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Tensorflow implementation for MobileFaceNet. Contribute to sirius-ai/MobileFaceNet_TF development by creating an account on GitHub.

This repo illustrates how to implement MobileFaceNet and Arcface for face recognition task. Pretrained model is posted for tests over picture, video and cam ...

Aug 08, 2019 · mobilefacenet-V2. now we get more higher accuray: [lfw][12000]Accuracy-Flip: 0.99667+-0.00358 [agedb_30][12000]Accuracy-Flip: 0.96667+-0.00167 use my modified mobilenet network.

Sep 24, 2019 · sirius-ai. /. MobileFaceNet_TF. Public. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 1. Pre-requirements tensorflow 2.0+. Failed to load latest commit information.

Nov 03, 2021 · GitHub - yeyupiaoling/Pytorch-MobileFaceNet: Pytorch实现的人脸识别明细MobileFaceNet模型，在预测使用MTCNN检测人脸，然后使用MobileFaceNet模型识别。 yeyupiaoling / Pytorch-MobileFaceNet Public master 1 branch 0 tags Go to file Code yeyupiaoling 更新文档 268f820 on Nov 3, 2021 8 commits dataset 修改结构和保存模型方式 9 months ago detection 修改结构和保存模型方式 9 months ago face_db 修改结构和保存模型方式 9 months ago

Dec 06, 2018 · MobileFaceNet Introduction. This repository is the pytorch implement of the paper: MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices and I almost follow the implement details of the paper.

06.12.2018 · MobileFaceNet Introduction. This repository is the pytorch implement of the paper: MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices and I almost follow the implement details of the paper. I train the model on CASIA-WebFace dataset, and evaluate on LFW dataset.

14.03.2022 · GitHub is where people build software. More than 73 million people use GitHub to discover, fork, and contribute to over 200 million projects.

implement mobilefacenet(including arcface layer). Contribute to zhijiesun/Mobilefacenet development by creating an account on GitHub.

24.09.2019 · sirius-ai. /. MobileFaceNet_TF. Public. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 1. Pre-requirements tensorflow 2.0+. Failed to load latest commit information.

Jun 20, 2018 · MobileFaceNet. 论文 ： MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices 的AMsoftmaxLoss实现. 环境需要. Amsoftmax的配置. DepthwiseConvolution层的添加. 训练数据集. VggFace2数据集，图像size：112X96. LFW准确率. 参考： mobilefacenet-caffe. AMSoftmax. depthwise convolution ...

KFaceForUnity is a deep learning face recognition plugin for Unity. It`s a mobile platform offline face recognition solution, including face detection, ...

GitHub - qidiso/mobilefacenet-V2: improve the accuracy of mobilefacenet(insight face) reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30.

Test Over Picture, Video and Cam

github.com: /foamliu/MobileFaceNet. Languages: Python Add/Edit. Libraries: Add/Edit. Description: Add/Edit. PyTorch implementation of MobileFaceNets.

KFaceForUnity is a deep learning face recognition plugin for Unity. It`s a mobile platform offline face recognition solution, including face detection, face alignment, face normalization, face feature extraction and face search. unity3d face-recognition unity3d-plugin dlib mobilefacenet offline-face-recognition. Updated on Feb 19, 2019.

03.11.2021 · MobileFaceNet. 本项目参考了ArcFace的损失函数结合MobileNet，意在开发一个模型较小，但识别准确率较高且推理速度快的一种人脸识别项目，该项目训练数据使用emore数据集，一共有85742个人，共5822653张图片，使用lfw-align-128数据集作为测试数据。. 数据集准备. 本项目提供了标注文件，存放在dataset目录下 ...

MobileFaceNets. apm. PyTorch implementation of MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices. paper.

... MTCNN and MobileFaceNet on Face Recognition. Contribute to shoutOutYangJie/Face_Recognition_using_pytorch development by creating an account on GitHub.

08.08.2019 · mobilefacenet-V2. now we get more higher accuray: [lfw][12000]Accuracy-Flip: 0.99667+-0.00358 [agedb_30][12000]Accuracy-Flip: 0.96667+-0.00167 use my modified mobilenet network.

20.06.2018 · MobileFaceNet. 论文 ： MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices 的AMsoftmaxLoss实现. 环境需要. Amsoftmax的配置. DepthwiseConvolution层的添加. 训练数据集. VggFace2数据集，图像size：112X96. LFW准确率. 参考： mobilefacenet-caffe. AMSoftmax. depthwise convolution ...

MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices - GitHub - Xiaoccer/MobileFaceNet_Pytorch: ...

A face recognition system with low computational cost - GitHub - pedroprates/mobile-face-net: A face recognition system with low computational cost.