107 lines
3.7 KiB
TeX
107 lines
3.7 KiB
TeX
%%% Preamble
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\documentclass[11pt, a4paper]{article}
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\usepackage[english]{babel} % English language/hyphenation
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\usepackage{url}
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\usepackage{tabularx}
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\usepackage{pdfpages}
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\usepackage{float}
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\usepackage{graphicx}
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\graphicspath{ {../images for report/} }
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\usepackage[margin=2cm]{geometry}
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\usepackage{hyperref}
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\hypersetup{
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colorlinks,
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citecolor=black,
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filecolor=black,
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linkcolor=black,
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urlcolor=black
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}
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\usepackage{cleveref}
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%%% Custom headers/footers (fancyhdr package)
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\usepackage{fancyhdr}
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\pagestyle{fancyplain}
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\fancyhead{} % No page header
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\fancyfoot[L]{} % Empty
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\fancyfoot[C]{\thepage} % Pagenumbering
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\fancyfoot[R]{} % Empty
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\renewcommand{\headrulewidth}{0pt} % Remove header underlines
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\renewcommand{\footrulewidth}{0pt} % Remove footer underlines
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\setlength{\headheight}{13.6pt}
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\usepackage[style=numeric,sorting=none,backend=biber]{biblatex}
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\addbibresource{../main.bib}
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% Write the approved title of your dissertation
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\title{Automated image classification with expandable models}
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% Write your full name, as in University records
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\author{Andre Henriques, 6644818}
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\date{}
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%%% Begin document
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\begin{document}
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\maketitle
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\newpage
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\tableofcontents
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\newpage
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% This section should contain an introduction to the problem aims and objectives (0.5 page)
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\section{Introduction}
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Image classification is a useful task in many areas. Currently, there exists a few systems that can do image classification, for example Google's Vision API or Amazon's Rekoginition, which tend to be for more general object recognition and labelling. My plan is to create a generalized image classification solution to automatically generate models with the input dataset with minimal user interaction.
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The system should also allow the user to create expandable models, where the number of classes is not known at the moment of that the model is created. Therefore, the system should allow the user to create models that can expand and reduce.
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This should be done in an efficient, and a change in the number of classes should not result in the entire model retraining.
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\subsection{Aims}
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This project aims to:
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\begin{itemize}
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\item Create a system to automatically create and train models
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\item Create a system to automatically expand and reduce models without fully retraining.
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\item Create a system to automatically to merge modules to increase efficiency
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\end{itemize}
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\subsection{Objectives}
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This project objectives are to:
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\begin{itemize}
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\item Create a system to automatically create and train models
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\item Create a system to automatically expand and reduce models without fully retraining.
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\item Create a system to automatically to merge modules to increase efficiency
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\end{itemize}
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\section{Literature Review}
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There currently exist systems that do image classification, like Google Vision AI, and Amazon's Rekoginition. Their tools, while providing similar services to what my project is supposed to do, it mostly focusses on general image classification rather than specific image classification, i.e. Car vs Boat, vs, Car model X vs Car model Y.
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1 page of background and literature review. Here you will need to references things. Gamal et al.~\cite{gamal} introduce the concept of \ldots
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\section{Technical overview}
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1 page of overview. My approach is shown in Figure~\ref{fig:sample}. You can draw the diagram in powerpoint and save the picture
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\section{Workplan}
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The following work plan is what I will be using for the project is shown in Figure~\ref{fig:sample2}.
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\appendix
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\newpage
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\section{References}
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\bibliographystyle{IEEEtran}
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\printbibliography[heading=none]
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% TODO add my job title
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\end{document}
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