| # 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-path <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. | |