Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets

Minősített cikkek
2023-02-02 12:35:00

Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.

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Hivatkozás

MLA: Khanday, Owais Mujtaba, Samad Dadvandipour, and Mohd Aaqib Lone. "Effect of filter sizes on image classification in CNN: A case study on CFIR10 and fashion-MNIST datasets." IAES International Journal of Artificial Intelligence 10.4 (2021): 872.

APA: Khanday, O. M., Dadvandipour, S., & Lone, M. A. (2021). Effect of filter sizes on image classification in CNN: A case study on CFIR10 and fashion-MNIST datasets. IAES International Journal of Artificial Intelligence, 10(4), 872.

ISO690: KHANDAY, Owais Mujtaba; DADVANDIPOUR, Samad; LONE, Mohd Aaqib. Effect of filter sizes on image classification in CNN: A case study on CFIR10 and fashion-MNIST datasets. IAES International Journal of Artificial Intelligence, 2021, 10.4: 872.

BibTeX:

@article{khanday2021effect,
  title={Effect of filter sizes on image classification in CNN: A case study on CFIR10 and fashion-MNIST datasets},
  author={Khanday, Owais Mujtaba and Dadvandipour, Samad and Lone, Mohd Aaqib},
  journal={IAES International Journal of Artificial Intelligence},
  volume={10},
  number={4},
  pages={872},
  year={2021},
  publisher={IAES Institute of Advanced Engineering and Science}
}

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