yolov8_test/docs/index.md

Introducing Ultralytics YOLOv8, the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.

Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned machine learning practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects

Where to Start

• Install ultralytics with pip and get up and running in minutes   :material-clock-fast: Get Started{ .md-button }
• Predict new images and videos with YOLOv8   :octicons-image-16: Predict on Images{ .md-button }
• Train a new YOLOv8 model on your own custom dataset   :fontawesome-solid-brain: Train a Model{ .md-button }
• Explore YOLOv8 tasks like segment, classify, pose and track   :material-magnify-expand: Explore Tasks{ .md-button }

YOLO: A Brief History

YOLO (You Only Look Once), a popular object detection and image segmentation model, was developed by Joseph Redmon and Ali Farhadi at the University of Washington. Launched in 2015, YOLO quickly gained popularity for its high speed and accuracy.

• YOLOv2, released in 2016, improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters.
• YOLOv3, launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors and spatial pyramid pooling.
• YOLOv4 was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function.
• YOLOv5 further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats.
• YOLOv6 was open-sourced by Meituan in 2022 and is in use in many of the company's autonomous delivery robots.
• YOLOv7 added additional tasks such as pose estimation on the COCO keypoints dataset.

Since its launch YOLO has been employed in various applications, including autonomous vehicles, security and surveillance, and medical imaging, and has won several competitions like the COCO Object Detection Challenge and the DOTA Object Detection Challenge.

Ultralytics YOLOv8

Ultralytics YOLOv8 is the latest version of the YOLO object detection and image segmentation model. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency.

YOLOv8 is designed with a strong focus on speed, size, and accuracy, making it a compelling choice for various vision AI tasks. It outperforms previous versions by incorporating innovations like a new backbone network, a new anchor-free split head, and new loss functions. These improvements enable YOLOv8 to deliver superior results, while maintaining a compact size and exceptional speed.

Additionally, YOLOv8 supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification. This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.