| # YOLOv8 OpenVINO Inference in C++ 🦾 | |
| Welcome to the YOLOv8 OpenVINO Inference example in C++! This guide will help you get started with leveraging the powerful YOLOv8 models using OpenVINO and OpenCV API in your C++ projects. Whether you're looking to enhance performance or add flexibility to your applications, this example has got you covered. | |
| ## 🌟 Features | |
| - 🚀 **Model Format Support**: Compatible with `ONNX` and `OpenVINO IR` formats. | |
| - ⚡ **Precision Options**: Run models in `FP32`, `FP16`, and `INT8` precisions. | |
| - 🔄 **Dynamic Shape Loading**: Easily handle models with dynamic input shapes. | |
| ## 📋 Dependencies | |
| To ensure smooth execution, please make sure you have the following dependencies installed: | |
| | Dependency | Version | | |
| | ---------- | -------- | | |
| | OpenVINO | >=2023.3 | | |
| | OpenCV | >=4.5.0 | | |
| | C++ | >=14 | | |
| | CMake | >=3.12.0 | | |
| ## ⚙️ Build Instructions | |
| Follow these steps to build the project: | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/ultralytics/ultralytics.git | |
| cd ultralytics/YOLOv8-OpenVINO-CPP-Inference | |
| ``` | |
| 2. Create a build directory and compile the project: | |
| ```bash | |
| mkdir build | |
| cd build | |
| cmake .. | |
| make | |
| ``` | |
| ## 🛠️ Usage | |
| Once built, you can run inference on an image using the following command: | |
| ```bash | |
| ./detect <model_path.{onnx, xml}> <image_path.jpg> | |
| ``` | |
| ## 🔄 Exporting YOLOv8 Models | |
| To use your YOLOv8 model with OpenVINO, you need to export it first. Use the command below to export the model: | |
| ```bash | |
| yolo export model=yolov8s.pt imgsz=640 format=openvino | |
| ``` | |
| ## 📸 Screenshots | |
| ### Running Using OpenVINO Model | |
|  | |
| ### Running Using ONNX Model | |
|  | |
| ## ❤️ Contributions | |
| We hope this example helps you integrate YOLOv8 with OpenVINO and OpenCV into your C++ projects effortlessly. Happy coding! 🚀 | |