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  1. 主页 - Ultralytics YOLO 文档

    Ultralytics YOLO 有哪些可用的许可选项? Ultralytics 为 YOLO 提供两种许可选项: AGPL-3.0 许可:此开源许可非常适合教育和非商业用途,可促进开放协作。 企业许可:此许可专为商业应用而设计, …

  2. GitHub - ultralytics/ultralytics: Ultralytics YOLO

    Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models …

  3. 目标检测:YOLOv11 (Ultralytics)环境配置 - CSDN博客

    Feb 26, 2026 · 文章浏览阅读880次,点赞24次,收藏19次。本文是一篇面向零基础新手的 YOLOv11 (Ultralytics) 环境配置保姆级指南,旨在帮助用户在 Windows 系统上快速、成功地搭建目标检测环境 …

  4. Ultralytics | Revolutionizing the World of Vision AI

    Ultralytics' mission is to empower people and companies to unleash the positive potential of AI. Bring your models to life with our vision AI tools.

  5. Home - Ultralytics YOLO Docs

    Jan 24, 2026 · Discover Ultralytics YOLO - the latest in real-time object detection and image segmentation. Learn about its features and maximize its potential in your projects.

  6. Home · ultralytics/ultralytics Wiki · GitHub

    Oct 9, 2025 · Welcome to the Ultralytics YOLO wiki! 🎯 Here, you'll find all the resources you need to get the most out of the YOLO object detection framework. From in-depth tutorials to seamless …

  7. Ultralytics - GitHub

    At Ultralytics, we are dedicated to creating the best artificial intelligence models in the world. Our open source works here on GitHub offer cutting-edge solutions for a wide range of AI tasks, including …

  8. Fine-tune Ultralytics YOLO26 Object Detection Model

    Feb 23, 2026 · Fine-tune the model Fine-tuning Ultralytics YOLO26 requires a single command to train for 20 epochs, starting from the pre-trained checkpoint. Check this code 👇

  9. Ultralytics | 彻底改变视觉人工智能世界

    由最先进的人工智能Ultralytics YOLO 提供支持 Ultralytics YOLO 是我们的智能工具,它就像哈佛大学的学生一样--高智商,永远渴望学习。 只需创建一个课程表,它就能成长!

  10. Releases · ultralytics/ultralytics - GitHub

    Ultralytics v8.4.17 makes NDJSON dataset conversions resplit-friendly —reusing existing images, cleaning stale labels, and avoiding unnecessary downloads for faster iteration 🚀📦