Please use this identifier to cite or link to this item:
Title: Image classification with various deep learning architectures
Authors: Liu, Hexin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
Main article2.91 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 24, 2024

Download(s) 50

Updated on Jun 24, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.