YoloX-ncnn-Jetson-Nano

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YoloX-ncnn-Jetson-Nano

(★ 20)

YoloX for a Jetson Nano using ncnn.

YoloX-ncnn-Jetson-Nano 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# YoloX Jetson Nano
![output image](https://qengineering.eu/images/test_parkX.webp)
## YoloX 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/2107.08430.pdf 
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) 
- 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/YoloX-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 
YoloX.cpb 
yoloX.cpp 
yoloxS.bin 
yoloxS.param 
yoloxT.bin 
yoloxT.param 
yoloxN.bin 
yoloxN.param

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

## Running the app.
To run the application load the project file YoloX.cbp in Code::Blocks. 
Next, 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_busX.webp)

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

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