Ultralytics YOLOv8 is 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.
The YOLOv8 object detection model is designed to detect objects in images and video in real-time, making it suitable for a wide range of applications such as surveillance, self-driving cars, and robotics. This model is intended for use by developers and researchers who are experienced in computer vision and deep learning.
The YOLOv8 model has demonstrated state-of-the-art performance on object detection tasks, as measured by various benchmark datasets. For example, on the COCO dataset, YOLOv8 achieved a mean average precision (mAP) of 45.6 on the validation set, outperforming previous state-of-the-art models. The model is capable of detecting 80 different classes of objects, including people, animals, vehicles, and household items.
The YOLOv8 model has limitations that users should be aware of. The model may struggle to detect objects in cluttered scenes or when objects are partially occluded. Additionally, the model may have difficulty detecting small objects or objects with low contrast.
- Authors: Glenn Jocher and Ayush Chaurasia and Jing Qiu
- Title: Ultralytics YOLOv8
- Version: 8.0.0
- Year: 2023
- URL: https://github.com/ultralytics/ultralytics
- Orcid: 0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069
- License: AGPL-3.0
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