lzb-James / YoloV4-ncnn-Raspberry-Pi-4

YoloV4 on a bare Raspberry Pi 4 with ncnn framework

YoloV4-ncnn-Raspberry-Pi-4

YoloV4 with the ncnn framework.

The frame rate is about 3 FPS (RPi 64 bit OS, overclocked to 1950 MHz)
Paper: https://arxiv.org/pdf/2004.10934.pdf
Size: 608x608

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

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 4.3
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

Running the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit/archive/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 folder must now look like this:
dog.jpg
mumbai.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 Gdrive)
yolov4.param

Many thanks to nihui again!