An Image-based Transfer Learning Framework for Classification of E-Commerce Products

Surve, Vrushali Atul (2021) An Image-based Transfer Learning Framework for Classification of E-Commerce Products. Masters thesis, Dublin, National College of Ireland.

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Abstract

Classification of ecommerce products involves identifying the products and placing those products into the correct category, for example, men's Nike Air Max will be in the men's category shoes on an e-Commerce platform. Identifying the correct classifications of products is time-consuming for businesses. This research proposes an Image-based Transfer learning framework to classify the images into the correct category in the shortest time. The framework combines Image-based algorithms with Transfer Learning. This research compares the time to predict the category and accuracy of traditional CNN and transfer learning models such as VGG19, InceptionV3, ResNet50 and MobileNet. A visual classifier is trained on CNN and transfer learning models such as VGG19, InceptionV3, ResNet50 and MobileNet. Theis models are trained on an e-commerce product dataset that combines the ImageNet dataset with pre-trained weights. The dataset consists of 15000 images scraped from the web. Results of these five models shown in this paper are based on accuracy, loss, and each model takes time to identify the correct category. CNN was found to be 51% accurate, whereas vgg19 and ResNet50 were found to be 55% and 76 percent accurate, respectively. With 85 percent accuracy and timing 0.1 seconds, Inception V3 and MobileNet outperform other models. This research helps the e-commerce websites to classify their product data.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Deep Learning; CNN; VGG19; InceptionV3; ResNet50; MobileNet; ImageNet; Image classification; Transfer Learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	13 Mar 2023 16:03
Last Modified:	13 Mar 2023 16:03
URI:	https://norma.ncirl.ie/id/eprint/6322

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