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https://github.com/qengineering/yolov6-ncnn-jetson-nano

YoloV6 for a Jetson Nano using ncnn.
https://github.com/qengineering/yolov6-ncnn-jetson-nano

deep-learning jetson-nano ncnn ncnn-framework ncnn-model yolov6 yolov6-nano

Last synced: 25 days ago
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YoloV6 for a Jetson Nano using ncnn.

Host: GitHub
URL: https://github.com/qengineering/yolov6-ncnn-jetson-nano
Owner: Qengineering
License: bsd-3-clause
Created: 2022-08-03T12:52:49.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-09-30T11:17:41.000Z (about 1 year ago)
Last Synced: 2023-09-30T12:23:46.677Z (about 1 year ago)
Topics: deep-learning, jetson-nano, ncnn, ncnn-framework, ncnn-model, yolov6, yolov6-nano
Language: C++
Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
Size: 8.36 MB
Stars: 4
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # YoloV6 Jetson Nano

![output image]( https://qengineering.eu/images/ParkingYoloV7.jpg )

## YoloV6 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://tech.meituan.com/2022/06/23/yolov6-a-fast-and-accurate-target-detection-framework-is-opening-source.html



Special made for a Jetson Nano 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-Jetson-Nano) | 320x320 | 20.6  |  26.2 FPS | 13.0 FPS | 43.2 FPS |36.0 FPS |

| [NanoDet Plus](https://github.com/Qengineering/NanoDetPlus-ncnn-Jetson-Nano) | 416x416 | 30.4  |  18.5 FPS | 5.0 FPS | 30.0 FPS | 24.9 FPS |

| [PP-PicoDet](https://github.com/Qengineering/PP-PicoDet-ncnn-Jetson-Nano) | 320x320 | 27.0  |  24.0 FPS | 7.5 FPS | 53.7 FPS | 46.7 FPS |

| [YoloFastestV2](https://github.com/Qengineering/YoloFastestV2-ncnn-Jetson-Nano) | 352x352 | 24.1 |  38.4 FPS | 18.8 FPS | 78.5 FPS | 65.4 FPS | 

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

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

| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano) | 416x416 tiny | 21.7 | 16.1 FPS | 3.4 FPS | 26.8 FPS | 22.4 FPS | 

| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano) | 608x608 full | 45.3 | 1.3 FPS | 0.2 FPS | 1.82 FPS | 1.5 FPS | 

| [YoloV5](https://github.com/Qengineering/YoloV5-ncnn-Jetson-Nano) | 640x640 small | 22.5 | 5.0 FPS | 1.6 FPS | 14.9 FPS | 12.5 FPS | 

| [YoloV6](https://github.com/Qengineering/YoloV6-ncnn-Jetson-Nano) | 640x640 nano | 35.0 | 10.5 FPS | 2.7 FPS | 25.0 FPS | 20.8 FPS | 

| [YoloV7](https://github.com/Qengineering/YoloV5-ncnn-Jetson-Nano) | 640x640 tiny | 38.7 | 8.5 FPS | 2.1 FPS | 21.5 FPS | 17.9 FPS | 

| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Jetson-Nano) | 640x640 nano | 37.3 | 14.5 FPS | 3.1 FPS | 20.0 FPS | 16.3 FPS | 

| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Jetson-Nano) | 640x640 small | 44.9 | 4.5 FPS | 1.47 FPS | 11.0 FPS | 9.2 FPS | 

| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 416x416 nano | 25.8 | 22.6 FPS | 7.0 FPS | 34.2 FPS | 28.5 FPS | 

| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 416x416 tiny | 32.8 | 11.35 FPS | 2.8 FPS | 21.8 FPS | 18.1 FPS | 

| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 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:

- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-jetson-nano.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/YoloV6-ncnn-Jetson-Nano/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 


YoloV6.cpb 


yolo.cpp 


yolo.h 


yoloV6main.cpp 


yolov6-tiny.bin 


yolov6-tiny.param 


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

## Running the app.

To run the application load the project file YoloV6.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).



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

### Dynamic sizes.

YoloV6 can handle different input resolutions without changing the deep learning model.


On line 28 of `yolov6main.cpp` you can change the `target_size` (default 640).


Decreasing the size to say 412 will speed up the inference time. On the other hand, the resizing makes the image less detailed; the model will no longer detect all objects.



Many thanks to [nihui](https://github.com/nihui/) and [FeiGeChuanShu](https://github.com/FeiGeChuanShu)



![output image]( https://qengineering.eu/images/BusstopYoloV7.jpg )

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

[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)