Yolov10cpp

<h1 align="center">Yolo V10 cpp</h1> <h3 align="center"> Jose Sarmiento | josedanielsarmiento219@gmail.com</h3>

Resumen

The next repository aims to provide a basic c++ script using std 17 over, to do it and consider the speed The code use OpenCv 4.9.0_8 and Onnx 1.17.1 to manipulate the image and inference the model. Note that Opncv don't support a native integration because yolov10 integra A top K layer in their architecture.

Prepare the code

1. Download de model you want

• yolov10n
• yolov10s
• yolov10m
• yolov10b
• yolov10l
• yolov10x

    python download_model.py  --model {MODEL_SELECTED}

Install packages

    conda create -n yolov10 python=3.9
    conda activate yolov10

    git clone https://github.com/THU-MIG/yolov10
    cd yolov10

    pip install -r requirements.txt
    pip install -e .

    cd ..

Convert model

    yolo export model=yolov10n.pt format=onnx

Dependencies

1. ffmpeg
2. Opnecv
3. onnxruntime

• MacOs

    brew install ffmpeg 
    brew install opencv
    brew install onnxruntime

• Ubuntu: Unfortunately, onnx runtime is no available using native apt-get

You can use python

sudo apt-get update
sudo apt-get install python3-pip
pip3 install onnxruntime

dotnet

dotnet add package Microsoft.ML.OnnxRuntime

How to run this code

1. Using Cmake, Recommended

    mkdir build
    cd build
    cmake ..
    make

2. Run the following command

static images

    ./yolov10_cpp [MODEL_PATH] [IMAGE_PATH]

realtime

    ./yolov10_cpp_video [MODEL_PATH] [SOURCE]

Results

our cpp binding | python binding

<p align="center"> <img src="./assets/cpp/bus.jpg" alt="Image 1" width="45%" style="margin-right: 10px;"/> <img src="./assets/yolo/bus.jpg" alt="Image 2" width="45%"/> </p> <p align="center"> <img src="./assets/cpp/zidane.jpg" alt="Image 1" width="45%" style="margin-right: 10px;"/> <img src="./assets/yolo/zidane.jpg" alt="Image 2" width="45%"/> </p>

source = Apple M3 PRO

| Command Line Execution | Resource Utilization | |---------------------------------------------------------------------|------------------------------------------------------| | ./yolov10_cpp ../yolov10n.onnx ../bus.jpg | 0.46s user, 0.10s system, 94% CPU, 0.595s total | | yolo detect predict model=yolov10n.onnx source=bus.jpg | 1.69s user, 2.44s system, 291% CPU, 1.413s total |

Future plans

1. Modularize the components. ✅
2. Make a example to video real time. ✅
3. Support Cuda. ?

Inspiration

Ultraopxt

Reference

[1] Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2405.14458