AnnoVate: Revolutionizing Data Annotation with Automated Labeling Technique

Abstract

This research introduces AnnoVate, an innovative web application designed to automate the labor-intensive task of object annotation for computer vision applications. Focused on image annotation, the study addresses the escalating demand for data refinement and labeling in the field of artificial intelligence (AI). Leveraging the power of YOLOv8 (You Only Look Once), a high-performance object detection algorithm, AnnoVate minimizes human intervention while achieving an impressive 85% overall accuracy in object detection. The methodology integrates active learning, allowing labelers to selectively prioritize uncertain data during the labeling process. An iterative training approach continuously refines the model, creating a self-improving loop that enhances accuracy over successive loops. The system's flexibility enables users to export labeled datasets for their preferred AI model architectures. AnnoVate not only overcomes the limitations of traditional labeling methods but also establishes a collaborative human-machine interaction paradigm, setting the stage for further advancements in computer vision.