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Update YOLO11 Docs page (#16543)

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---
comments: true
description: Discover Ultralytics YOLOv8 - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
keywords: Ultralytics, YOLOv8, object detection, image segmentation, deep learning, computer vision, AI, machine learning, documentation, tutorial
description: Discover Ultralytics YOLO - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
keywords: Ultralytics, YOLO, YOLO11, object detection, image segmentation, deep learning, computer vision, AI, machine learning, documentation, tutorial
---
<div align="center">
@ -20,7 +20,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, deep learni
<br>
<br>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLO Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
<a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
@ -31,9 +31,9 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, deep learni
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
Introducing [Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 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/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.
Explore the YOLOv8 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 YOLOv8's potential in your projects
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
<div align="center">
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@ -55,9 +55,9 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
## Where to Start
- **Install** `ultralytics` with pip and get up and running in minutes &nbsp; [:material-clock-fast: Get Started](quickstart.md){ .md-button }
- **Predict** new images and videos with YOLOv8 &nbsp; [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLOv8 model on your own custom dataset &nbsp; [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLOv8 tasks like segment, classify, pose and track &nbsp; [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **Predict** new images and videos with YOLO &nbsp; [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLO model on your own custom dataset &nbsp; [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLO tasks like segment, classify, pose and track &nbsp; [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest SOTA models &nbsp; [:material-magnify-expand: Explore a Dataset](models/yolo11.md){ .md-button }
<p align="center">
@ -68,7 +68,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb" target="_blank">Google Colab</a>.
<strong>Watch:</strong> How to Train a YOLO model on Your Custom Dataset in <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb" target="_blank">Google Colab</a>.
</p>
## YOLO: A Brief History
@ -99,7 +99,7 @@ Our licensing strategy is designed to ensure that any improvements to our open-s
### What is Ultralytics YOLO and how does it improve object detection?
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLO supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
### How can I get started with YOLO installation and setup?
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Here's an example command:
```bash
yolo train model=yolov8n.pt data=coco128.yaml epochs=100 imgsz=640
yolo train model=yolo11n.pt data=coco128.yaml epochs=100 imgsz=640
```
For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@ -141,7 +141,7 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
```bash
yolo track model=yolov8n.pt source=video.mp4
yolo track model=yolo11n.pt source=video.mp4
```
For a detailed guide on setting up and running object tracking, check our [tracking mode](modes/track.md) documentation, which explains the configuration and practical applications in real-time scenarios.