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Welcome to the Ultralytics YOLOv8 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. For any bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.
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
Ultralytics YOLOv8
Ultralytics YOLOv8 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.