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Garment Fabric Pattern Classification via ResNet-34

Published: 17 January 2024 Publication History

Abstract

This article presents a novel automatic classification method for garment fabric pattern images using the vanilla Reset. The study begins by collecting industry-standard garment fabric images, which are further subjected to preprocessing techniques such as cropping, rotation, and contrast enhancement. These steps contribute to an expanded garment fabric image dataset. The dataset is then divided into a validation set and a training set for conducting image classification experiments. Different ResNet frameworks are employed to analyze the datasets and compare the results. The findings demonstrate that the classification model based on ResNet-34, serving as the backbone network, achieves the highest accuracy of 91.8% in garment fabric pattern classification. This performance surpasses the accuracy achieved by alternative backbone networks, namely AlexNet, VGG16, and GoogleNet, by a substantial margin. The superiority of ResNet-34 as a backbone network is thus affirmed. The proposed method's effectiveness is validated by the significant improvement in classification accuracy achieved by ResNet-34 compared to other backbone networks. These results highlight the potential of ResNet-34 in garment fabric pattern classification tasks. By leveraging the strengths of the ResNet architecture, our approach offers a promising solution for automating the classification of garment fabric patterns, contributing to efficiency and accuracy in the fashion industry. Overall, this study establishes the value of employing the ResNet-34 backbone network for garment fabric pattern image classification, as it outperforms competing networks and achieves remarkable classification accuracy. Future research can build upon these findings to explore further advancements in automatic garment fabric pattern classification.

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PCCNT '23: Proceedings of the 2023 International Conference on Power, Communication, Computing and Networking Technologies
September 2023
552 pages
ISBN:9781450399951
DOI:10.1145/3630138
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2024

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Author Tags

  1. Convolution neural network
  2. ResNet-34
  3. fabric pattern classification

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