{"id":21802502,"url":"https://github.com/qengineering/yolact-ncnn-raspberry-pi-4","last_synced_at":"2026-03-06T22:05:44.313Z","repository":{"id":41507377,"uuid":"281440825","full_name":"Qengineering/Yolact-ncnn-Raspberry-Pi-4","owner":"Qengineering","description":"Yolact running on the ncnn framework on a bare Raspberry Pi 4 with 64 OS, overclocked to 1950 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example on our [SD-image](https://github.com/Qengineering/RPi-image)\n# Yolact-ncnn on Raspberry Pi 64 bits\n![output image]( https://qengineering.eu/images/Yolact_result_zebra.png )\u003cbr/\u003e\n## Yolact with the ncnn framework. \u003cbr/\u003e\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\nThe frame rate is about 3.5 sec per image (RPi overclocked to 1950 MHz)\u003cbr/\u003e\nSpecial made for a bare Raspberry Pi see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) \u003cbr/\u003e\u003cbr/\u003e\nPaper: https://openaccess.thecvf.com/content_ICCV_2019/papers/Bolya_YOLACT_Real-Time_Instance_Segmentation_ICCV_2019_paper.pdf \u003cbr/\u003e\n\n------------\n\n## Benchmark.\n| Model  | size | objects | mAP |  RPi 4 64-OS 1950 MHz |\n| ------------- | :-----:  | :-----:  | :-------------:  | :-------------: |\n| [YoloV5n](https://github.com/Qengineering/YoloV5-segmentation-ncnn-RPi4) | 640x640 nano| 80 | 28.0 | 1.4 - 2.0  FPS |\n| [YoloV5s](https://github.com/Qengineering/YoloV5-segmentation-ncnn-RPi4) | 640x640 small| 80 | 37.4 | 1.0 FPS | \n| [YoloV5l](https://github.com/Qengineering/YoloV5-segmentation-ncnn-RPi4) | 640x640 large| 80 | 49.0 | 0.25 FPS | \n| [YoloV5x](https://github.com/Qengineering/YoloV5-segmentation-ncnn-RPi4) | 640x640 x-large| 80 | 50.7 | 0.15 FPS |\n| Yoact | 550x550 | 80 | 28.2 | 0.28 FPS |\n\n------------\n\n## Dependencies.\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 4.3](https://qengineering.eu/install-opencv-4.3-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/Yolact-ncnn/archive/refs/heads/master.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \ndog.jpg \u003cbr/\u003e\nelephant.jpeg \u003cbr/\u003e\ngirafe.jpeg \u003cbr/\u003e\nmumbai.jpg \u003cbr/\u003e\nonyx.jpeg \u003cbr/\u003e\nresult_elephant.png \u003cbr/\u003e\nresult_zebra.png \u003cbr/\u003e\nYolact.cpb \u003cbr/\u003e\nyolact.cpp \u003cbr/\u003e\nyolact.bin (download this file from [Gdrive](https://drive.google.com/file/d/1vu3GGOEWh-jmedM-cvoqzhGzaZaOQB9k) )\u003cbr/\u003e\nyolact.param \u003cbr/\u003e\n\n------------\n\n## Running the app.\nRun Yolact.cpb with Code::Blocks.\u003cbr/\u003e\nFor more info 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!\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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fyolact-ncnn-raspberry-pi-4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Fyolact-ncnn-raspberry-pi-4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fyolact-ncnn-raspberry-pi-4/lists"}