README.md

YOLOv8.OpenVino.cpp

YOLOv8 is a state-of-the-art object detector by ultralytics. This project implements the YOLOv8 object detector in C++ with an OpenVINO backend to speed up inference performance.

Features

• Supports both image and video inference.
• Provides faster inference speeds.

Prerequisites

Tested on Macbook Pro M1.

• CMake v3.8+ - Download from https://cmake.org/.
• OpenVINO Toolkit 2022.1+ - Tested on version 2023.1.0. Download from here.
• OpenCV v4.6+ - Tested on version v4.8.0_7. Download from here.

Getting Started

1. Set the OpenCV_DIR environment variable to point to your ../../opencv/build directory.

2. Set the OpenVINO_DIR environment variable to point to your ../../openvino/runtime/cmake directory.

3. Run the following build commands:

   For Mac with VS Developer Command Prompt:

   cd <git-directory>
   
   # Install dependencies
   brew install openvino
   brew install opencv
   
   # Build
   cmake -S. -Bbuild -DCMAKE_BUILD_TYPE=Release
   cd build
   make

Inference

1. Export the ONNX file:

   pip install ultralytics
   yolo export model=yolov8s.pt format=onnx  # Export official model

2. To run the inference, execute the following command:

   yolo --model <MODEL_PATH> [-i <IMAGE_PATH> | -v <VIDEO_PATH>] [--imgsz IMAGE_SIZE] [--gpu] [--iou-thresh IOU_THRESHOLD] [--score-thresh CONFIDENCE_THRESHOLD]
   
   # Example:
   yolo --model yolov8x.onnx -i images/zidane.jpg

License

This project is licensed under the MIT License. See the LICENSE file for details.

References

• YOLOv8 by ultralytics: https://github.com/ultralytics/ultralytics
• YOLO-NAS-OpenVino-cpp by Y-T-G: [https://github.com/Y-T-G/YOLO-NAS-OpenVino-cpp]
• OpenVINO: https://github.com/openvinotoolkit/openvino
• OpenCV: https://github.com/opencv/opencv