Abstract
In this paper, performance evaluation of image data-set using 2-layer Convolutional Neural Network Architecture and transfer learning method, is studied on Fashion MNIST dataset. Fashion MNIST is a dataset of images consisting 70000 28 \(\times \) 28 gray-scale images, associated with label of 10 classes. The area of research of this paper in transfer learning is limited to pre-trained neural network VGG-16. The accuracy and respective losses are evaluated using the two mentioned methods. The work under this paper is inspired by widely famous Image-net Large Scale Visual Recognition Challenge, although due to constraint on time, resources, a smaller data set i.e. Fashion MNIST is taken for the study. The work is dependent on Keras Functional API and Tensor Flow. With the accuracy 88.24% and loss 29.02 as per CNN model, the model can be utilised by online clothing stores to classify their articles under right category.
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Rawat, J., Logofătu, D., Chiramel, S. (2020). Factors Affecting Accuracy of Convolutional Neural Network Using VGG-16. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_19
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DOI: https://doi.org/10.1007/978-3-030-48791-1_19
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