65 lines
4.3 KiB
Markdown
65 lines
4.3 KiB
Markdown
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# YOLOv8-Segmentation-ONNXRuntime-Python Demo
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This repository provides a [Python](https://www.python.org/) demo for performing instance segmentation with [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) using [ONNX Runtime](https://onnxruntime.ai/). It highlights the interoperability of YOLOv8 models, allowing inference without requiring the full [PyTorch](https://pytorch.org/) stack. This approach is ideal for deployment scenarios where minimal dependencies are preferred. Learn more about the [segmentation task](https://docs.ultralytics.com/tasks/segment/) on our documentation.
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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 [half-precision](https://www.ultralytics.com/glossary/half-precision) (FP16) for [ONNX](https://onnx.ai/) models, catering to different computational needs and optimizing [inference latency](https://www.ultralytics.com/glossary/inference-latency).
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- **Ease of Use**: Utilizes simple command-line arguments for straightforward model execution.
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- **Broad Compatibility**: Leverages [NumPy](https://numpy.org/) and [OpenCV](https://opencv.org/) for image processing, ensuring wide compatibility across various environments.
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## 🛠️ Installation
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Install the required packages using pip. You will need [`ultralytics`](https://github.com/ultralytics/ultralytics) for exporting the YOLOv8-seg ONNX model and using some utility functions, [`onnxruntime-gpu`](https://pypi.org/project/onnxruntime-gpu/) for GPU-accelerated inference, and [`opencv-python`](https://pypi.org/project/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 # For CPU-only support
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pip install numpy 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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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](https://docs.ultralytics.com/modes/export/) for more details on export options and our [ONNX integration guide](https://docs.ultralytics.com/integrations/onnx/).
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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 or video sources. Specify the path to your ONNX model and the image source using the command-line arguments.
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```bash
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python main.py --model yolov8s-seg.onnx --source path/to/image.jpg
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```
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### Example Output
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After running the command, the script will process the image, perform segmentation, and display the results with bounding boxes and masks overlaid.
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<img src="https://user-images.githubusercontent.com/51357717/279988626-eb74823f-1563-4d58-a8e4-0494025b7c9a.jpg" alt="YOLOv8 Segmentation ONNX Demo Output" width="800">
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## 💡 Advanced Usage
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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.
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## 🤝 Contributing
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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](https://github.com/ultralytics/ultralytics). See our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for more details on how to get involved.
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## 📄 License
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This project is licensed under the AGPL-3.0 License. For detailed information, please see the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file or read the full [AGPL-3.0 license text](https://opensource.org/license/agpl-v3).
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## 🙏 Acknowledgments
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- This YOLOv8-Segmentation-ONNXRuntime-Python demo was contributed by GitHub user [jamjamjon](https://github.com/jamjamjon).
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- Thanks to the [ONNX Runtime community](https://github.com/microsoft/onnxruntime) for providing a robust and efficient inference engine.
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We hope you find this demo useful! Feel free to contribute and help make it even better.
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