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Welcome to the Ultralytics YOLOv8 documentation landing
page! [Ultralytics YOLOv8 ](https://github.com/ultralytics/ultralytics ) is the latest version of the YOLO (You Only Look
Once) object detection and image segmentation model developed by [Ultralytics ](https://ultralytics.com ). 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. For any bugs and feature requests please
visit [GitHub Issues ](https://github.com/ultralytics/ultralytics/issues ). For professional support
please [Contact Us ](https://ultralytics.com/contact ).
## 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 ](https://github.com/ultralytics/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
## Ultralytics YOLOv8
[Ultralytics YOLOv8 ](https://github.com/ultralytics/ultralytics ) is the latest version of the YOLO object detection and
image segmentation model developed by Ultralytics. YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds
upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and
flexibility.
One key feature of YOLOv8 is its extensibility. It is designed as a framework that supports all previous versions of
YOLO, making it easy to switch between different versions and compare their performance. This makes YOLOv8 an ideal
choice for users who want to take advantage of the latest YOLO technology while still being able to use their existing
YOLO models.
In addition to its extensibility, YOLOv8 includes a number of other innovations that make it an appealing choice for a
wide range of object detection and image segmentation tasks. These include a new backbone network, a new anchor-free
detection head, and a new loss function. YOLOv8 is also highly efficient and can be run on a variety of hardware
platforms, from CPUs to GPUs.
Overall, YOLOv8 is a powerful and flexible tool for object detection and image segmentation that offers the best of both
worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.
