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74 lines
5.2 KiB
<div align="center"> |
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<a href="https://github.com/ultralytics/ultralytics" target="_blank"> |
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<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> |
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<br> |
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a> |
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a> |
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<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a> |
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<br> |
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<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> |
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<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |
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<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> |
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<br> |
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</div> |
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Welcome to the Ultralytics YOLOv8 documentation landing |
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page! [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO (You Only Look |
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Once) object detection and image segmentation model developed by [Ultralytics](https://ultralytics.com). This page |
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serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and |
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understand its features and 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. For any bugs and feature requests please |
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visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For professional support |
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please [Contact Us](https://ultralytics.com/contact). |
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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](https://github.com/ultralytics/yolov5), which further improved the model's |
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performance and added new features such as 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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## Ultralytics YOLOv8 |
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[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO object detection and |
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image segmentation model developed by Ultralytics. YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds |
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upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and |
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flexibility. |
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One key feature of YOLOv8 is its extensibility. It is designed as a framework that supports all previous versions of |
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YOLO, making it easy to switch between different versions and compare their performance. This makes YOLOv8 an ideal |
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choice for users who want to take advantage of the latest YOLO technology while still being able to use their existing |
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YOLO models. |
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In addition to its extensibility, YOLOv8 includes a number of other innovations that make it an appealing choice for a |
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wide range of object detection and image segmentation tasks. These include a new backbone network, a new anchor-free |
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detection head, and a new loss function. YOLOv8 is also highly efficient and can be run on a variety of hardware |
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platforms, from CPUs to GPUs. |
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Overall, YOLOv8 is a powerful and flexible tool for object detection and image segmentation that offers the best of both |
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worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.
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