YoloV10-ncnn-Raspberry-Pi-4

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YoloV10-ncnn-Raspberry-Pi-4

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YoloV10 for a bare Raspberry Pi 4 or 5

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# YoloV10 Raspberry Pi 4 or 5
![output image](https://qengineering.eu/github/YoloV10_parking.jpg)
## YoloV10 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/2405.14458 
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.
Numbers in **FPS** and reflect only the inference timing. Grabbing frames, post-processing and drawing are not taken into account.

| Model | size | mAP | Jetson Nano | RPi 4 1950 | RPi 5 2900 | Rock 5 | RK35881 NPU | RK3566/682 NPU | Nano TensorRT | Orin TensorRT |
| ------------- | :-----: | :-----: | :-------------: | :-------------: | :-----: | :-----: | :-------------: | :-------------: | :-----: | :-----: |
| [NanoDet](https://github.com/Qengineering/NanoDet-ncnn-Raspberry-Pi-4) | 320x320 | 20.6 | 26.2 | 13.0 | 43.2 |36.0 |||||
| [NanoDet Plus](https://github.com/Qengineering/NanoDetPlus-ncnn-Raspberry-Pi-4) | 416x416 | 30.4 | 18.5 | 5.0 | 30.0 | 24.9 |||||
| [PP-PicoDet](https://github.com/Qengineering/PP-PicoDet-ncnn-Raspberry-Pi-4) | 320x320 | 27.0 | 24.0 | 7.5 | 53.7 | 46.7 |||||
| [YoloFastestV2](https://github.com/Qengineering/YoloFastestV2-ncnn-Raspberry-Pi-4) | 352x352 | 24.1 | 38.4 | 18.8 | 78.5 | 65.4 | ||||
| [YoloV2](https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4) 20| 416x416 | 19.2 | 10.1 | 3.0 | 24.0 | 20.0 | ||||
| [YoloV3](https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4) 20| 352x352 tiny | 16.6 | 17.7 | 4.4 | 18.1 | 15.0 | ||||
| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) | 416x416 tiny | 21.7 | 16.1 | 3.4 | 17.5 | 22.4 | ||||
| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) | 608x608 full | 45.3 | 1.3 | 0.2 | 1.82 | 1.5 | ||||
| [YoloV5](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) | 640x640 nano | 22.5 | 5.0 | 1.6 | 13.6 | 12.5 | 58.8 | 14.8 | 19.0 | 100 |
| [YoloV5](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) | 640x640 small | 22.5 | 5.0 | 1.6 | 6.3 | 12.5 | 37.7 | 11.7 | 9.25 | 100 |
| [YoloV6](https://github.com/Qengineering/YoloV6-ncnn-Raspberry-Pi-4) | 640x640 nano | 35.0 | 10.5 | 2.7 | 15.8 | 20.8 | 63.0 | 18.0 |||
| [YoloV7](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) | 640x640 tiny | 38.7 | 8.5 | 2.1 | 14.4 | 17.9 | 53.4 | 16.1 | 15.0 ||
| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) | 640x640 nano | 37.3 | 14.5 | 3.1 | 20.0 | 16.3 | 53.1 | 18.2 |||
| [YoloV8](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) | 640x640 small | 44.9 | 4.5 | 1.47 | 11.0 | 9.2 | 28.5 | 8.9 |||
| [YoloV9](https://github.com/Qengineering/YoloV9-ncnn-Raspberry-Pi-4) | 640x640 comp | 53.0 | 1.2 | 0.28 | 1.5 | 1.2 | |||| 
| [YoloV10](https://github.com/Qengineering/YoloV10-ncnn-Raspberry-Pi-4) | 640x640 nano | 38.0 | | 1.66 | | | |||| 
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 416x416 nano | 25.8 | 22.6 | 7.0 | 38.6 | 28.5 | ||||
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 416x416 tiny | 32.8 | 11.35 | 2.8 | 17.2 | 18.1 | ||||
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Raspberry-Pi-4) | 640x640 small | 40.5 | 3.65 | 0.9 | 4.5 | 7.5 | 30.0 | 10.0 |||

1 The Rock 5 and Orange Pi5 have the RK3588 on board. 
2 The Rock 3, Radxa Zero 3 and Orange Pi3B have the RK3566 on board. 
20 Recognize 20 objects (VOC) instead of 80 (COCO)

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

## Opset.
YoloV10 use layer operations yet unknown to ncnn. To let YoloV10 run on an ncnn framework, [zhouweigogogo](https://github.com/zhouweigogogo) has modified the onnx file by replacing these layers. 
At the same time, he expanded the original output to three. Some features, like variable input size, aren't supported now. 
Until the ncnn framework fully supports onnx opset 13, we face these limitations.

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

## Dependencies.
To run the application, you have to:
- A Raspberry Pi 4 or 5 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 (Optional). (```$ 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/YoloV10-ncnn-Raspberry-Pi-4/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 
YoloV10.cpb 
yoloV10.cpp 
yolov10n.ncnn.bin 
yolov10n.ncnn.param

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

## Running the app.
You can use **Code::Blocks**.
- Load the project file *.cbp in Code::Blocks.
- Select _Release_, not Debug.
- Compile and run with F9.
- You can alter command line arguments with _Project -> Set programs arguments..._

Or use **Cmake**.
```
$ cd *MyDir*
$ mkdir build
$ cd build
$ cmake ..
$ make -j4
```
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).

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

### Thanks.
A more than special thanks to [***THU-MIG***](https://github.com/THU-MIG), who adapted the YoloV10 to onnx models. 
And [***zhouweigogogo***](https://github.com/zhouweigogogo), who adapted the onnx model to the ncnn framework. 
![output image](https://qengineering.eu/github/YoloV10_busstop.jpg)

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

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