这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Overview

Facenet+Retinaface:人脸识别模型在Keras当中的实现


目录

  1. 注意事项 Attention
  2. 所需环境 Environment
  3. 文件下载 Download
  4. 预测步骤 How2predict
  5. 参考资料 Reference

注意事项

该库中包含了两个网络,分别是retinaface和facenet。二者使用不同的权值。 在使用网络时一定要注意权值的选择,以及主干与权值的匹配。

所需环境

tensorflow-gpu==1.13.1
keras==2.1.5

文件下载

预测所需的权值文件可以在百度云下载。
链接: https://pan.baidu.com/s/1byskhV594bK9b0eHONjF2g 提取码: tn8y

预测步骤

  1. 本项目自带主干为mobilenet的retinaface模型与facenet模型。可以直接运行,如果想要使用主干为resnet50的retinafa和主干为inception_resnetv1的facenet模型需要。
  2. 在retinaface.py文件里面,在如下部分修改model_path和backbone使其对应训练好的文件。
_defaults = {
    "retinaface_model_path" : 'model_data/retinaface_mobilenet025.h5',
    #-----------------------------------#
    #   可选retinaface_backbone有
    #   mobilenet和resnet50
    #-----------------------------------#
    "retinaface_backbone"   : "mobilenet",
    "confidence"            : 0.5,
    "iou"                   : 0.3,
    #----------------------------------------------------------------------#
    #   是否需要进行图像大小限制。
    #   输入图像大小会大幅度地影响FPS,想加快检测速度可以减少input_shape。
    #   开启后,会将输入图像的大小限制为input_shape。否则使用原图进行预测。
    #   keras代码中主干为mobilenet时存在小bug,当输入图像的宽高不为32的倍数
    #   会导致检测结果偏差,主干为resnet50不存在此问题。
    #   可根据输入图像的大小自行调整input_shape,注意为32的倍数,如[640, 640, 3]
    #----------------------------------------------------------------------#
    "retinaface_input_shape": [640, 640, 3],
    "letterbox_image"       : True,

    "facenet_model_path"    : 'model_data/facenet_mobilenet.h5',
    #-----------------------------------#
    #   可选facenet_backbone有
    #   mobilenet和inception_resnetv1
    #-----------------------------------#
    "facenet_backbone"      : "inception_resnetv1",
    "facenet_input_shape"   : [160,160,3],
    "facenet_threhold"      : 0.9,
}
  1. 运行encoding.py,对face_dataset里面的图片进行编码,face_dataset的命名规则为XXX_1.jpg、XXX_2.jpg。最终在model_data文件夹下生成对应的数据库人脸编码数据文件。
  2. 运行predict.py,输入下述文字,可直接预测。
img/zhangxueyou.jpg
  1. 利用video.py可进行摄像头检测。

Reference

https://github.com/biubug6/Pytorch_Retinaface

Owner
Bubbliiiing
Bubbliiiing
optimization routines for hyperparameter tuning

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

Marc Claesen 398 Nov 09, 2022
Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Posture: Pose Tracking and Machine Learning for prescribing corrective suggestions to improve posture and form while exercising. This repository conta

Pratham Mehta 10 Nov 11, 2022
Segmentation models with pretrained backbones. PyTorch.

Python library with Neural Networks for Image Segmentation based on PyTorch. The main features of this library are: High level API (just two lines to

Pavel Yakubovskiy 6.6k Jan 06, 2023
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

ademxapp Visual applications by the University of Adelaide In designing our Model A, we did not over-optimize its structure for efficiency unless it w

Zifeng Wu 338 Dec 12, 2022
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)

KoGPT KoGPT (Korean Generative Pre-trained Transformer) https://github.com/kakaobrain/kogpt https://huggingface.co/kakaobrain/kogpt Model Descriptions

Kakao Brain 799 Dec 28, 2022
机器学习、深度学习、自然语言处理等人工智能基础知识总结。

说明 机器学习、深度学习、自然语言处理基础知识总结。 目前主要参考李航老师的《统计学习方法》一书,也有一些内容例如XGBoost、聚类、深度学习相关内容、NLP相关内容等是书中未提及的。

Peter 445 Dec 12, 2022
Differentiable Annealed Importance Sampling (DAIS)

Differentiable Annealed Importance Sampling (DAIS) This repository contains the code to reproduce the DAIS results from the paper Differentiable Annea

Guodong Zhang 6 Dec 26, 2021
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper) (Accepted for oral presentation at ACM

Minha Kim 1 Nov 12, 2021
Sdf sparse conv - Deep Learning on SDF for Classifying Brain Biomarkers

Deep Learning on SDF for Classifying Brain Biomarkers To reproduce the results f

1 Jan 25, 2022
Learning What and Where to Draw

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee This is the code for our NIPS 201

Scott Ellison Reed 337 Nov 18, 2022
[CVPR 2021] NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning

NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning Project Page | Paper | Supplemental material #1 | Supplement

KAIST VCLAB 49 Nov 24, 2022
Stacked Generative Adversarial Networks

Stacked Generative Adversarial Networks This repository contains code for the paper "Stacked Generative Adversarial Networks", CVPR 2017. Part of the

Xun Huang 241 May 07, 2022
Atif Hassan 103 Dec 14, 2022
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

FaceQgen FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment This repository is based on the paper: "FaceQgen: Semi-Supervised D

Javier Hernandez-Ortega 3 Aug 04, 2022
PyTorch implementation of 'Gen-LaneNet: a generalized and scalable approach for 3D lane detection'

(pytorch) Gen-LaneNet: a generalized and scalable approach for 3D lane detection Introduction This is a pytorch implementation of Gen-LaneNet, which p

Yuliang Guo 233 Jan 06, 2023
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Zalando Research 120 Dec 24, 2022
Stratified Transformer for 3D Point Cloud Segmentation (CVPR 2022)

Stratified Transformer for 3D Point Cloud Segmentation Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia

DV Lab 195 Jan 01, 2023
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Automated Side Channel Analysis of Media Software with Manifold Learning Official implementation of USENIX Security 2022 paper: Automated Side Channel

Yuanyuan Yuan 175 Jan 07, 2023