Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture

Recently, deep learning has been used extensively in a wide range of domains. A class of deep neural networks that give the most rigorous effects in solving real-world problems is a Convolutional Neural Network (CNN). Fashion businesses have used CNN on their e-commerce to solve many problems such as clothes recognition, clothes search and recommendation. A core step for all of these implementations is image classification. However, clothes classification is a challenge task as clothes have many properties, and the depth of clothes categorization is highly complicated. This complicated depth makes different classes to have very similar features, and so the classification problem becomes very hard. In this paper, CNN based LeNet-5 architecture is proposed to train parameters of the CNN on Fashion MNIST dataset. Experimental results show that LeNet-5 model achieved accuracy over 98%. Therefore, it outperforms both the classical CNN model and the other existing state-of-the-art models in literatures.