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---
comments: true
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
---
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</div>
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 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
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</div>
## Where to Start
<div class="grid cards" markdown>
- :material-clock-fast:{ .lg .middle } &nbsp; **Getting Started**
***
Install `ultralytics` with pip and get up and running in minutes to train a YOLO model
***
[:octicons-arrow-right-24: Quickstart](quickstart.md)
- :material-image:{ .lg .middle } &nbsp; **Predict**
***
Predict on new images, videos and streams with YOLO <br /> &nbsp;
***
[:octicons-arrow-right-24: Learn more](modes/predict.md)
- :fontawesome-solid-brain:{ .lg .middle } &nbsp; **Train a Model**
***
Train a new YOLO model on your own custom dataset from scratch or load and train on a pretrained model
***
[:octicons-arrow-right-24: Learn more](modes/train.md)
- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore Tasks**
***
Discover YOLO tasks like detect, segment, classify, pose, OBB and track <br /> &nbsp;
***
[:octicons-arrow-right-24: Explore Tasks](tasks/index.md)
- :rocket:{ .lg .middle } &nbsp; **Explore YOLO11 NEW**
***
Discover Ultralytics' latest state-of-the-art YOLO11 models and their capabilities <br /> &nbsp;
***
[:octicons-arrow-right-24: YOLO11 Models 🚀 NEW](models/yolo11.md)
- :material-scale-balance:{ .lg .middle } &nbsp; **Open Source, AGPL-3.0**
***
Ultralytics offers two licensing options for YOLO: AGPL-3.0 License and Enterprise License. Ultralytics is available on [GitHub](https://github.com/ultralytics/ultralytics)
***
[:octicons-arrow-right-24: License](https://www.ultralytics.com/license)
</div>
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LNwODJXcvt4?si=7n1UvGRLSd9p5wKs"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<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
[YOLO](https://arxiv.org/abs/1506.02640) (You Only Look Once), a popular [object detection](https://www.ultralytics.com/glossary/object-detection) and [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) model, was developed by Joseph Redmon and Ali Farhadi at the University of Washington. Launched in 2015, YOLO quickly gained popularity for its high speed and accuracy.
- [YOLOv2](https://arxiv.org/abs/1612.08242), released in 2016, improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters.
- [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf), launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors and spatial pyramid pooling.
- [YOLOv4](https://arxiv.org/abs/2004.10934) was released in 2020, introducing innovations like Mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation), a new anchor-free detection head, and a new [loss function](https://www.ultralytics.com/glossary/loss-function).
- [YOLOv5](https://github.com/ultralytics/yolov5) further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats.
- [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
- [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
- [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.
## YOLO Licenses: How is Ultralytics YOLO licensed?
Ultralytics offers two licensing options to accommodate diverse use cases:
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license).
Our licensing strategy is designed to ensure that any improvements to our open-source projects are returned to the community. We hold the principles of open source close to our hearts ❤️, and our mission is to guarantee that our contributions can be utilized and expanded upon in ways that are beneficial to all.
## FAQ
### 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. 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?
Getting started with YOLO is quick and straightforward. You can install the Ultralytics package using [pip](https://pypi.org/project/ultralytics/) and get up and running in minutes. Here's a basic installation command:
!!! example "Installation using pip"
=== "CLI"
```bash
pip install ultralytics
```
For a comprehensive step-by-step guide, visit our [quickstart guide](quickstart.md). This resource will help you with installation instructions, initial setup, and running your first model.
### How can I train a custom YOLO model on my dataset?
Training a custom YOLO model on your dataset involves a few detailed steps:
1. Prepare your annotated dataset.
2. Configure the training parameters in a YAML file.
3. Use the `yolo TASK train` command to start training. (Each `TASK` has its own argument)
Here's example code for the Object Detection Task:
!!! example "Train Example for Object Detection Task"
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model (you can choose n, s, m, l, or x versions)
model = YOLO("yolo11n.pt")
# Start training on your custom dataset
model.train(data="path/to/dataset.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Train a YOLO model from the command line
yolo detect train data=path/to/dataset.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.
### What are the licensing options available for Ultralytics YOLO?
Ultralytics offers two licensing options for YOLO:
- **AGPL-3.0 License**: This open-source license is ideal for educational and non-commercial use, promoting open collaboration.
- **Enterprise License**: This is designed for commercial applications, allowing seamless integration of Ultralytics software into commercial products without the restrictions of the AGPL-3.0 license.
For more details, visit our [Licensing](https://www.ultralytics.com/license) page.
### How can Ultralytics YOLO be used for real-time object tracking?
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
!!! example "Example for Object Tracking on a Video"
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model
model = YOLO("yolo11n.pt")
# Start tracking objects in a video
# You can also use live video streams or webcam input
model.track(source="path/to/video.mp4")
```
=== "CLI"
```bash
# Perform object tracking on a video from the command line
# You can specify different sources like webcam (0) or RTSP streams
yolo track source=path/to/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.