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# Welcome to Ultralytics YOLO
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Welcome to the Ultralytics YOLO documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
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Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
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point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
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capabilities.
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The YOLOv8 model is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of
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object detection and image segmentation tasks. It can be trained on large datasets and is capable of running on a
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variety of hardware platforms, from CPUs to GPUs.
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Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page
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will help you get the most out of YOLOv8. Please feel free to browse the documentation and reach out to us with any
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questions or feedback.
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### A Brief History of YOLO
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YOLO (You Only Look Once) is a popular object detection and image segmentation model developed by Joseph Redmon and Ali
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Farhadi at the University of Washington. The first version of YOLO was released in 2015 and quickly gained popularity
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due to its high speed and accuracy.
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YOLOv2 was released in 2016 and improved upon the original model by incorporating batch normalization, anchor boxes, and
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dimension clusters. YOLOv3 was released in 2018 and further improved the model's performance by using a more efficient
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backbone network, adding a feature pyramid, and making use of focal loss.
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In 2020, YOLOv4 was released which introduced a number of innovations such as the use of Mosaic data augmentation, a new
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anchor-free detection head, and a new loss function.
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In 2021, Ultralytics released YOLOv5, which further improved the model's performance and added new features such as
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support for panoptic segmentation and object tracking.
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YOLO has been widely used in a variety of applications, including autonomous vehicles, security and surveillance, and
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medical imaging. It has also been used to win several competitions, such as the COCO Object Detection Challenge and the
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DOTA Object Detection Challenge.
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For more information about the history and development of YOLO, you can refer to the following references:
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- Redmon, J., & Farhadi, A. (2015). You only look once: Unified, real-time object detection. In Proceedings of the IEEE
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conference on computer vision and pattern recognition (pp. 779-788).
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- Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
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