A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors

Abstract

This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. There is a notable gap in systematic analyses of foundation models for specialized detection tasks, particularly within the domain of facial accessories. Six state-of-the-art models—Grounding DINO, Detic, OWLViT, OWLv2, YOLO World, and Florence-2—were evaluated across three datasets (FFHQ with custom annotations, CelebAMask-HQ, and Face Synthetics) to assess their effectiveness in zero-shot detection and labeling. Performance metrics, including Average Precision (AP), Average Recall (AR), and Intersection over Union (IoU), were used to benchmark foundation models. The results show that Detic achieved the highest performance scores (AP of 0.97 and AR of 0.98 on FFHQ, with IoU values reaching 0.97), making it highly suitable for automated annotation workflows. Grounding DINO and OWLv2 also showed potential, especially in high-recall scenarios. The results emphasize the importance of prompt engineering. Practical recommendations for using foundation models in specialized dataset annotation are provided.