--- 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 ---

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

## Where to Start - **Install** `ultralytics` with pip and get up and running in minutes [:material-clock-fast: Get Started](quickstart.md){ .md-button } - **Predict** new images and videos with YOLO [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button } - **Train** a new YOLO model on your own custom dataset [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button } - **Tasks** YOLO tasks like segment, classify, pose and track [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button } - **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest SOTA models [:material-magnify-expand: Explore new YOLO11 models](models/yolo11.md){ .md-button }

Watch: How to Train a YOLO model on Your Custom Dataset in Google Colab.

## 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) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains. - [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. ## 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 === "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 train` command to start training. Here's example code: !!! example === "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 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 === "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.