Publication Date: 2023/05/20
Abstract: Deep learning has recently been applied to scene labelling, object tracking, pose estimation, text detection and recognition, visual saliency detection, and image categorization. Deep learning typically uses models like Auto Encoder, Sparse Coding, Restricted Boltzmann Machine, Deep Belief Networks, and Convolutional Neural Networks. Convolutional neural networks have exhibited good performance in picture categorization when compared to other types of models. A straightforward Convolutional neural network for image categorization was built in this paper. The image classification was finished by this straightforward Convolutional neural network. On the foundation of the Convolutional neural network, we also examined several learning rate setting techniques and different optimisation algorithms for determining the ideal parameters that have the greatest influence on image categorization.
Keywords: Convolutional neural network, Deep Learning, Transfer Learning, ImageNet, Image classification; learning rate, parametric solution.
DOI: https://doi.org/10.5281/zenodo.8020781
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY881.pdf
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