Classification of Female Apparel using Convolutional Neural Network

Abstract

With the vigorous development of clothing e-commerce, the amount of clothing image data on the internet has increased dramatically. A tedious effort was required to manually label and classify the semantic attributes of clothing images. Manual marking is time-consuming and laborious, so a method of automatic classification using convolutional neural networks was studied. In this paper, a female cloth dataset consisting of 10 types of female clothing was built. Convolutional Neural Network (CNN) was employed to learn the feature vectors for each type. Five different types of architectures, including ResNet50, Inception-v3, and VGG-19, AlexNet, and FashionNet were used for performance comparison. Experimental results have shown that Inception-v3 possesses the highest accuracy (98.07% for training and 96.91% for testing) in clothing classification compared with other methods.