/Yolact-ncnn-Raspberry-Pi-4

Yolact running on the ncnn framework on a bare Raspberry Pi 4 with 64 OS, overclocked to 1950 MHz

Primary LanguageC++BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

output image Find this example on our SD-image

Yolact-ncnn on Raspberry Pi 64 bits

output image

Yolact with the ncnn framework.

License

The frame rate is about 3.5 sec per image (RPi overclocked to 1950 MHz)
Special made for a bare Raspberry Pi see Q-engineering deep learning examples

Paper: https://openaccess.thecvf.com/content_ICCV_2019/papers/Bolya_YOLACT_Real-Time_Instance_Segmentation_ICCV_2019_paper.pdf


Benchmark.

Model size objects mAP RPi 4 64-OS 1950 MHz
YoloV5n 640x640 nano 80 28.0 1.4 - 2.0 FPS
YoloV5s 640x640 small 80 37.4 1.0 FPS
YoloV5l 640x640 large 80 49.0 0.25 FPS
YoloV5x 640x640 x-large 80 50.7 0.15 FPS
Yoact 550x550 80 28.2 0.28 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 4.3
  • 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/Yolact-ncnn/archive/refs/heads/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
elephant.jpeg
girafe.jpeg
mumbai.jpg
onyx.jpeg
result_elephant.png
result_zebra.png
Yolact.cpb
yolact.cpp
yolact.bin (download this file from Gdrive )
yolact.param


Running the app.

Run Yolact.cpb with Code::Blocks.
For more info follow the instructions at Hands-On.

Many thanks to nihui again!


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