earticle

영어

Fashion occupies a large part of the industry and has been a part of our lives. One of the ways to analyze trends in fashion is to detect and classify categories in fashion images. In this paper, we present fashion category detection through the utilization of Detectron2's Mask R-CNN, which is easy to learn with custom datasets and has a high model construction and learning speed. Learning is also done based on Fashionpedia, a large-scale fashion segmentation and attribute localization dataset built with fashion ontology. As a result, the average precision (AP) of the bounding box was 52.45 and that of segmentation was 48.77, showing reasonably high performances. We propose a possibility of using fashion category detection and classification work in the field of fashion design.