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https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4

MobileNetV2_YOLOV3 for ncnn framework
https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4

cpp deep-learning mobilenet mobilenetv2-yolov3 ncnn ncnn-framework ncnn-yo orange-pi-5 raspberry raspberry-pi raspberry-pi-3 raspberry-pi-4 rock-5 rock-pi-5 yolo

Last synced: 12 days ago
JSON representation

MobileNetV2_YOLOV3 for ncnn framework

Host: GitHub
URL: https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4
Owner: Qengineering
License: bsd-3-clause
Created: 2019-09-03T16:29:12.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-09-30T11:02:58.000Z (9 months ago)
Last Synced: 2024-02-29T09:31:17.007Z (4 months ago)
Topics: cpp, deep-learning, mobilenet, mobilenetv2-yolov3, ncnn, ncnn-framework, ncnn-yo, orange-pi-5, raspberry, raspberry-pi, raspberry-pi-3, raspberry-pi-4, rock-5, rock-pi-5, yolo
Language: C++
Homepage: https://qengineering.eu/opencv-c-examples-on-raspberry-pi.html
Size: 19.1 MB
Stars: 25
Watchers: 4
Forks: 2
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-yolo-object-detection - Qengineering/YoloV3-ncnn-Raspberry-Pi-4 - ncnn-Raspberry-Pi-4?style=social"/> : YoloV3 Raspberry Pi 4. (Lighter and Deployment Frameworks)

README

        # YoloV3 Raspberry Pi 4

![output image]( https://qengineering.eu/images/test_parkV3.jpg )

## YoloV3 with the ncnn framework. 


[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)



Paper: https://arxiv.org/pdf/1506.02640.pdf 


Training set: VOC2007 


Specially 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 | mAP | Jetson Nano | RPi 4 1950 | RPi 5 2900 | Rock 5 | 

| ------------- | :-----:  | :-----:  | :-------------:  | :-------------: | :-----: | :-----: |

| [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 |

| [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 |

| [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 |

| [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 | 

| [YoloV2](https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4) ²⁰| 416x416 | 19.2 |  10.1 FPS | 3.0 FPS | 24.0 FPS | 20.0 FPS | 

| [YoloV3](https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4) ²⁰| 352x352 tiny | 16.6 | 17.7 FPS | 4.4 FPS | 18.1 FPS | 15.0 FPS | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

| [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 | 

²⁰ Recognize 20 objects (VOC) instead of 80 (COCO)

------------

## 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/MobileNetV2_YOLOV3_ncnn/archive/refs/heads/master.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 


MobiYO.cpb 


MobiYO.cpp 


mobilenetv2_yolov3.bin 


mobilenetv2_yolov3.param 



------------

## Running the app.

To run the application load the project file MobiYO.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 [nihui](https://github.com/nihui/) again!



![output image]( https://qengineering.eu/images/test_busV3.jpg )

------------

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