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FaceMaskDetection

开源人脸口罩检测模型和数据 Detect faces and determine whether people are wearing mask.

ModelsObject detectionJupyter Notebook
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创建于 2020-02-18 · 更新于 2026-10-14 · 今日第 8957 名
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DaniellChiangAIZOOTech

AIZOOTech/FaceMaskDetection 排名第 8,957,有 2,120 个星标。

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AIZOOTech/FaceMaskDetection 排名第 8,957,此处有 8 个同类条目;OpenCoder-llm/OpenCoder-llm 排名第 8,952。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Models
子类别Object detection
星标数2,120
分叉数614
贡献者2
创建日期2020-02-18
更新日期2026-10-14
1 天星标数0
7 天星标数-1
排名8,957
上次排名9,012
排名变化55
7 天星标排名16,948
上次 7 天星标排名16,946
7 天星标排名变化-2
总星标排名6,054
上次总星标排名6,044
总星标排名变化-10
README 摘录

FaceMaskDetection 中文版 | English version We open source all the popular deep learning frameworks' model and inference code to do face mask detection. ** We make face mask detection models with five mainstream deep learning frameworks (PyTorch、TensorFlow、Keras、MXNet和caffe) open sourced, and the corresponding inference codes.

常见问题

记录了多少星标和分叉?

AIZOOTech/FaceMaskDetection 有 2,120 个星标和 614 个分叉。

仓库排名是多少?

AIZOOTech/FaceMaskDetection 排名第 8,957。

记录的语言和类别是什么?

AIZOOTech/FaceMaskDetection 有 2,120 个星标,语言为 Jupyter Notebook,类别为 Models。

哪个仓库排名最接近?

AIZOOTech/FaceMaskDetection 与 OpenCoder-llm/OpenCoder-llm 比较,后者排名第 8,952,有 2,123 个星标。

README

FaceMaskDetection

中文版 | English version

We open source all the popular deep learning frameworks' model and inference code to do face mask detection.

  • [x] PyTorch
  • [x] TensorFlow(include tflite and pb model)
  • [x] Keras
  • [x] MXNet
  • [x] Caffe
  • [x] Paddle
  • [x] OpenCV dnn

** Detect faces and determine whether they are wearing mask. **

** First of all, we hope the people in the world defeat COVID-2019 as soon as possible. Stay strong, all the countries in the world.**

  • We make face mask detection models with five mainstream deep learning frameworks (PyTorch、TensorFlow、Keras、MXNet和caffe) open sourced, and the corresponding inference codes.

  • We published 7971 images to train the models. The dataset is composed of WIDER Face and MAFA, we verified some wrong annotations. You can download here from Google drive, if you can not visit Google, you can download it from BaiduDisk, click here to know how to download.

We deployed a web page, you can click the link to experience it. The page is writen by Tensorflow.js.

AIZOO face mask detection

Model structure

We used the structure of SSD. However, in order to make it run quickly in the browser, the backbone network is lite. The total model only has 1.01M parametes.

Input size of the model is 260x260, the backbone network only has 8 conv layers. The total model has only 24 layers with the location and classification layers counted.

SSD anchor configurtion is show bellow:

multibox layers feature map size anchor size aspect ratio)
First 33x33 0.04,0.056 1,0.62,0.42
Second 17x17 0.08,0.11
Third 9x9 0.16,0.22 1,0.62,0.42
Forth 5x5 0.32,0.45 1,0.62,0.42
Fifth 3x3 0.64,0.72 1,0.62,0.42

How to run

opencv

python opencv_dnn_infer.py  --img-path /path/to/your/img

paddle

on image:

python paddle_infer.py  --img-path /path/to/your/img

pytorch

on image:

python pytorch_infer.py  --img-path /path/to/your/img

on video:

python pytorch_infer.py --img-mode 0 --video-path /path/to/video  
# If you want to run with camera video, set  video_path to be 0
python pytorch_infer.py --img-mode 0 --video-path 0

TensorFlow/Keras/MXNet/Caffe

The other four frameworks running method is similar to pytorch, just replace pytorchwith tensorflow, keras,caffe,mxnet, if you want to use tensorflow, just run:

python tensorflow_infer.py  --img-path /path/to/your/img

Attention,for caffe's inference ,we use permute layer,so that we should use caffe-ssd,you can use opencv-dnn to do the inference.

Appendix

Feedback

If you use wechat, you can follow us.

** If you use Wechat, scan the code to add me on wechat **

Model structure

We merge the BN to Conv layers in order to accelerate the inference speed.

Testset PR curve

Our demo page

aizoo.com face mask detection demo

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