Distraction Dataset

This project provides a comprehensive, labeled dataset designed to support the development of machine learning models that detect distraction in various contexts. The data includes diverse samples annotated with distraction categories, enabling researchers to train robust classifiers. It is structured to facilitate easy integration with popular ML pipelines, offering clear documentation on format and labeling conventions. The primary audience includes data scientists and researchers working on human-computer interaction, driver safety, and behavioral analytics. By offering a standardized benchmark, the dataset helps compare algorithm performance and accelerate innovation in distraction detection.