Open AccessTransfer learning-based Fashion Image Classification using Hybrid 2D-CNN and ImageNet Neural Network
Author(s) -
Sweety Duseja
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39054
Subject(s) - mnist database , transfer of learning , computer science , convolutional neural network , artificial intelligence , machine learning , pattern recognition (psychology) , contextual image classification , feature (linguistics) , feature extraction , deep learning , identification (biology) , artificial neural network , image (mathematics) , linguistics , philosophy , botany , biology
Many algorithms have been developed as a result of recent advances in machine learning to handle a variety of challenges. In recent years, the most popular transfer learning method has allowed researchers and engineers to run experiments with minimal computing and time resources. To tackle the challenges of classification, product identification, product suggestion, and picture-based search, this research proposed a transfer learning strategy for Fashion image classification based on hybrid 2D-CNN pretrained by VGG-16 and AlexNet. Pre-processing, feature extraction, and classification are the three parts of the proposed system's implementation. We used the Fashion MNIST dataset, which consists of 50,000 fashion photos that have been classified. Training and validation datasets have been separated. In comparison to other conventional methodologies, the suggested transfer learning approach has higher training and validation accuracy and reduced loss. Keywords: Machine Learning, Transfer Learning, Convolutional Neural Network, Image Classification, VGG16, AlexNet, 2D CNN.