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