https://github.com/qengineering/yolov5-segmentation-ncnn-rpi4
YoloV5 segmentation for a bare Raspberry Pi 4
https://github.com/qengineering/yolov5-segmentation-ncnn-rpi4
aarch64 cpp deep-learning ncnn ncnn-framework ncnn-model raspberry-pi-4 raspberry-pi-64-os segmentation yolov5 yolov5-seg
Last synced: 5 months ago
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YoloV5 segmentation for a bare Raspberry Pi 4
- Host: GitHub
- URL: https://github.com/qengineering/yolov5-segmentation-ncnn-rpi4
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2022-12-29T15:44:51.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-29T17:00:49.000Z (over 2 years ago)
- Last Synced: 2024-11-27T11:36:16.013Z (7 months ago)
- Topics: aarch64, cpp, deep-learning, ncnn, ncnn-framework, ncnn-model, raspberry-pi-4, raspberry-pi-64-os, segmentation, yolov5, yolov5-seg
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 17.5 MB
- Stars: 4
- Watchers: 2
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YoloV5 segmentation Raspberry Pi 4
## YoloV5 segmentation with the ncnn framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Special 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)
------------
## 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](https://github.com/Qengineering/Yolact-ncnn-Raspberry-Pi-4) | 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](https://qengineering.eu/install-raspberry-64-os.html)
- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html)
- 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/YoloV5-segmentation-ncnn-RPi4/archive/refs/heads/main.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
YoloV5-seg.cpb
main.cpp
yolov5n-seg.bin
yolov5n-seg.param
yolov5s-seg.bin
yolov5s-seg.param
------------
## Running the app.
To run the application load the project file YoloV5-seg.cbp in Code::Blocks. More info or
if 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).
Many thanks to [FeiGeChuanShu](https://github.com/FeiGeChuanShu)!
------------
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)