Glenn Jocher
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3 months ago | |
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README.md | 9 months ago | |
main.py | 3 months ago |
README.md
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.
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.
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.
python main.py --model <MODEL_PATH> --source <IMAGE_PATH>
Example Output
After running the command, you should see segmentation results similar to this:
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 file for details.
Acknowledgments
- The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user jamjamjon.
- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.