| # 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. |
|
|
| ```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. |
|
|
| ```bash |
| 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. |
|
|
| ```bash |
| python main.py --model <MODEL_PATH> --source <IMAGE_PATH> |
| ``` |
|
|
| ### Example Output |
|
|
| After running the command, you should see segmentation results similar to this: |
|
|
| <img src="https://user-images.githubusercontent.com/51357717/279988626-eb74823f-1563-4d58-a8e4-0494025b7c9a.jpg" alt="Segmentation Demo" width="800"> |
|
|
| ## 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](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. |
|
|
