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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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