MagFace

MagFace: A Universal Representation for Face Recognition and Quality Assessment
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, Oral presentation.

Paper: arXiv

A toy example: examples.ipynb

Poster: GoogleDrive, BaiduDrive code: dt9e

Beamer: GoogleDrive, BaiduDrive, code: c16b

Presentation: TBD

NOTE: The original codes are implemented on a private codebase and will not be released. This repo is an official but abridged version. See todo list for plans.

BibTex

@inproceedings{meng2021magface,
  title={MagFace: A universal representation for face recognition and quality assessment},
  author={Meng, Qiang and Zhao, Shichao and Huang, Zhida and Zhou, Feng},
  booktitle=IEEE Conference on Computer Vision and Pattern Recognition,
  year=2021
}

Model Zoo

Parallel Method	Float Type	Backbone	Dataset	Split FC?	Model	Log File
DDP	fp32	iResNet100	MS1MV2	Yes	GoogleDrive, BaiduDrive code: wsw3	Trained by original codes
DP	fp32	iResNet50	MS1MV2	No	BaiduDrive code: tvyv	BaiduDrive, code: hpbt
DDP	fp32	iResNet50	MS1MV2	Yes	BaiduDrive code: idkx	BaiduDrive, code: 66j1

Evaluation

Quality Assessment

Steps to calculate face qualities (examples.ipynb is a toy example).

1. Extract features from faces with inference/gen_feat.py.
2. Calculate feature magnitudes with np.linalg.norm().

Basic Training

1. install requirements.
2. Prepare a training list with format imgname 0 id 0 in each line, as indicated here. In the paper, we employ MS1MV2 as the training dataset which can be downloaded from BaiduDrive or Dropbox. Use rec2image.py to extract images.
3. Modify parameters in run/run.sh and run it!

Parallel Training

Note: Use Pytorch > 1.7 for this feature. Codes are mainly based on Aibee's mpu (author: Kaiyu Yue, will be released in middle of April).

How to run:

1. Update NCCL info (can be found with the command ifconfig) and port info in train_dist.py
2. Set the number of gpus in here.
3. [Optional. Not tested yet!] If training with multi-machines, modify node number.
4. [Optional. Not tested yet!] Enable fp16 training by setiing --fp16 1 in run/run_dist.sh.
5. run run/run_dist.sh.

Parallel training (Sec. 5.1 in ArcFace) can highly speed up training as well as reduce consumption of GPU memory. Here are some results.

Parallel Method	Float Type	Backbone	GPU	Batch Size	FC Size	Split FC?	Avg. Throughput (images/sec)	Memory (MiB)
DP	FP32	iResNet50	v100 x 8	512	85742	No	1099.41	8681
DDP	FP32	iResNet50	v100 x 8	512	85742	Yes	1687.71	8137
DDP	FP16	iResNet50	v100 x 8	512	85742	Yes	3388.66	5629
DP	FP32	iResNet100	v100 x 8	512	85742	No	612.40	11825
DDP	FP32	iResNet100	v100 x 8	512	85742	Yes	1060.16	10777
DDP	FP16	iResNet100	v100 x 8	512	85742	Yes	2013.90	7319

Third-party Re-implementation

• Pytorch: FaceX-Zoo from JDAI.

Logs

TODO list:

• add toy examples and release models
• migrate basic codes from the private codebase
• add beamer (after the ddl for iccv2021)
• test the basic codes
• add presentation
• migrate parallel training
• release mpu (Kaiyu Yue, in April)
• test parallel training
• add evaluation codes for recognition
• add evaluation codes for quality assessment
• add fp16
• test fp16
• extend the idea to CosFace

20210331 test fp32 + parallel training and release a model/log

20210325.2 add codes for parallel training as well as fp16 training (not tested).

20210325 the basic training codes are tested! Please find the trained model and logs from the table in Model Zoo.

20210323 add requirements and beamer presentation; add debug logs.

20210315 fix figure 2 and add gdrive link for checkpoint.

20210312 add the basic code (not tested yet).

20210312 add paper/poster/model and a toy example.

20210301 add ReadMe and license.