Paper Title (use style: paper title)

YOLOv8

YOLOv8 is an anchor-free object detection model that was introduced in January 2023 by Ultralytics [9]. Anchor-free models do not use pre-defined anchor boxes, which can improve accuracy and reduce the number of false positives.
YOLOv8 also uses mosaic augmentation during training. Mosaic augmentation is a technique that combines multiple images to create a new image. This can help the model to learn to detect objects in different contexts.
However, it was found that using mosaic augmentation throughout the entire training process can be counterproductive. Therefore, it is disabled for the final ten epochs. This allows the model to fine-tune its predictions on the original images without being distracted by the mosaic augmentation.
Overall, YOLOv8 is a powerful object detection model that offers significant improvements over previous YOLO models in terms of accuracy and speed. It is a good choice for a variety of applications, including real-time object detection and autonomous driving. YOLO is a family of object detection models that have been developed over the years. Each version of YOLO has its own strengths and limitations, making it suitable for different applications and environments.
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Architecture of YOLOv8. YOLOv8 is a new object detection model that has not yet been published in a paper. This means that we do not have direct access to the research methodology or ablation studies that were used to create the model. However, we can still learn about YOLOv8 by analyzing the repository and other information that is available. “Fig.7” is that GitHub user RangeKing created an image that provides a detailed visualization of the network's architecture.

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