diff --git a/mini_proj/report/references.bib b/mini_proj/report/references.bib
index 1a35b55..ee6b990 100644
--- a/mini_proj/report/references.bib
+++ b/mini_proj/report/references.bib
@@ -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}
+}
\ No newline at end of file
diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex
index 37ac784..97bb394 100644
--- a/mini_proj/report/waldo.tex
+++ b/mini_proj/report/waldo.tex
@@ -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}