YoloV5 segmentation Raspberry Pi 4

YoloV5 segmentation with the ncnn framework.

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

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Benchmark.

Model	size	objects	mAP	RPi 4 64-OS 1950 MHz
YoloV5n	640x640 nano	80	28.0	1.4 - 2.0 FPS
YoloV5s	640x640 small	80	37.4	1.0 FPS
YoloV5l	640x640 large	80	49.0	0.25 FPS
YoloV5x	640x640 x-large	80	50.7	0.15 FPS
Yoact	550x550	80	28.2	0.28 FPS

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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 Tencent ncnn framework installed. Install ncnn
  
• OpenCV 64 bit installed. Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

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Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloV5-segmentation-ncnn-RPi4/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:
parking.jpg
busstop.jpg
YoloV5-seg.cpb
main.cpp
yolov5n-seg.bin
yolov5n-seg.param
yolov5s-seg.bin
yolov5s-seg.param

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Running the app.

To run the application load the project file YoloV5-seg.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.

Many thanks to FeiGeChuanShu!

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