Face Detection Raspberry Pi 32 64 Bits Save

Super fast face detection on Raspberry Pi 4

Project README

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Face Detection on Raspberry Pi 32/64 bits

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Super fast face detection up to 80 FPS on a bare Raspberry Pi 4.

This is a ultra fast C++ implementation of the face detector of Linzaer running on a MNN framework.
https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB.

Paper: https://arxiv.org/abs/1905.00641.pdf
Size: 320x320

Special made for a bare Raspberry Pi see https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html


Frameworks.

Three frameworks are supported:

  • Alibaba's MNN framework
  • Tencent ncnn framework
  • OpenCV dnn
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    The frame rate is based upon the average execution time of the single frames.
    Loading frames from a file, plotting boxes, and showing the result on the screen are not taken into account.

    The MNN framework has also 8 bit quantized models. These are very fast.

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    See the video at https://youtu.be/DERA83C9K2A

Thanks.

https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB


Benchmark.

Model framework model size mAP Jetson Nano
2015 MHz
RPi 4 64-OS
1950 MHz
Ultra-Light-Fast ncnn slim-320 320x240 67.1 - FPS 26 FPS
Ultra-Light-Fast ncnn RFB-320 320x240 69.8 - FPS 23 FPS
Ultra-Light-Fast MNN slim-320 320x240 67.1 70 FPS 65 FPS
Ultra-Light-Fast MNN RFB-320 320x240 69.8 60 FPS 56 FPS
Ultra-Light-Fast OpenCV slim-320 320x240 67.1 48 FPS 40 FPS
Ultra-Light-Fast OpenCV RFB-320 320x240 69.8 43 FPS 35 FPS
Ultra-Light-Fast + Landmarks ncnn slim-320 320x240 67.1 50 FPS 24 FPS
LFFD ncnn 5 stage 320x240 88.6 16.4 FPS 4.85 FPS
LFFD ncnn 8 stage 320x240 88.6 11.7 FPS 3.45 FPS
LFFD MNN 5 stage 320x240 88.6 2.6 FPS 2.17 FPS
LFFD MNN 8 stage 320x240 88.6 1.8 FPS 1.49 FPS

RFB-320

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slim_320

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LFFD-5

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LFFD-8

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Open Source Agenda is not affiliated with "Face Detection Raspberry Pi 32 64 Bits" Project. README Source: Qengineering/Face-detection-Raspberry-Pi-32-64-bits

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