Classifying Garments from Fashion-MNIST Dataset Through CNNs

Abstract

Online fashion market is constantly growing, and an algorithm capable of identifying garments can help companies in the clothing sales sector to understand the profile of potential buyers and focus on sales targeting specific niches, as well as developing campaigns based on the taste of customers and improve user experience. Artificial Intelligence approaches able to understand and label humans' clothes are necessary, and can be used to improve sales, or better understanding users. Convolutional Neural Network models have been shown efficiency in image c1assification. This paper presents four different Convolutional Neural Networks models that used Fashion-MNIST dataset. Fashion-MNIST is a dataset made to help researchers finding models to classify this kind of product such as clothes, and the paper that describes it presents a comparison between the main classification methods to find the one that better label this kind of data. The main goal of this project is to provide future research with better comparisons between classification methods. This paper presents a Convolutional Neural Network approach for this problem and compare the classification results with the original ones. This method could enhance accuracy from 89.7% (the best result in the original paper, using SVM) to 99.1% (with a new cnn model called cnn-dropout-3).