Image classification with various deep learning architectures

The goal of the image classification is to correctly predict the subject of an image. For this project, because of the restrictions on resources and time, we worked by using a smaller dataset called Tiny ImageNet, then attempted to train an image classifier using this data. This project implemen...

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Bibliographic Details
Main Author:	Liu, Hexin
Other Authors:	Ponnuthurai N. Suganthan
Format:	Theses and Dissertations
Language:	English
Published:	2018
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	http://hdl.handle.net/10356/76031
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-76031
record_format	dspace
spelling	sg-ntu-dr.10356-760312023-07-04T15:56:25Z Image classification with various deep learning architectures Liu, Hexin Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The goal of the image classification is to correctly predict the subject of an image. For this project, because of the restrictions on resources and time, we worked by using a smaller dataset called Tiny ImageNet, then attempted to train an image classifier using this data. This project implemented some famous Convolutional Neural Networks with various useful techniques. The deep learning architectures we implemented in this project include AlexNet, GoogLeNet, ResNet and DenseNet and their several different versions, the techniques we applied in this project include dropout, data augmentation, weight decay and snapshot ensembles and cyclic learning rates. Consequently, we compared the performance of them in image classification to get the best one with the highest accuracy. Master of Science (Computer Control and Automation) 2018-09-24T02:33:50Z 2018-09-24T02:33:50Z 2018 Thesis http://hdl.handle.net/10356/76031 en 70 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Liu, Hexin Image classification with various deep learning architectures
description	The goal of the image classification is to correctly predict the subject of an image. For this project, because of the restrictions on resources and time, we worked by using a smaller dataset called Tiny ImageNet, then attempted to train an image classifier using this data. This project implemented some famous Convolutional Neural Networks with various useful techniques. The deep learning architectures we implemented in this project include AlexNet, GoogLeNet, ResNet and DenseNet and their several different versions, the techniques we applied in this project include dropout, data augmentation, weight decay and snapshot ensembles and cyclic learning rates. Consequently, we compared the performance of them in image classification to get the best one with the highest accuracy.
author2	Ponnuthurai N. Suganthan
author_facet	Ponnuthurai N. Suganthan Liu, Hexin
format	Theses and Dissertations
author	Liu, Hexin
author_sort	Liu, Hexin
title	Image classification with various deep learning architectures
title_short	Image classification with various deep learning architectures
title_full	Image classification with various deep learning architectures
title_fullStr	Image classification with various deep learning architectures
title_full_unstemmed	Image classification with various deep learning architectures
title_sort	image classification with various deep learning architectures
publishDate	2018
url	http://hdl.handle.net/10356/76031
_version_	1772827045294768128

Image classification with various deep learning architectures

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