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Signed-off-by: Alex <alexis.barou@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Alex 2025-01-30 09:37:37 +00:00 • committed by GitHub
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@ -32,7 +32,7 @@ keywords: Ultralytics, YOLO, YOLO11, object detection, image segmentation, deep
<a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
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Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/blog/everything-you-need-to-know-about-computer-vision-in-2025), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Explore the Ultralytics Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLO's potential in your projects
@ -87,7 +87,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
[:octicons-arrow-right-24: Learn more](modes/train.md)
- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore Tasks**
- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore computer vision tasks**
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@ -115,7 +115,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
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[:octicons-arrow-right-24: License](https://www.ultralytics.com/license)
[:octicons-arrow-right-24: YOLO License](https://www.ultralytics.com/license)
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@ -143,7 +143,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
- [YOLOv8](https://github.com/ultralytics/ultralytics) released in 2023 by Ultralytics. YOLOv8 introduced new features and improvements for enhanced performance, flexibility, and efficiency, supporting a full range of vision AI tasks,
- [YOLOv9](models/yolov9.md) introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
- [YOLOv10](models/yolov10.md) is created by researchers from [Tsinghua University](https://www.tsinghua.edu.cn/en/) using the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/). This version provides real-time [object detection](tasks/detect.md) advancements by introducing an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements.
- **[YOLO11](models/yolo11.md) 🚀 NEW**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md), leverage capabilities across diverse AI applications and domains.
- **[YOLO11](models/yolo11.md) 🚀 NEW**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks, including [object detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md), leverage capabilities across diverse AI applications and domains.
## YOLO Licenses: How is Ultralytics YOLO licensed?