SkillAgentSearch skills...

Mixfacenets

Official repository for MixFaceNets: Extremely Efficient Face Recognition Networks

Install / Use

/learn @fdbtrs/Mixfacenets
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

MixFaceNets

This is the official repository of the paper: MixFaceNets: Extremely Efficient Face Recognition Networks.

(Accepted in IJCB2021) https://ieeexplore.ieee.org/abstract/document/9484374

Paper Arxiv

| Model | MFLOPs |Params (M)|LFW%| AgeDB-30% |IJB-B( TAR at FAR1e–6) | IJB-C( TAR at FAR1e–6)| Pretrained model| | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- |------------- | | MixFaceNet-M | 626.1 | 3.95 | 99.68 | 97.05 | 91.55 | 93.42 |pretrained-mode | | ShuffleMixFaceNet-M | 626.1 | 3.95 | 99.60 | 96.98 | 91.47 | 93.5 | pretrained-mode| | MixFaceNet-S | 451.7 | 3.07 | 99.60 | 96.63 |90.17 | 92.30 | pretrained-mode | |ShuffleMixFaceNet-S | 451.7 | 3.07 | 99.58 | 97.05 |90.94 | 93.08 | pretrained-mode| |MixFaceNet-XS | 161.9 | 1.04 |99.60 | 95.85 | 88.48 | 90.73 |pretrained-mode | |ShuffleMixFaceNet-XS | 161.9 | 1.04 | 99.53 | 95.62 |87.86 | 90.43 | pretrained-mode|

FLOPs vs. performance on LFW (accuracy), AgeDB-30 (accuracy), MegaFace (TAR at FAR1e-6), IJB-B (TAR at FAR1e-4), IJB-C (TAR at FAR1e-4) and refined version of MegaFace, noted as MegaFace (R), (TAR at FAR1e-6). Our MixFaceNet models are highlighted with triangle marker and red edge color.

LFW LFW

AgeDb-30 LFW

MegaFace LFW

MegaFace(R) LFW

IJB-B LFW

IJB-C LFW

If you find MixFaceNets useful in your research, please cite the following paper:

Citation

@INPROCEEDINGS{9484374,
  author={Boutros, Fadi and Damer, Naser and Fang, Meiling and Kirchbuchner, Florian and Kuijper, Arjan},
  booktitle={2021 IEEE International Joint Conference on Biometrics (IJCB)}, 
  title={MixFaceNets: Extremely Efficient Face Recognition Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/IJCB52358.2021.9484374}}


The model is trained with ArcFace loss using Partial-FC algorithms. If you train the MixfaceNets with ArcFace and Partial-FC, please follow their distribution licenses.

Citation

@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{an2020partical_fc,
  title={Partial FC: Training 10 Million Identities on a Single Machine},
  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
  Zhang, Debing and Fu Ying},
  booktitle={Arxiv 2010.05222},
  year={2020}
}
View on GitHub
GitHub Stars70
CategoryEducation
Updated3mo ago
Forks12

Languages

Python

Security Score

97/100

Audited on Dec 22, 2025

No findings