb.yaml	Face Alignment	300W (Full)	NME_inter-ocular ⇓	2.93/2.92	Paper, Rep
face_alignment/ train_wflw_farl-b-ep16_448_refinebb.yaml	Face Alignment	WFLW (Full)	NME_inter-ocular ⇓	3.96/3.98	Paper, Rep

We also report results using the 50M pre-trained backbone, showing further enhancement on LaPa and AFLW-19.

File Name	Task	Benchmark	Metric	Score	Logs
face_parsing/ train_celebm_farl-b-50m-ep16-448_refinebb.yaml	Face Parsing	CelebAMask-HQ	F1-mean ⇑	89.68	Rep
face_parsing/ train_lapa_farl-b-50m-ep16_448_refinebb.yaml	Face Parsing	LaPa	F1-mean ⇑	94.01	Rep
face_alignment/ train_aflw19_farl-b-50m-ep16_448_refinebb.yaml	Face Alignment	AFLW-19 (Full)	NME_diag ⇓	0.937	Rep
face_alignment/ train_ibug300w_farl-b-50m-ep16_448_refinebb.yaml	Face Alignment	300W (Full)	NME_inter-ocular ⇓	2.92	Rep
face_alignment/ train_wflw_farl-b-50m-ep16_448_refinebb.yaml	Face Alignment	WFLW (Full)	NME_inter-ocular ⇓	3.99	Rep

Citation

If you find our work helpful, please consider citing

@article{zheng2021farl,
  title={General Facial Representation Learning in a Visual-Linguistic Manner},
  author={Zheng, Yinglin and Yang, Hao and Zhang, Ting and Bao, Jianmin and Chen, Dongdong and Huang, Yangyu and Yuan, Lu and Chen, Dong and Zeng, Ming and Wen, Fang},
  journal={arXiv preprint arXiv:2112.03109},
  year={2021}
}

Contact

For help or issues concerning the code and the released models, please submit a GitHub issue. Otherwise, please contact Hao Yang (haya@microsoft.com).

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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