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Andre Henriques 2024-04-24 11:38:42 +01:00
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report/report.tex
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\usepackage{tabularx}
\usepackage{pdfpages}
\usepackage{float}
\usepackage{longtable}
\usepackage{graphicx}
\usepackage{svg}
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\tableofcontents
\newpage
\section{Introduction}
% This section should contain an introduction to the problem aims and objectives (0.5 page)
Currently, there are many classification tasks that are being done manually. These tasks could be done more effectively if there was tooling that would allow the easy creation of classification models, without the knowledge of data analysis and machine learning models creation.
The aim of this project is to create a classification service that requires zero user knowledge about machine learning, image classification or data analysis.
The system should allow the user to create a reasonable accurate model that can satisfy the users' need.
The system should also allow the user to create expandable models; models where classes can be added after the model has been created. % hyperparameters, augmenting the data.
\section{Introduction} \label{sec:introduction}
% This section should contain an introduction to the problem aims and obectives (0.5 page)
This project is to design and create a new software as a service platform, where users with no experience in machine learning, data analysis could create machine learning models to process their data.
In this project the platform will be scoped to image classification, with the ability to be updated later with more model types.
As an easy-to-use platform needs to be able to handle: image uploads, processing, and verification; model creation, management, and expansion; and image classification.
This report will do a brief analysis of current image classification systems, followed by an overview of the design of the system, and implementation details. The report will finish with analysis of legal, ethical and societal issues, and evaluation of results, and objectives.
\subsection{Project Motivations}
Currently, there are many classification tasks that are being done manually.
Thousands of man-hours are used to classify images, this task can be automated.
There are a few easy-to-use image classification systems that require low to no knowledge of image classification.
This project aims to fill that role and provide an easy-to-use system that anyone without knowledge of image classification could use.
% These tasks could be done more effectively if there was tooling that would allow the easy creation of classification models, without the knowledge of data analysis and machine learning models creation.
% The aim of this project is to create a classification service that requires zero user knowledge about machine learning, image classification or data analysis.
% The system should allow the user to create a reasonable accurate model that can satisfy the users' need.
% The system should also allow the user to create expandable models; models where classes can be added after the model has been created. % hyperparameters, augmenting the data.
\subsection{Project Aim}
The project aims to create a platform where users can create different types of classification models without the users having any knowledge of image classification.
The project aims to create a platform an easy to use where users can create different types of classification models without the users having any knowledge of image classification.
\subsection{Project Objectives}
This project's primary objectives are to create:
This project's primary objectives are to design and implement:
\begin{itemize}
\item a platform where the users can create and manage their models.
\item a system to automatically create and train models.
\item a system to automatically expand and reduce models without fully retraining the models.
% \item a system to automatically expand and reduce models without fully retraining the models.
\item a system to automatically expand models without fully retraining the models.
\item an API that users can interact programmatically with the system.
\end{itemize}
This project extended objectives are to:
\begin{itemize}
\item Create a system to automatically to merge modules to increase efficiency.
% \item Create a system to automatically to merge modules to increase efficiency.
\item Create a system to distribute the load of training the model's among multiple services.
\end{itemize}
\subsection{Success Criteria}
This project can be considered successful when:
\begin{itemize}
\item A user can upload images, train a model on those images, and evaluate images using the web interface.
\item A user can perform the same tasks, via the API service.
\end{itemize}
\subsection{Project Structure}
The report on the project shows the development and designs stages of the project. With each section addressing a part of the design and development process.
\begin{longtable}{| r | r |}
\hyperref[sec:introduction]{Introduction} & The introduction section will do a brief introduction of the project and it's objectives \\
\hyperref[sec:lit-tech-review]{Literature and Technical Review} & The Literature and Technical Review section will introduce some current existing projects that are similar to this one, and introduce some technologies that can be used to implement this project \\
% foo & bar \\ \hline
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\caption{Project structure}
\label{tab:project-structure}
\end{longtable}
\pagebreak
\section{Literature and Technical Review}
\section{Literature and Technical Review} \label{sec:lit-tech-review}
This section reviews existing technologies in the market that do image classification. It also reviews current image classification technologies, which meet the requirements for the project. This review also analyses methods that are used to distribute the learning between various physical machines, and how to spread the load so minimum reloading of the models is required when running the model.
\subsection{Existing Classification Platforms}
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And as the model will be trained and run multiple times decreasing the computational cost might be a better overall target for sustainability then being able to offer higher accuracies.
Even though scaling using the EfficientNet compound method might not yield the best results using some EfficientNets what were optimized by the team to would be optimal, for example, EfficientNet-B1 is both small and efficient while still obtaining $79.1\%$ top-1 accuracy in ImageNet, and realistically the datasets that this system will process will be smaller and more scope specific than ImageNet.
% \subsection{Efficiency of transfer learning}
% \subsection{Creation Models}
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% There are also unsupervised learning methods that do not have a fixed number of classes. While this method would work as an expandable model method, it would not work for the purpose of this project. This project requires that the model has a specific set of labels which does not work with unsupervised learning which has unlabelled data. Some technics that are used for unsupervised learning might be useful in the process of creating expandable models.
\subsection{Conclusion}
The technical review of current systems reveal that there are current systems that exist that can perform image classification tasks, but they are not friendly in ways to easy expand currently existing models.
The current methods that exist for image classification seem to have reached a classification accuracy and efficiency that make a project like this feasible.
% TODO talk about web serving thechnlogies
\pagebreak
\section{Service Analysis and Requirements}
Understanding the project that is being built is critical in the software deployment process, this section will look into the required parts for the project to work.