How to Train YOLOv8 Object Detection on a Custom Dataset

YOLOv8, developed by Ultralytics, is the latest in the YOLO (You Only Look Once) series of object detection models, offering enhancements in object detection, instance segmentation, and image classification. Built with PyTorch, it runs on both CPU and GPU, and supports various export formats such as TF.js and CoreML. YOLOv8 introduces a new API and command line interface (CLI) that simplifies training and deployment, allowing users to easily train models on custom datasets with minimal code. The process involves creating and labeling datasets, generating dataset versions, and using the CLI for tasks like training and inference. Roboflow supports this process by simplifying dataset management and offering deployment options, including hosted API endpoints and edge deployment solutions. The new architecture is designed to be more flexible and intuitive, making it easier for developers to integrate into complex applications and achieve superior performance compared to previous YOLO versions.

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