E-commerce image classification with transfer learning

Abstract

A major e-commerce platform like Shopee offers thousands of macro categories for listing various products. Product price comparison shopping is a challenge for the e-commerce platform. Image classification helps customers accurately search for products from a large number of images, and it also helps them find the best price deals. To develop an effective image classification model for this use case, we utilized Convolutional Neural Networks (CNN) with transfer learning in our study. We sourced our image dataset from Kaggle (Shopee -Price Match Guarantee) and selected 645 ecommerce images from the top 20 classes, each containing the highest number of images. We chose MobileNetV2, EfficientNetB3, and InceptionV3 to compare the efficiency of pre-trained models. Our experimental results revealed that EfficientNetB3 and MobileNetV2 outperformed InceptionV3 in the validation set. Moreover, the time required to train all layers of EfficientNetB3 was approximately 8 times less than that of MobileNetV2.