ultralytics/examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md

YOLOv8-Segmentation-ONNXRuntime-Python Demo

This repository provides a Python demo for performing segmentation with YOLOv8 using ONNX Runtime, highlighting the interoperability of YOLOv8 models without the need for the full PyTorch stack.

Features

• Framework Agnostic: Runs segmentation inference purely on ONNX Runtime without importing PyTorch.
• Efficient Inference: Supports both FP32 and FP16 precision for ONNX models, catering to different computational needs.
• Ease of Use: Utilizes simple command-line arguments for model execution.
• Broad Compatibility: Leverages Numpy and OpenCV for image processing, ensuring broad compatibility with various environments.

Installation

Install the required packages using pip. You will need ultralytics for exporting YOLOv8-seg ONNX model and using some utility functions, onnxruntime-gpu for GPU-accelerated inference, and opencv-python for image processing.

pip install ultralytics
pip install onnxruntime-gpu  # For GPU support
# pip install onnxruntime    # Use this instead if you don't have an NVIDIA GPU
pip install numpy
pip install opencv-python

Getting Started

1. Export the YOLOv8 ONNX Model

Export the YOLOv8 segmentation model to ONNX format using the provided ultralytics package.

yolo export model=yolov8s-seg.pt imgsz=640 format=onnx opset=12 simplify

2. Run Inference

Perform inference with the exported ONNX model on your images.

python main.py --model-path <MODEL_PATH> --source <IMAGE_PATH>

Example Output

After running the command, you should see segmentation results similar to this:

Advanced Usage

For more advanced usage, including real-time video processing, please refer to the main.py script's command-line arguments.

Contributing

We welcome contributions to improve this demo! Please submit issues and pull requests for bug reports, feature requests, or submitting a new algorithm enhancement.

License

This project is licensed under the AGPL-3.0 License - see the LICENSE file for details.

Acknowledgments

• The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user jamjamjon.
• Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.