Paper: https://towardsdatascience.com/yolo-v5-is-here-b668ce2a4908
Special made for a bare Raspberry Pi 4, see Q-engineering deep learning examples
| Model | size | objects | mAP | Jetson Nano 2015 MHz | RPi 4 64-OS 1950 MHz |
|---|---|---|---|---|---|
| NanoDet | 320x320 | 80 | 20.6 | 28.2 FPS | 13.0 FPS |
| YoloFastestV2 | 352x352 | 80 | 24.1 | 38.4 FPS | 18.8 FPS |
| YoloV2 | 416x416 | 20 | 19.2 | 10.1 FPS | 3.0 FPS |
| YoloV3 | 352x352 tiny | 20 | 16.6 | 17.7 FPS | 4.4 FPS |
| YoloV4 | 416x416 tiny | 80 | 21.7 | 11.2 FPS | 3.4 FPS |
| YoloV4 | 608x608 full | 80 | 45.3 | 0.7 FPS | 0.2 FPS |
| YoloV5 | 640x640 small | 80 | 22.5 | 4.0 FPS | 1.6 FPS |
| YoloX | 416x416 nano | 80 | 25.8 | 17.6 FPS | 7.0 FPS |
| YoloX | 416x416 tiny | 80 | 32.8 | 8.3 FPS | 2.8 FPS |
| YoloX | 640x640 small | 80 | 40.5 | 2.6 FPS | 0.9 FPS |
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)
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloV5-ncnn-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:
parking.jpg
busstop.jpg
YoloV5.cpb
yoloV5.cpp
yolov5s.bin
yolov5s.param
To run the application load the project file YoloV5.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 nihui again!
