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YoloV3 Raspberry Pi 4\n![output image]( https://qengineering.eu/images/test_parkV3.jpg )\n## YoloV3 with the ncnn framework. \u003cbr/\u003e\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\nPaper: https://arxiv.org/pdf/1506.02640.pdf \u003cbr/\u003e\nTraining set: VOC2007 \u003cbr/\u003e\nSpecially made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)\n\n------------\n\n## Benchmark.\n| Model  | size | mAP | Jetson Nano | RPi 4 1950 | RPi 5 2900 | Rock 5 | \n| ------------- | :-----:  | :-----:  | :-------------:  | :-------------: | :-----: | :-----: |\n| [NanoDet](https://github.com/Qengineering/NanoDet-ncnn-Raspberry-Pi-4) | 320x320 | 20.6  |  26.2 FPS | 13.0 FPS | 43.2 FPS |36.0 FPS |\n| [NanoDet Plus](https://github.com/Qengineering/NanoDetPlus-ncnn-Raspberry-Pi-4) | 416x416 | 30.4  |  18.5 FPS | 5.0 FPS | 30.0 FPS | 24.9 FPS |\n| [PP-PicoDet](https://github.com/Qengineering/PP-PicoDet-ncnn-Raspberry-Pi-4) | 320x320 | 27.0  |  24.0 FPS | 7.5 FPS | 53.7 FPS | 46.7 FPS |\n| [YoloFastestV2](https://github.com/Qengineering/YoloFastestV2-ncnn-Raspberry-Pi-4) | 352x352 | 24.1 |  38.4 FPS | 18.8 FPS | 78.5 FPS | 65.4 FPS | \n| [YoloV2](https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4) \u003csup\u003e20\u003c/sup\u003e| 416x416 | 19.2 |  10.1 FPS | 3.0 FPS | 24.0 FPS | 20.0 FPS | \n| [YoloV3](https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4) \u003csup\u003e20\u003c/sup\u003e| 352x352 tiny | 16.6 | 17.7 FPS | 4.4 FPS | 18.1 FPS | 15.0 FPS | \n| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) | 416x416 tiny | 21.7 | 16.1 FPS | 3.4 FPS | 26.8 FPS | 22.4 FPS | \n| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) | 608x608 full | 45.3 | 1.3 FPS | 0.2 FPS | 1.82 FPS | 1.5 FPS | \n| [YoloV5](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) | 640x640 small | 22.5 | 5.0 FPS | 1.6 FPS | 14.9 FPS | 12.5 FPS | \n| [YoloV6](https://github.com/Qengineering/YoloV6-ncnn-Raspberry-Pi-4) | 640x640 nano | 35.0 | 10.5 FPS | 2.7 FPS | 25.0 FPS | 20.8 FPS | \n| [YoloV7](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) | 640x640 tiny | 38.7 | 8.5 FPS | 2.1 FPS | 21.5 FPS | 17.9 FPS | \n| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) | 640x640 nano | 37.3 | 14.5 FPS | 3.1 FPS | 20.0 FPS | 16.3 FPS | \n| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) | 640x640 small | 44.9 | 4.5 FPS | 1.47 FPS | 11.0 FPS | 9.2 FPS | \n| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 416x416 nano | 25.8 | 22.6 FPS | 7.0 FPS | 34.2 FPS | 28.5 FPS | \n| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 416x416 tiny | 32.8 | 11.35 FPS | 2.8 FPS | 21.8 FPS | 18.1 FPS | \n| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 640x640 small | 40.5 | 3.65 FPS | 0.9 FPS | 9.0 FPS | 7.5 FPS | \n\n\u003cb\u003e\u003csup\u003e20\u003c/sup\u003e\u003c/b\u003e Recognize 20 objects (VOC) instead of 80 (COCO)\n\n------------\n\n## Dependencies.\u003cbr/\u003e\nTo run the application, you have to:\n- 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](https://qengineering.eu/install-raspberry-64-os.html) \u003cbr/\u003e\n- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) \u003cbr/\u003e\n- OpenCV 64-bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) \u003cbr/\u003e\n- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)\n\n------------\n\n## Installing the app.\nTo extract and run the network in Code::Blocks \u003cbr/\u003e\n$ mkdir *MyDir* \u003cbr/\u003e\n$ cd *MyDir* \u003cbr/\u003e\n$ wget https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn/archive/refs/heads/master.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip, LICENSE and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm LICENSE \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \nparking.jpg \u003cbr/\u003e\nbusstop.jpg \u003cbr/\u003e\nMobiYO.cpb \u003cbr/\u003e\nMobiYO.cpp \u003cbr/\u003e\nmobilenetv2_yolov3.bin \u003cbr/\u003e\nmobilenetv2_yolov3.param \u003cbr/\u003e\u003cbr/\u003e\n\n------------\n\n## Running the app.\nTo run the application load the project file MobiYO.cbp in Code::Blocks. More info or\u003cbr/\u003e \nif you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).\u003cbr/\u003e\u003cbr/\u003e\nMany thanks to [nihui](https://github.com/nihui/) again!\u003cbr/\u003e\u003cbr/\u003e\n![output image]( https://qengineering.eu/images/test_busV3.jpg )\n\n------------\n\n[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL) \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fyolov3-ncnn-raspberry-pi-4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Fyolov3-ncnn-raspberry-pi-4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fyolov3-ncnn-raspberry-pi-4/lists"}