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63 lines
2.5 KiB
63 lines
2.5 KiB
# YOLOv8-Segmentation-ONNXRuntime-Python Demo |
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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. |
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## Features |
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- **Framework Agnostic**: Runs segmentation inference purely on ONNX Runtime without importing PyTorch. |
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- **Efficient Inference**: Supports both FP32 and FP16 precision for ONNX models, catering to different computational needs. |
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- **Ease of Use**: Utilizes simple command-line arguments for model execution. |
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- **Broad Compatibility**: Leverages Numpy and OpenCV for image processing, ensuring broad compatibility with various environments. |
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## Installation |
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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. |
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```bash |
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pip install ultralytics |
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pip install onnxruntime-gpu # For GPU support |
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# pip install onnxruntime # Use this instead if you don't have an NVIDIA GPU |
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pip install numpy |
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pip install opencv-python |
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``` |
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## Getting Started |
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### 1. Export the YOLOv8 ONNX Model |
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Export the YOLOv8 segmentation model to ONNX format using the provided `ultralytics` package. |
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```bash |
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yolo export model=yolov8s-seg.pt imgsz=640 format=onnx opset=12 simplify |
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``` |
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### 2. Run Inference |
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Perform inference with the exported ONNX model on your images. |
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```bash |
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python main.py --model-path <MODEL_PATH> --source <IMAGE_PATH> |
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``` |
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### Example Output |
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After running the command, you should see segmentation results similar to this: |
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<img src="https://user-images.githubusercontent.com/51357717/279988626-eb74823f-1563-4d58-a8e4-0494025b7c9a.jpg" alt="Segmentation Demo" width="800"> |
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## Advanced Usage |
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For more advanced usage, including real-time video processing, please refer to the `main.py` script's command-line arguments. |
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## Contributing |
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We welcome contributions to improve this demo! Please submit issues and pull requests for bug reports, feature requests, or submitting a new algorithm enhancement. |
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## License |
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This project is licensed under the AGPL-3.0 License - see the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details. |
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## Acknowledgments |
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- The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user [jamjamjon](https://github.com/jamjamjon). |
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- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.
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