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...

Full description

Saved in:
Bibliographic Details
Main Author: Liu, Hexin
Other Authors: Ponnuthurai N. Suganthan
Format: Theses and Dissertations
Language:English
Published: 2018
Subjects:
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
Description
Summary: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.