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https://github.com/Qengineering/YoloV7-ncnn-Raspberry-Pi-4

YoloV7 for a bare Raspberry Pi using ncnn.
https://github.com/Qengineering/YoloV7-ncnn-Raspberry-Pi-4

deep-learning ncnn ncnn-framework ncnn-model orange-pi-5 raspberry-pi-4 raspberry-pi-64-os rock-5 rock-pi-5 yolov7 yolov7-tiny

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YoloV7 for a bare Raspberry Pi using ncnn.

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README 

        
# YoloV7 Raspberry Pi 4

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

## YoloV7 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/2207.02696.pdf



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 | |||| 

| [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)



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



## 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/YoloV7-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 


YoloV7.cpb 


yolo.cpp 


yolo.h 


yoloV7main.cpp 


yolov7-tiny.bin 


yolov7-tiny.param 



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



## Running the app.

To run the application load the project file YoloV7.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.

YoloV7 can handle different input resolutions without changing the deep learning model.


On line 28 of `yolov7main.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 [Xiang Shin Wuu](https://github.com/xiang-wuu)



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



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