BongoFashion: A Versatile Dataset for Real-time Detection and Classification of Bangladeshi Wearables

In this paper, we present BongoFashion, the largest dataset of Bangladeshi clothing and fashion accessories, containing 11,498 images across 39 categories of commonly worn items. Our dataset captures the rich diversity of Bangladeshi wearables, including both traditional and modern attire, in real-world settings with varied backgrounds, resolutions, and poses. We trained and compared multiple object detection models, including six YOLO variants (YOLOv5 to YOLOv10) and the Detectron2 framework, in two experimental settings: one using the original dataset and another applying data augmentations to our training dataset. Our experiments revealed that augmentations improve model performance, with the YOLOv9 model achieving the highest mean average precision (mAP) of 91.7%. This dataset and our findings offer valuable resources for advancing wearable detection and classification, with promising applications in e-commerce, fashion search engines, and automated clothing categorization systems.