Fashion sub-categories and attributes prediction model using deep learning

The fashion clothing and items classification is challenging to incorporate category/sub-category classification and attributes prediction for numerous fashion items into a compact multitask learning infrastructure. The main motive of this research is to improve the fashion items categorization and their attributes prediction from extracted visual features. We proposed a novel fashion sub-categories and attributes prediction (FS_CAP) model using deep learning techniques. In this proposed model, YOLO and DeepSORT architectures are used for person detection and tracking, Faster-RCNN architecture is used for sub-categories classification, and Custom-EfficientNet-B3 architecture is designed for attributes prediction. Twenty-four distinct modules are designed to increase the attributes classification accuracy for detected fashion items again each sub-category. The performance of the proposed model is evaluated on a customized fully annotated FashionItem dataset. The experimental results clearly show that the proposed model outperforms the recent baseline methods in fashion sub-categories and attributes prediction.