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@ -15,17 +15,20 @@
# Welcome to Ultralytics YOLOv8
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.
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).
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
@ -40,8 +43,8 @@ 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.
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
@ -55,9 +58,10 @@ For more information about the history and development of YOLO, you can refer to
## 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.
[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