Dekker1/ResearchMethods
Archived
1
0
Fork 0
Code

Minor report additions

Browse Source
This commit is contained in:
Silver-T 2018-05-25 18:23:56 +10:00
parent 640002813d
commit 6786f9173c
2 changed files with 35 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
mini_proj/report/references.bib
View File

@ -137,3 +137,12 @@ month={Nov},}
pages={2825--2830},
year={2011}
}
@misc{bilogur_2017,
title={Where's Waldo | Kaggle},
url={https://www.kaggle.com/residentmario/wheres-waldo},
journal={Countries of the World | Kaggle},
publisher={Aleksey Bilogur},
author={Bilogur, Aleksey},
year={2017},
month={Oct}
}

45
mini_proj/report/waldo.tex
View File

@ -279,11 +279,11 @@
\hline
\textbf{Method} & \textbf{Test Accuracy} & \textbf{Training Time (s)}\\
\hline
LeNet & 87.86\% & 65.67\\
LeNet & 89.81\% & 58.13\\
\hline
CNN & \textbf{95.63\%} & 119.31\\
CNN & \textbf{95.63\%} & 113.81\\
\hline
FCN & 94.66\% & 113.94\\
FCN & 94.66\% & 117.69\\
\hline
Support Vector Machine & 83.50\% & 5.90\\
\hline
@ -300,29 +300,36 @@
\label{tab:results}
\end{table}
We can see by the results that Deep Neural Networks outperform our benchmark
We can see by in these results that Deep Neural Networks outperform our benchmark
classification models, although the time required to train these networks is
significantly greater.
\section{Conclusion} \label{sec:conclusion}
\section{Conclusion} \label{sec:conclusion}
Image from the ``Where's Waldo?'' puzzle books are ideal images to test
image classification techniques. Their tendency for hidden objects and ``red
herrings'' make them challenging to classify, but because they are drawings
they remain tangible for the human eye.
Image from the ``Where's Waldo?'' puzzle books are ideal images to test
image classification techniques. Their tendency for hidden objects and ``red
herrings'' make them challenging to classify, but because they are drawings
they remain tangible for the human eye.
In our experiments we show that, given unspecialized methods, Neural
Networks perform best on this kind of image classification task. No matter
which architecture their accuracy is very high. One has to note though that
random forest performed surprisingly well, coming close to the performance
of the better Neural Networks. Especially when training time is taking into
account it is the clear winner.
In our experiments we show that, given unspecialized methods, Neural
Networks perform best on this kind of image classification task. No matter
which architecture their accuracy is very high. One has to note though that
random forest performed surprisingly well, coming close to the performance
of the better Neural Networks. Especially when training time is taking into
account it is the clear winner.
It would be interesting to investigate various of these methods further.
There might be quite a lot of ground that could be gained by using
specialized variants of these clustering algorithms.
It would be interesting to investigate various of these methods further.
There might be quite a lot of ground that could be gained by using
specialized variants of these clustering algorithms.
\clearpage % Ensures that the references are on a seperate page
Discussion of the results:
- Was this what we expected to see?
- What was surprising?
- If you take learning time into account, are NN still as good?
- We also did say we would have these other measures, so we should at least try to include them. Then the question is also what do they show.
\clearpage % Ensures that the references are on a separate page
\pagebreak
\bibliographystyle{alpha}
\bibliography{references}