71 lines
3.7 KiB
Markdown
71 lines
3.7 KiB
Markdown
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# YOLOv8 - ONNX Runtime
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This repository provides an example implementation for running [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) models using the [ONNX Runtime](https://onnxruntime.ai/). This allows for efficient inference across various hardware platforms supporting the [ONNX format](https://onnx.ai/).
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## ⚙️ Installation
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To get started, you'll need [Python](https://www.python.org/) installed. Then, install the necessary dependencies.
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### Installing Required Dependencies
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Clone the repository and install the packages listed in the `requirements.txt` file using [pip](https://pip.pypa.io/en/stable/):
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```bash
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git clone https://github.com/ultralytics/ultralytics.git
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cd ultralytics/examples/YOLOv8-ONNXRuntime
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pip install -r requirements.txt
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```
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### Installing ONNX Runtime Backend
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You need to choose the appropriate ONNX Runtime package based on your hardware.
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**GPU Acceleration (NVIDIA)**
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If you have an NVIDIA GPU and want to leverage CUDA for faster inference, install the `onnxruntime-gpu` package. Ensure you have the correct [NVIDIA drivers](https://www.nvidia.com/Download/index.aspx) and CUDA toolkit installed. Refer to the official [ONNX Runtime GPU documentation](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html) for compatibility details.
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```bash
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pip install onnxruntime-gpu
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```
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**CPU Only**
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If you don't have a compatible NVIDIA GPU or prefer CPU-based inference, install the standard `onnxruntime` package. Check the [ONNX Runtime installation guide](https://onnxruntime.ai/docs/install/) for more options.
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```bash
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pip install onnxruntime
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```
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## 🚀 Usage
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Once the dependencies and the appropriate ONNX Runtime backend are installed, you can perform inference using the provided Python script.
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### Exporting Your Model
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Before running inference, you need a YOLOv8 model in the ONNX format (`.onnx`). You can export your trained Ultralytics YOLOv8 models using the Ultralytics CLI or Python SDK. See the [Ultralytics export documentation](https://docs.ultralytics.com/modes/export/) for detailed instructions.
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Example export command:
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```bash
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yolo export model=yolov8n.pt format=onnx # Export yolov8n model to ONNX
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```
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### Running Inference
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Execute the `main.py` script with the path to your ONNX model and input image. You can also adjust the confidence and [Intersection over Union (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) thresholds for [object detection](https://docs.ultralytics.com/tasks/detect/).
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```bash
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python main.py --model yolov8n.onnx --img image.jpg --conf-thres 0.5 --iou-thres 0.5
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```
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- `--model`: Path to the YOLOv8 ONNX model file (e.g., `yolov8n.onnx`).
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- `--img`: Path to the input image (e.g., `image.jpg`).
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- `--conf-thres`: Confidence threshold for filtering detections. Only detections with a score higher than this value will be kept. Learn more about thresholds in the [performance metrics guide](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
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- `--iou-thres`: IoU threshold for Non-Maximum Suppression (NMS). Boxes with IoU greater than this threshold will be suppressed. See the [NMS glossary entry](https://www.ultralytics.com/glossary/non-maximum-suppression-nms) for details.
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The script will process the image, perform object detection, draw bounding boxes on the detected objects, and save the output image as `output.jpg`.
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## Contributing
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Contributions to enhance this example or add new features are welcome! Please refer to the main [Ultralytics repository](https://github.com/ultralytics/ultralytics) for contribution guidelines. If you encounter issues or have suggestions, feel free to open an issue on the [ONNX Runtime GitHub](https://github.com/microsoft/onnxruntime) or the Ultralytics repository.
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