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:

Clone the repository:

git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics/YOLOv8-OpenVINO-CPP-Inference

Create a build directory and compile the project:
```
mkdir build
cd build
cmake ..
make
```

🛠️ Usage

Once built, you can run inference on an image using the following command:

./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:

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! 🚀

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