Help#
Welcome to the Ultralytics Help page. This page brings together practical guides, policies, and FAQs to support your work with Ultralytics YOLO models and repositories.
- Frequently Asked Questions (FAQ): Find answers to common questions and issues encountered by the community of Ultralytics YOLO users and contributors.
- Contributing Guide: Discover the protocols for making contributions, including how to submit pull requests, report bugs, and more.
- Continuous Integration (CI) Guide: Learn about the CI processes we use, complete with status reports for each Ultralytics repository.
- Contributor License Agreement (CLA): Review the CLA to understand the rights and responsibilities associated with contributing to Ultralytics projects.
- Minimum Reproducible Example (MRE) Guide: Learn the process for creating an MRE, which is crucial for the timely and effective resolution of bug reports.
- Code of Conduct: Our community guidelines support a respectful and open atmosphere for all collaborators.
- Environmental, Health, and Safety (EHS) Policy: Learn about our commitment to sustainability and the well-being of all our stakeholders.
- Security Policy: Familiarize yourself with our security protocols and the procedure for reporting vulnerabilities.
- Privacy Policy: Read our privacy policy to understand how we protect your data and respect your privacy in all our services and operations.
We encourage you to review these resources for a smooth and productive experience. If you need additional support, reach out via GitHub Issues or the Ultralytics Community.
FAQ#
Ultralytics YOLO (You Only Look Once) is a state-of-the-art, real-time object detection model. Its latest version, YOLO26, delivers faster, lighter inference with an optional NMS-free end-to-end head, optimized for edge and low-power devices, making it well suited to a wide range of applications, from real-time video analytics to advanced machine learning research. YOLO's efficiency in detecting objects in images and videos has made it a popular choice for businesses and researchers looking to integrate robust computer vision capabilities into their projects.
For more details on the current release, visit the YOLO26 documentation. For the upcoming family, explore YOLO27 (Coming Soon); its models are not yet available and no launch date is set.
Contributing to Ultralytics YOLO repositories is straightforward. Start by reviewing the Contributing Guide to understand the protocols for submitting pull requests, reporting bugs, and more. You'll also need to sign the Contributor License Agreement (CLA) to ensure your contributions are legally recognized. For effective bug reporting, refer to the Minimum Reproducible Example (MRE) Guide.
Ultralytics Platform is a no-code solution for managing your machine learning projects. It lets you annotate data, train, and deploy models like YOLO26 in one place, with cloud training, live training metrics, and dataset management. It covers the full workflow, from data preparation to model deployment, for both beginners and advanced users.
To get started, visit Ultralytics Platform Quickstart.
Continuous Integration (CI) in Ultralytics involves automated processes that ensure the integrity and quality of the codebase. Our CI setup includes Docker deployment, broken link checks, CodeQL analysis, and PyPI publishing. These processes help maintain stable and secure repositories by automatically running tests and checks on new code submissions.
Learn more in the Continuous Integration (CI) Guide.
Ultralytics takes data privacy seriously. Our Privacy Policy outlines how we collect and use anonymized data to improve the YOLO package while prioritizing user privacy and control. We adhere to strict data protection regulations to keep your information secure.