LFFD face detection Raspberry Pi 4

LFFD face detection with the MNN framework.

Paper: https://arxiv.org/pdf/1904.10633.pdf

Special made for a bare Raspberry Pi 4 see Q-engineering deep learning examples

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

Dependencies.

To run the application, you have to:

A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
The Alibaba's MNN framework installed. (https://qengineering.eu/install-mnn-on-raspberry-pi-4.html)
OpenCV 64 bit installed. Install OpenCV 4.5
Code::Blocks installed. ($ sudo apt-get install codeblocks)

Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/LFFD-MNN-Raspberry-Pi-4/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md

Your MyDir folder must now look like this:
images folder
Walks2.mp4
FaceDetection_LFFD_MNN.cpb
main.cpp
LFFD_MNN.h
LFFD_MNN.cpp
symbol_10_320_20L_5scales_v2_deploy.mnn
symbol_10_560_25L_8scales_v1_deploy.mnn

Running the app.

To run the application load the project file FaceDetection_LFFD_MNN.cbp in Code::Blocks.
Next, follow the instructions at Hands-On.