4.3 KiB
YOLOv8-Segmentation-ONNXRuntime-Python Demo
This repository provides a Python demo for performing instance segmentation with Ultralytics YOLOv8 using ONNX Runtime. It highlights the interoperability of YOLOv8 models, allowing inference without requiring the full PyTorch stack. This approach is ideal for deployment scenarios where minimal dependencies are preferred. Learn more about the segmentation task on our documentation.
✨ Features
- Framework Agnostic: Runs segmentation inference purely on ONNX Runtime without importing PyTorch.
- Efficient Inference: Supports both FP32 and half-precision (FP16) for ONNX models, catering to different computational needs and optimizing inference latency.
- Ease of Use: Utilizes simple command-line arguments for straightforward model execution.
- Broad Compatibility: Leverages NumPy and OpenCV for image processing, ensuring wide compatibility across various environments.
🛠️ Installation
Install the required packages using pip. You will need ultralytics for exporting the 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 # For CPU-only support
pip install numpy opencv-python
🚀 Getting Started
1. Export the YOLOv8 ONNX Model
First, export your Ultralytics YOLOv8 segmentation model to the ONNX format using the ultralytics package. This step converts the PyTorch model into a standardized format suitable for ONNX Runtime. Check our Export documentation for more details on export options and our ONNX integration guide.
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 or video sources. Specify the path to your ONNX model and the image source using the command-line arguments.
python main.py --model yolov8s-seg.onnx --source path/to/image.jpg
Example Output
After running the command, the script will process the image, perform segmentation, and display the results with bounding boxes and masks overlaid.
💡 Advanced Usage
For more advanced usage scenarios, such as processing video streams or adjusting inference parameters, please refer to the command-line arguments available in the main.py script. You can explore options like confidence thresholds and input image sizes.
🤝 Contributing
We welcome contributions to improve this demo! If you encounter bugs, have feature requests, or want to submit enhancements (like a new algorithm or improved processing steps), please open an issue or pull request on the main Ultralytics repository. See our Contributing Guide for more details on how to get involved.
📄 License
This project is licensed under the AGPL-3.0 License. For detailed information, please see the LICENSE file or read the full AGPL-3.0 license text.
🙏 Acknowledgments
- This YOLOv8-Segmentation-ONNXRuntime-Python demo was contributed by GitHub user jamjamjon.
- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.
We hope you find this demo useful! Feel free to contribute and help make it even better.