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YOLOv4 was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function.YOLOv3, launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors and spatial pyramid pooling.YOLOv2, released in 2016, improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters.Launched in 2015, YOLO quickly gained popularity for its high speed and accuracy. 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. Watch: How to Train a YOLOv8 model on Your Custom Dataset in Google Colab. Explore YOLOv8 tasks like segment, classify, pose and track Explore Tasks.Train a new YOLOv8 model on your own custom dataset Train a Model.

Predict new images and videos with YOLOv8 Predict on Images.

Install ultralytics with pip and get up and running in minutes Get Started.

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 Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.Įxplore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy.

Introducing Ultralytics YOLOv8, the latest version of the acclaimed real-time object detection and image segmentation model.

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