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chore: reformated and updated diagram
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Andre Henriques 2024-02-27 21:41:04 +00:00
parent 4721c3d305
commit 87fab390c7
2 changed files with 40 additions and 38 deletions
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22
diagrams/expandable_models_simple.d2
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@ -2,14 +2,6 @@ indata: "Input data" {
shape: cylinder shape: cylinder
} }
model-generation: Model Generation {
generator: Generator {
model-training: Model Training {
node: Node
}
}
}
node-manager: Node Manager { node-manager: Node Manager {
node1 node1
node2 node2
@ -20,8 +12,6 @@ node-manager: Node Manager {
node-manager->noden: Manage node-manager->noden: Manage
} }
model-generation.generator.model-training.node<->node-manager: Request/Gives node to train
model-database: Model database { model-database: Model database {
shape: cylinder shape: cylinder
} }
@ -40,9 +30,7 @@ model-runner: Model Runner {
headless->model-search: Results headless->model-search: Results
model-search: Model Search { model-search: Model Search {}
}
model-search<->_.model-database: Request Head Models model-search<->_.model-database: Request Head Models
@ -69,14 +57,14 @@ model-runner: Model Runner {
model-runner.node<->node-manager: Request/Gives node to run model model-runner.node<->node-manager: Request/Gives node to run model
User.shape: Person User.shape: Person
User->indata: Uploads data User->indata: Uploads data
User->model-generation: Requests Model
User->model-database: Manages Models User->model-database: Manages Models
User->model-runner: Request image for classification User->model-runner: Request image for classification
model-runner->User: Give class of image model-runner->User: Give class of image
model-generation.generator <-> indata: Requests Data model-generation: Model Generation {}
User->model-generation: Requests Model
model-generation <-> indata: Requests Data
model-generation<->node-manager: Request/Gives node to train

16
report/report.tex
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@ -55,6 +55,10 @@
\includegraphics[height=0.5\textheight]{uni_surrey} \includegraphics[height=0.5\textheight]{uni_surrey}
\end{center} \end{center}
\begin{center}
\today
\end{center}
\newpage \newpage
\section*{Declaration of Originality} \section*{Declaration of Originality}
@ -67,15 +71,24 @@
\newpage \newpage
\section*{Acknowledgements} \section*{Acknowledgements}
I would like to take this opportunity to thank my supervisor Rizwan Asghar that helped me from the
start of the project till the end.
I am honestly thankful to him for sharing his honest and educational views on a number of issues related
to this report.
Additionally, I would like to thank my parents and friends for their continued support and
encouragement from the first day of the university. They always have been motivating, inspiring and
helping me to achieve my life goals
\newpage \newpage
\section*{Abstract} \section*{Abstract}
Currently there are few automatic image calssificication platforms.
This project hopes to work as a guide for the creating a new image automatic classification platform.
The project goes through all the requirmenets for creating a platform service as well as all of its needs.
\newpage \newpage
\tableofcontents \tableofcontents
\newpage \newpage
\section{Introduction} \section{Introduction}
% This section should contain an introduction to the problem aims and objectives (0.5 page) % 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. 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.
@ -101,6 +114,7 @@
\end{itemize} \end{itemize}
\pagebreak \pagebreak
\section{Literature and Technical Review} \section{Literature and Technical 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. 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.