Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
RT-DETR Object Detection with ONNX Runtime
This project demonstrates how to run Ultralytics RT-DETR models using the ONNX Runtime inference engine in Python. It provides a straightforward example for performing object detection with RT-DETR models that have been exported to the ONNX format, a standard for representing machine learning models. RT-DETR, or Real-Time DEtection TRansformer, offers efficient and accurate object detection capabilities, detailed further in the RT-DETR research paper.
⚙️ Installation
To get started, you'll need to install the necessary dependencies. Follow the steps below.
Installing Required Dependencies
Install the core requirements using pip and the provided requirements.txt file. This installs the standard onnxruntime package (CPU-based inference). See the ONNX Runtime Execution Providers documentation for more information on available execution options.
pip install -r requirements.txt
Installing onnxruntime-gpu (Optional)
For accelerated inference using an NVIDIA GPU, install the onnxruntime-gpu package. Ensure you have the correct NVIDIA drivers and CUDA toolkit installed first. Consult the official ONNX Runtime GPU documentation for detailed compatibility information and setup instructions.
pip install onnxruntime-gpu
🚀 Usage
Once the dependencies are installed, you can run inference using the main.py script.
Execute the script from your terminal, specifying the path to your ONNX model, the input image, and optional confidence and IoU thresholds:
python main.py --model rtdetr-l.onnx --img image.jpg --conf-thres 0.5 --iou-thres 0.5
Arguments:
--model: Path to the RT-DETR ONNX model file (e.g.,rtdetr-l.onnx). You can easily export Ultralytics models to ONNX format. Find more models on the Ultralytics Models page.--img: Path to the input image file (e.g.,image.jpg).--conf-thres: Confidence threshold for filtering detections. Only detections with a score higher than this value will be kept. Learn more about thresholds in our guide on YOLO performance metrics.--iou-thres: Intersection over Union (IoU) threshold used for Non-Maximum Suppression (NMS) to remove redundant bounding boxes.
Adjust the --conf-thres and --iou-thres values based on your specific requirements for detection sensitivity and overlap removal.
🤝 Contributing
Contributions to enhance this example are welcome! Whether it's fixing bugs, adding new features, improving documentation, or suggesting optimizations, your input is valuable. Please refer to the Ultralytics Contribution Guide for detailed information on how to get started. You can also explore general guides on contributing to open source projects. Thank you for helping improve the Ultralytics ecosystem and its resources available on GitHub!