Official repository for MixFaceNets: Extremely Efficient Face Recognition Networks

Related tags

Deep Learningdeep-learningpytorchface-recognitionlfwface-verificationmixfacenets
Overview

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}
}
Owner
Fadi Boutros
Fadi Boutros
GitHub Repository
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
Implementation of PersonaGPT Dialog Model

PersonaGPT An open-domain conversational agent with many personalities PersonaGPT is an open-domain conversational agent cpable of decoding personaliz

ILLIDAN Lab 42 Jan 01, 2023
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Rex Cheng 106 Jan 03, 2023
This repository contains PyTorch code for Robust Vision Transformers.

This repository contains PyTorch code for Robust Vision Transformers.

117 Dec 07, 2022
Expand human face editing via Global Direction of StyleCLIP, especially to maintain similarity during editing.

Oh-My-Face This project is based on StyleCLIP, RIFE, and encoder4editing, which aims to expand human face editing via Global Direction of StyleCLIP, e

AiLin Huang 51 Nov 17, 2022
Training deep models using anime, illustration images.

animeface deep models for anime images. Datasets anime-face-dataset Anime faces collected from Getchu.com. Based on Mckinsey666's dataset. 63.6K image

Tomoya Sawada 61 Dec 25, 2022
Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform This repository is the implementation of "Variable-Rate Deep Image C

Myungseo Song 47 Dec 13, 2022
This tool uses Deep Learning to help you draw and write with your hand and webcam.

This tool uses Deep Learning to help you draw and write with your hand and webcam. A Deep Learning model is used to try to predict whether you want to have 'pencil up' or 'pencil down'.

lmagne 169 Dec 10, 2022
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

DeepXF: Explainable Forecasting and Nowcasting with State-of-the-art Deep Neural Networks and Dynamic Factor Model Also, verify TS signal similarities

AjayAru 88 Dec 27, 2022
RNN Predict Street Commercial Vitality

RNN-for-Predicting-Street-Vitality Code and dataset for Predicting the Vitality of Stores along the Street based on Business Type Sequence via Recurre

Zidong LIU 1 Dec 15, 2021
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码，MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请，文件介绍如下： 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
Generates all variables from your .tf files into a variables.tf file.

tfvg Generates all variables from your .tf files into a variables.tf file. It searches for every var.variable_name in your .tf files and generates a v

1 Dec 01, 2022
This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

MoEBERT This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022). Installation Create an

Simiao Zuo 34 Dec 24, 2022
Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Ng Kam Woh 71 Dec 22, 2022
HNECV: Heterogeneous Network Embedding via Cloud model and Variational inference

HNECV This repository provides a reference implementation of HNECV as described in the paper: HNECV: Heterogeneous Network Embedding via Cloud model a

4 Jun 28, 2022
The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"

SHGNN: Structure-Aware Heterogeneous Graph Neural Network The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural

Wentao Xu 7 Nov 13, 2022
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022
This repository contains code to train and render Mixture of Volumetric Primitives (MVP) models

Mixture of Volumetric Primitives -- Training and Evaluation This repository contains code to train and render Mixture of Volumetric Primitives (MVP) m

Meta Research 125 Dec 29, 2022
A multi-scale unsupervised learning for deformable image registration

A multi-scale unsupervised learning for deformable image registration Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu and Baochang Zha

ShuweiShao 2 Apr 13, 2022