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)
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Ultralytics YOLO Examples
Warning
The examples in this directory are community-contributed and showcase creative ways to use Ultralytics YOLO models. While we truly appreciate these contributions, they may not always reflect the latest best practices or receive regular updates. To help streamline our codebase and focus our resources on maintaining comprehensive, up-to-date official documentation and guides, we plan to retire these examples in Ultralytics v8.4.0.
Welcome to the Ultralytics examples directory! This collection showcases practical applications and detailed walkthroughs for integrating Ultralytics YOLO models into various real-world projects. Explore Python scripts and Jupyter notebooks designed to help you leverage the power of models like Ultralytics YOLO26 for tasks like object detection, instance segmentation, pose estimation, and more.
Whether you're deploying models on edge devices using formats like ONNX with ONNX Runtime, optimizing with TensorRT on NVIDIA Jetson, using OpenVINO for Intel hardware, or integrating with frameworks like OpenCV, these examples provide valuable insights and code snippets. Find inspiration for your next computer vision project and see how others are using Ultralytics YOLO to build innovative AI solutions on platforms ranging from C++ and C# to Python and Rust.
💡 Example Applications
Browse through the community-contributed examples below. These projects demonstrate various use cases and deployment strategies for Ultralytics YOLO models across different platforms and programming languages.
🤝 How to Contribute
We actively encourage contributions from our vibrant community! Sharing your examples, applications, and guides helps others learn and build amazing things with Ultralytics. If you have a project you'd like to share, please follow these steps:
- Fork the Repository: Start by forking the main Ultralytics repository on GitHub.
- Create Your Example: Add your project folder within the
examples/directory of your forked repository. - Prepare Your Submission: Ensure your project meets the following criteria:
- It utilizes the
ultralyticspip package. - Includes a
README.mdfile with clear, step-by-step instructions for setup and execution. Explain the purpose of the example and any prerequisites. - Avoid committing large files or extensive dependencies. If necessary, provide instructions for users to download them separately (e.g., using
ultralytics.utils.downloads.safe_download()). - As a contributor, be prepared to offer support and address issues related to your example.
- It utilizes the
- Submit a Pull Request: Create a pull request (PR) targeting the
mainbranch of the official Ultralytics repository. Use the title prefix[Example](e.g.,[Example] Add YOLOv8 Pose Estimation on Raspberry Pi).
For more comprehensive guidelines on contributing code, documentation, or examples, please refer to our Contributing Guide. We appreciate your efforts to enhance the Ultralytics ecosystem! If you have questions, feel free to open an issue or PR, and the team will be happy to assist. Check out the Ultralytics Blog for more insights and updates, and explore Ultralytics Platform for streamlined model training and deployment.