• ArcFace in MindSpore
• • Introduction
  • Updates!!
  • Coming soon
  • Performance on lfw, cfp_fp, agedb_30, calfw and cplfw
  • 1. Training on Multi-Host GPU
  • Pretained models
  • Quick Start
  • Citations

ArcFace in MindSpore

Introduction

MindSpore is a new generation of full-scenario AI computing framework launched by Huawei in August 2019 and released On March 28, 2020.

This repository is the mindspore implementation of ArcFace and has achieved great performance. We implemented two versions based on ResNet and MobileNet to meet different needs.

Updates!!

• 【2022/09/24】 We upload ArcFace based on MindSpore and update the result of eval dataset.
• 【2022/06/18】 We create this repository.

Coming soon

• MS1MV3, Glint360K, WebFace42M dataset pretrained models.
• Support vit backbone.

Performance on lfw, cfp_fp, agedb_30, calfw and cplfw

1. Training on Multi-Host GPU

Datasets	Backbone	lfw	cfp_fp	agedb_30	calfw	cplfw
CASIA	mobilefacenet-0.45g	0.98483+-0.00425	0.86843+-0.01838	0.90133+-0.02118	0.90917+-0.01294	0.81217+-0.02232
CASIA	r50	0.98667+-0.00435	0.90357+-0.01300	0.91750+-0.02277	0.92033+-0.01122	0.83667+-0.01719
CASIA	r100	0.98950+-0.00366	0.90943+-0.01300	0.91833+-0.01655	0.92433+-0.01017	0.84967+-0.01904
MS1MV2	mobilefacenet-0.45g	0.98700+-0.00364	0.88214+-0.01493	0.90950+-0.02076	0.91750+-0.01088	0.82633+-0.02014
MS1MV2	r50	0.99767+-0.00260	0.97186+-0.00652	0.97783+-0.00869	0.96067+-0.01121	0.92033+-0.01732
MS1MV2	r100	0.99383+-0.00334	0.96800+-0.01042	0.93767+-0.01724	0.93267+-0.01327	0.89150+-0.01763

Pretained models

Soon

Quick Start

Installation

Step1. Git clone this repo ```shell git clone https://github.com/mindlab-ai/mindface.git ``` Step2. Install dependencies ```shell cd mindface pip install -r requirements.txt ```

Prepare Data

Step1. Prepare and Download the dataset

• CASIA (10K ids/0.5M images)
• MS1MV2 (87k IDs, 5.8M images)
• MS1MV3 (93k IDs, 5.2M images)
• Glint360K (360k IDs, 17.1M images)
• WebFace42M (2M IDs, 42.5M images)

Step2. Convert the dataset from rec format to jpg format

python utils/rec2jpg_dataset.py --include the/path/to/rec --output output/path

Train and Eval

The example commands below show how to run distributed training.

Step1. Train

# Distributed training example
bash scripts/run_distribute_train.sh rank_size /path/dataset

Step2. Eval

# Evaluation example
bash scripts/run_eval.sh /path/evalset /path/ckpt

Tutorials

Soon

Citations

@inproceedings{deng2019arcface,
  title={Arcface: Additive angular margin loss for deep face recognition},
  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4690--4699},
  year={2019}
}
@inproceedings{An_2022_CVPR,
    author={An, Xiang and Deng, Jiankang and Guo, Jia and Feng, Ziyong and Zhu, XuHan and Yang, Jing and Liu, Tongliang},
    title={Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month={June},
    year={2022},
    pages={4042-4051}
}
@inproceedings{zhu2021webface260m,
  title={Webface260m: A benchmark unveiling the power of million-scale deep face recognition},
  author={Zhu, Zheng and Huang, Guan and Deng, Jiankang and Ye, Yun and Huang, Junjie and Chen, Xinze and Zhu, Jiagang and Yang, Tian and Lu, Jiwen and Du, Dalong and Zhou, Jie},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10492--10502},
  year={2021}
}