How to Detect Objects with YOLOv8

YOLOv8, developed by Ultralytics, is a cutting-edge computer vision model designed for object detection, classification, and segmentation tasks. The model, which builds upon its predecessor YOLOv5, can be deployed using the native Python SDK or through Roboflow Inference, a robust and scalable computer vision inference server. This guide explains how to utilize YOLOv8 for object detection, emphasizing the importance of having a trained model, either pre-trained on the Microsoft COCO dataset or customized for specific objects using tools like Roboflow. The guide provides detailed instructions for setting up and running models, from installation to visualization of results, using both Roboflow Inference and the Ultralytics Python SDK. It highlights the capabilities of Roboflow Inference to support extensive computer vision tasks and the utility of the supervision Python package for visualizing and manipulating model predictions, offering users a comprehensive approach to employing YOLOv8 in practical applications.