Deep Learning Based Sustainable Material Attribution for Apparels

Abstract

Material attribution is an integral part of product life cycle management. In the apparel fashion industry, material attribution activities are error prone because of their manual and monotonic nature. As a part of intelligent process automation for material attribution, we are proposing a model that uses deep neural networks to automate the classification of apparels based on attributes such as gender, category, subcategory, and color, when an image of an apparel is passed to the model. Our model assures process improvement by accurately extracting all the attributes in one go by using a computationally efficient algorithm that also minimizes the carbon footprint.