More work on the literature review

main.bib

@@ -147,3 +147,50 @@ author={
 	note = {[Online; accessed 20. Dec. 2023]},
 	url = {https://cloud.google.com/vision-ai/docs/product-recognizer}
 }
+ @article{mnist,
+  title={The mnist database of handwritten digit images for machine learning research},
+  author={Deng, Li},
+  journal={IEEE Signal Processing Magazine},
+  volume={29},
+  number={6},
+  pages={141--142},
+  year={2012},
+  publisher={IEEE}
+} 
+@article{mist-high-accuracy,
+  author       = {Sanghyeon An and
+                  Min Jun Lee and
+                  Sanglee Park and
+                  Heerin Yang and
+                  Jungmin So},
+  title        = {An Ensemble of Simple Convolutional Neural Network Models for {MNIST}
+                  Digit Recognition},
+  journal      = {CoRR},
+  volume       = {abs/2008.10400},
+  year         = {2020},
+  url          = {https://arxiv.org/abs/2008.10400},
+  eprinttype    = {arXiv},
+  eprint       = {2008.10400},
+  timestamp    = {Fri, 28 Aug 2020 12:11:44 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-2008-10400.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+@article {lecun-98,
+original =    "orig/lecun-98.ps.gz",
+author = 	"LeCun, Y. and Bottou, L. and Bengio, Y. and Haffner, P.",
+title = 	"Gradient-Based Learning Applied to Document Recognition",
+journal =	"Proceedings of the IEEE",
+month =         "November",
+volume =        "86",
+number =        "11",
+pages =         "2278-2324",
+year =		1998
+}
+@inproceedings{imagenet,
+  title={Imagenet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  booktitle={2009 IEEE conference on computer vision and pattern recognition},
+  pages={248--255},
+  year={2009},
+  organization={Ieee}
+} 

report/report.tex

@@ -61,7 +61,7 @@
 		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.
+    	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.
@@ -83,7 +83,7 @@
     \section{Literature and Techincal Review}
     % 1 page of background and literature review. Here you will need to references things. Gamal et al.~\cite{gamal} introduce the concept of \ldots
 
-		\subsection{Intruduction}
+		\subsection{Intruduction}hyperparameters, augmenting the data.
 			This section reviews current existing thechnologies in the market that do image classification. It also reviews current image classification technologies, and which meats the requirements fot the project. This review also analysis methods that are use to distrubute the learning between various machines, and how to spread the load so miminum reloading of the models is required when running the model.
 
 		\subsection{Current existing classification platforms}
@@ -103,6 +103,32 @@
 
 			This method is more effective at allowing users to add new types of products but as it does not give defined classes as the output the system does not give the target functionality that this project is hoping to achive.
 
+		\subsection{Requirements of the Image Classification Models}
+
+			The of the main ojectives of this project is to be able to create models that can give a class given an image for anydataset. Which means that there will be no ''one solution fits all to the problem``. While the most complex way to solve a problem would most likely result in success it might not be the most efficient way to achive the problem.
+
+			This section will analyse possible models that would obtain the best results. The models for this project have to be the most effiecient as possible while resulting in the best accuracry as possible.
+
+			A classical example is the MISNT Dataset \cite{mnist}. Models for the classfication of the mnist dataset can be both vary simple or extremely complex and achive diferent levels of complexity. 
+			For example in \cite{mist-high-accuracy} a acurracy $99.91\%$, by combining 3 Convolutional Neural Networks(CNNs), with different kernel sizes and by changing hyperparameters, augmenting the data, and in \cite{lecun-98} an accuracy of $95\%$ was accived using a 2 layer neurual network with 300 hiden nodes. Both these models achive the accuracy that is required for this project but \cite{mist-high-accuracy} is more way more expensice to run. There when deciding when to choose the what models the create the system should chose to create the model that can achive the required accuracy while taking the leas amount of effort to train.
+
+			% TODO fix the inglish in these sentance
+			The models for this system to work as indented shold be as small as possible while obtaining the required accuracy required to achive the task of classification the classes.
+
+		\subsection{Method of image classification models}
+
+			There all multitple ways of creating of achiving image classification, the requirements of the system are that the system should return the class that an image that belongs to. Which means that we are going to be using superfised classification methods as this ones are the ones that meet the requirements of the system.
+
+			% TODO find some papers to proff this
+
+			The system will use supervised models to classify images, using a combination of different types models, using neural networks, convulution neural networks, deed neural networks and deep convluution neural networks.
+
+			These types where chosen as they have had a large success in past in other image classification chalanges, for example in the imagenet chanlage \cite{imagenet}, which has ranked various different models in classifiying a large range of images.
+
+			% TODO talk about imagenet
+
+			When talking about general image classification we have to talk about imagenet. 
+
     	\subsection{Creation Models}
     		The models that I will be creating will be Convolutional Neural Network(CNN) \cite{lecun1989handwritten,fukushima1980neocognitron}.
     		The system will be creating two types of models that cannot be expanded and models that can be expanded. For the models that can be expanded, see the section about expandable models.