Paper: https://arxiv.org/pdf/2004.10934.pdf
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 |
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 |
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/YoloV4-ncnn-Raspberry-Pi-4/archive/refs/heads/master.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
YoloV4.cpb
yoloV4.cpp
yolov4-tiny-opt.bin
yolov4-tiny-opt.param
If you want to run the full YoloV4 version you need:
yolov4.bin (download this 245 MB file from Mega)
yolov4.param
To run the application load the project file YoloV4.cbp in Code::Blocks.
Next, follow the instructions at Hands-On.
Many thanks to nihui again!