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

Surve, Vrushali Atul, Pathak, Pramod, Hasanuzzaman, Mohammed, Haque, Rejwanul and Stynes, Paul (2022) An Image-based Transfer Learning Framework for Classification of E-Commerce Products. In: ICDLT '22: Proceedings of the 2022 6th International Conference on Deep Learning Technologies. Association for Computing Machinery, pp. 26-31. ISBN 978-1-4503-9693-6

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Abstract

Classification of e-commerce 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 classification of a product from hundreds of categories 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 CNN and transfer learning models such as VGG19, InceptionV3, ResNet50, and MobileNet. The 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 demonstrate that Inception V3 outperforms all other models based on a TIMING of 0.10 seconds and an accuracy of 85%.

Item Type:	Book Section
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 Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HF Commerce > Electronic Commerce
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	19 Oct 2022 16:16
Last Modified:	19 Oct 2022 16:16
URI:	https://norma.ncirl.ie/id/eprint/5816

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