Evaluation of CNN Models with Fashion MNIST Data

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2019-01-01
Authors
Zhang, Yue
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Joseph Zambreno
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Electrical and Computer Engineering

The Department of Electrical and Computer Engineering (ECpE) contains two focuses. The focus on Electrical Engineering teaches students in the fields of control systems, electromagnetics and non-destructive evaluation, microelectronics, electric power & energy systems, and the like. The Computer Engineering focus teaches in the fields of software systems, embedded systems, networking, information security, computer architecture, etc.

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The Department of Electrical Engineering was formed in 1909 from the division of the Department of Physics and Electrical Engineering. In 1985 its name changed to Department of Electrical Engineering and Computer Engineering. In 1995 it became the Department of Electrical and Computer Engineering.

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1909-present

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  • Department of Electrical Engineering (1909-1985)
  • Department of Electrical Engineering and Computer Engineering (1985-1995)

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The work seeks to evaluate the performance of four CNNs with respect to Fashion MNIST data set. Fashion MNIST is a dataset of images consisting of 70000 28*28 grayscale images, associated with a label from 10 classes. In this report, the accuracy of four popular CNN models that are LeNet-5, AlexNet, VGG-16 and ResNet for classifying MNIST-fashion data revealed that ResNet was the best suited for the selected dataset. The training process has been coded with Tensorflow. After the result accuracy improving, we could use the new model to the fashion company that can help the fashion company more accurately classify clothing. Moreover you could build your own closet online for your fashion.

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Tue Jan 01 00:00:00 UTC 2019