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.
```bash
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.
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](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
## Acknowledgments
- The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user [jamjamjon](https://github.com/jamjamjon).
- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.
