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| true | Discover Ultralytics YOLO - the latest in real-time object detection and image segmentation. Learn about its features and maximize its potential in your projects. | Ultralytics, YOLO, YOLO26, YOLO11, object detection, image segmentation, deep learning, computer vision, AI, machine learning, documentation, tutorial |
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Introducing Ultralytics YOLO26, the latest version of the acclaimed real-time object detection and image segmentation model. YOLO26 is built on deep learning and computer vision advancements, featuring end-to-end NMS-free inference and optimized edge deployment. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. For stable production workloads, both YOLO26 and YOLO11 are recommended.
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 practitioner or new to the field, this hub aims to maximize YOLO's potential in your projects.
Where to Start
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:material-clock-fast:{ .lg .middle } Getting Started
Install
ultralyticswith pip and get up and running in minutes to train a YOLO model
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:material-image:{ .lg .middle } Predict
Predict on new images, videos and streams with YOLO
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:fontawesome-solid-brain:{ .lg .middle } Train a Model
Train a new YOLO model on your own custom dataset from scratch or load and train on a pretrained model
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:material-magnify-expand:{ .lg .middle } Explore Computer Vision Tasks
Discover YOLO tasks like detect, segment, classify, pose, OBB and track
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🚀{ .lg .middle } Explore YOLO26 🚀 NEW
Discover Ultralytics' latest YOLO26 models with NMS-free inference and edge optimization
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:material-select-all:{ .lg .middle } SAM 3: Segment Anything with Concepts 🚀 NEW
Meta's latest SAM 3 with Promptable Concept Segmentation - segment all instances using text or image exemplars
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:material-scale-balance:{ .lg .middle } Open Source, AGPL-3.0
Ultralytics offers two YOLO licenses: AGPL-3.0 and Enterprise. Explore YOLO on GitHub.
Watch: How to Train a YOLO26 model on Your Custom Dataset in Google Colab.
YOLO: A Brief History
YOLO (You Only Look Once), a popular object detection and image segmentation model, was developed by Joseph Redmon and Ali Farhadi at the University of Washington. Launched in 2015, YOLO gained popularity for its high speed and accuracy.
- YOLOv2, released in 2016, improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters.
- YOLOv3, launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors, and spatial pyramid pooling.
- YOLOv4 was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function.
- 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 was open-sourced by Meituan in 2022 and is used in many of the company's autonomous delivery robots.
- YOLOv7 added additional tasks such as pose estimation on the COCO keypoints dataset.
- YOLOv8 released in 2023 by Ultralytics, introduced new features and improvements for enhanced performance, flexibility, and efficiency, supporting a full range of vision AI tasks.
- YOLOv9 introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
- YOLOv10 created by researchers from Tsinghua University using the Ultralytics Python package, provides real-time object detection advancements by introducing an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements.
- YOLO11: Released in September 2024, YOLO11 delivers excellent performance across multiple tasks, including object detection, segmentation, pose estimation, tracking, and classification, enabling deployment across diverse AI applications and domains.
- YOLO26 🚀: Ultralytics' next-generation YOLO model optimized for edge deployment with end-to-end NMS-free inference.
YOLO Licenses: How is Ultralytics YOLO licensed?
Ultralytics offers two licensing options to accommodate diverse use cases:
- AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the 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.
Our licensing strategy is designed to ensure that any improvements to our open-source projects are returned to the community. We believe in open source, and our mission is to ensure that our contributions can be used and expanded in ways that benefit everyone.
The Evolution of Object Detection
Object detection has evolved significantly over the years, from traditional computer vision techniques to advanced deep learning models. The YOLO family of models has been at the forefront of this evolution, consistently pushing the boundaries of what's possible in real-time object detection.
YOLO's unique approach treats object detection as a single regression problem, predicting bounding boxes and class probabilities directly from full images in one evaluation. This revolutionary method has made YOLO models significantly faster than previous two-stage detectors while maintaining high accuracy.
With each new version, YOLO has introduced architectural improvements and innovative techniques that have enhanced performance across various metrics. YOLO26 continues this tradition by incorporating the latest advancements in computer vision research, featuring end-to-end NMS-free inference and optimized edge deployment for real-world applications.
FAQ
What is Ultralytics YOLO and how does it improve object detection?
Ultralytics YOLO is the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. The latest model, YOLO26, builds on previous versions by introducing end-to-end NMS-free inference and optimized edge deployment. YOLO supports various vision AI tasks such as detection, segmentation, pose estimation, tracking, and classification. Its efficient architecture ensures excellent 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 and get up and running in minutes. Here's a basic installation command:
!!! example "Installation using pip"
=== "CLI"
```bash
pip install -U ultralytics
```
For a comprehensive step-by-step guide, visit our Quickstart page. 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:
- Prepare your annotated dataset.
- Configure the training parameters in a YAML file.
- Use the
yolo TASK traincommand to start training. (EachTASKhas 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 pretrained YOLO model (you can choose n, s, m, l, or x versions)
model = YOLO("yolo26n.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 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 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 pretrained YOLO model
model = YOLO("yolo26n.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 Track Mode documentation, which explains the configuration and practical applications in real-time scenarios.