| 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 |
| CenterFace | ncnn | - | 320x240 | 93 | 16.5 FPS | 6.8 FPS |
To run the application, you have to:
- The Alibaba's MNN framework installed. (https://qengineering.eu/install-mnn-on-raspberry-pi-4.html)
- OpenCV 32 bit installed. Install OpenCV 4.5
- Code::Blocks installed. (
$ sudo apt-get install codeblocks)
To run the application on a 64 OS, you have to:
- A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. (https://qengineering.eu/install-raspberry-64-os.html)
- The Alibaba's MNN framework installed. (https://qengineering.eu/install-mnn-on-raspberry-pi-4.html)
- OpenCV 64 bit installed. (https://qengineering.eu/install-opencv-4.3-on-raspberry-64-os.html)
- Code::Blocks installed. (
$ sudo apt-get install codeblocks)
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/Face-detection-Raspberry-Pi-32-64-bits/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your MyDir/MNN folder must now look like this:
Walk2.mp4 (demo video)
FaceDetection.cpb (code::blocks project file)
main.cpp (main example file)
MNN_UltraFace.cpp (Ultra face class)
MNN_UltraFace.hpp (Ultra face class)
RFB-320.mnn (fp32)
RFB-320-quant-ADMM-32.mnn (int8 ADMM quantified)
RFB-320-quant-KL-5792.mnn (int8 KL quantified)
slim_320.mnn (fp32)
slim_320-quant-ADMM-50.mnn (int8 ADMM quantified)
The RFB-320 model recognizes slightly more faces than slim_320 at the expense of a little bit of speed. The quantified models are a lot faster. Choose the one you like.
See the video at https://youtu.be/DERA83C9K2A
