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Yolact-ncnn on Raspberry Pi 64 bits

Yolact with the ncnn framework.

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!

Qengineering/Yolact-ncnn-Raspberry-Pi-4