Degree Type

Creative Component

Semester of Graduation

Summer 2019

Department

Electrical and Computer Engineering

First Major Professor

Joseph Zambreno

Degree(s)

Master of Science (MS)

Major(s)

Electrical Engineering

Abstract

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.

Copyright Owner

Yue Zhang

File Format

PDF

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