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@ -78,12 +78,12 @@
\vspace*{\fill}
\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
start of the project until the end.
I am honestly thankful to him for sharing his honest and educational views on several 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
encouragement from the first day of the university. They have always been motivating, inspiring and
helping me to achieve my life goals.
\vspace*{\fill}
\end{center}
\newpage
@ -91,9 +91,9 @@
\begin{center}
\vspace*{\fill}
\section*{Abstract}
Currently there are few automatic image calssificication platforms.
Currently there are few automatic image classification 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.
The project goes through all the requirements for creating a platform service, and all of its needs.
\vspace*{\fill}
\end{center}
\newpage
@ -178,7 +178,7 @@
These models can be used in two ways in the system, they can be used to generate the models via transfer learning and by using the model structure as a basis to generate a complete new model.
\subsection{Current Known Models}
\subsection{Well-known models}
% TODO compare the models
This section will compare the different models that did well in the image net challenge.
@ -197,7 +197,7 @@
% EfficientNet
EfficientNet \cite{efficient-net} is a deep convolution neural network that was able to achieve $84.3\%$ top-1 accuracy while ``$8.4x$ smaller and $6.1x$ faster on inference than the best existing ConvNet''. EfficientNets\footnote{the family of models that use the thecniques that described in \cite{efficient-net}} are models that instead of the of just increasing the depth or the width of the model, we increase all the parameters at the same time by a constant value. By not scaling only depth EfficientNets can acquire more information about the images specially the image size is considered.
To test their results the EfficientNet team created a baseline model which as a building block used the mobile inverted bottleneck MBConv \cite{inverted-bottleneck-mobilenet}. The baseline model was then scaled using the compound method which resulted in better top-1 and top-5 accuracy.
To test their results, the EfficientNet team created a baseline model which as a building block used the mobile inverted bottleneck MBConv \cite{inverted-bottleneck-mobilenet}. The baseline model was then scaled using the compound method which resulted in better top-1 and top-5 accuracy.
While EfficientNets are smaller than their non-EfficientNet counterparts they are more computational intensive, a ResNet-50 scaled using the EfficientNet compound scaling method is $3\%$ more computational intensive than a ResNet-50 scaled using only depth while only improving the top-1 accuracy by $0.7\%$, 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.
@ -229,8 +229,13 @@
\item{Model Management}
\end{itemize}
\subsection{Resourses}
The system needs manage what server are available to it in various cases
\subsection{Overall structure}
The system needs to have some level of distributivity, this requirement exists because the expensive nature of machine learning training.
It would be unwise to perform machine learning training on the same machine that the main web server is running, as it would starve that server of resources.
\subsection{Resources}
The system has to manage what servers are available to do machine learning tasks.
The system needs to be aware and manage all GPU servers, servers that have GPUs available, and run the possible models.
\subsection{Web platform}
The web app is where users manage models, and data. The user will access the web app and configure the model, and manage that data set.
@ -248,13 +253,13 @@
For example, when training, send training jobs to the same server to prevent the server from having to reload the data again.
\subsection{Dataset Management}
Without data the system can not train models. And management of data is important as this data might contain some data that is private.
Such as biometrics the system will need to be able to safely handle this data.
Without data, the system cannot train models. And management of data is important as this data might contain some private data.
Such as biometrics, the system will need to be able to safely handle this data.
The system will also have to decide when to clear data, since storage space is also a resource that the system needs to manage.
\subsection{Model Management}
Once the model has been created the system needs to keep track of the model, as well as the actual acuracy of the model.
It needs to keep track of the how much it the model used so it can distribute the load from in different gpu servers.
Once the model has been created, the system has to keep track of the model, as well as the actual accuracy of the model.
It has to keep track of how much the model used so it can distribute the load from in different GPU servers.
\pagebreak
@ -377,5 +382,3 @@
% TODO add my job title
\end{document}