# Welcome to Ultralytics YOLO

Welcome to the Ultralytics YOLO documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
capabilities.

The YOLOv8 model is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of
object detection and image segmentation tasks. It can be trained on large datasets and is capable of running on a
variety of hardware platforms, from CPUs to GPUs.

Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page
will help you get the most out of YOLOv8. Please feel free to browse the documentation and reach out to us with any
questions or feedback.

### A Brief History of YOLO

YOLO (You Only Look Once) is a popular object detection and image segmentation model developed by Joseph Redmon and Ali
Farhadi at the University of Washington. The first version of YOLO was released in 2015 and quickly gained popularity
due to its high speed and accuracy.

YOLOv2 was released in 2016 and improved upon the original model by incorporating batch normalization, anchor boxes, and
dimension clusters. YOLOv3 was released in 2018 and further improved the model's performance by using a more efficient
backbone network, adding a feature pyramid, and making use of focal loss.

In 2020, YOLOv4 was released which introduced a number of innovations such as the use of Mosaic data augmentation, a new
anchor-free detection head, and a new loss function.

In 2021, Ultralytics released YOLOv5, which further improved the model's performance and added new features such as
support for panoptic segmentation and object tracking.

YOLO has been widely used in a variety of applications, including autonomous vehicles, security and surveillance, and
medical imaging. It has also been used to win several competitions, such as the COCO Object Detection Challenge and the
DOTA Object Detection Challenge.

For more information about the history and development of YOLO, you can refer to the following references:

- Redmon, J., & Farhadi, A. (2015). You only look once: Unified, real-time object detection. In Proceedings of the IEEE
  conference on computer vision and pattern recognition (pp. 779-788).
- Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings