64 lines
2.5 KiB
Markdown
64 lines
2.5 KiB
Markdown
|
# 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.
|