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YoloV3 Raspberry Pi 4

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YoloV3 with the ncnn framework.

License

Paper: https://arxiv.org/pdf/1506.02640.pdf
Training set: VOC2007
Special made for a bare Raspberry Pi 4 see Q-engineering deep learning examples


Benchmark.

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

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)

Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn/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
MobiYO.cpb
MobiYO.cpp
mobilenetv2_yolov3.bin
mobilenetv2_yolov3.param


Running the app.

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

Many thanks to nihui again!
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