From 94ac62fefa683c5493584d4f567898cce5bc58c7 Mon Sep 17 00:00:00 2001
From: Andre Henriques <andr3h3nriqu3s@gmail.com>
Date: Sat, 23 Dec 2023 14:35:46 +0000
Subject: [PATCH] More work on the literature review

---
 main.bib          | 35 ++++++++++++++++++++++++++++++++++-
 report/report.tex |  9 ++++++---
 2 files changed, 40 insertions(+), 4 deletions(-)

diff --git a/main.bib b/main.bib
index c13a43a..65d071c 100644
--- a/main.bib
+++ b/main.bib
@@ -193,4 +193,37 @@ year =		1998
   pages={248--255},
   year={2009},
   organization={Ieee}
-} 
+}
+@article{resnet-152,
+  author       = {Qilong Wang and
+                  Banggu Wu and
+                  Pengfei Zhu and
+                  Peihua Li and
+                  Wangmeng Zuo and
+                  Qinghua Hu},
+  title        = {ECA-Net: Efficient Channel Attention for Deep Convolutional Neural
+                  Networks},
+  journal      = {CoRR},
+  volume       = {abs/1910.03151},
+  year         = {2019},
+  url          = {http://arxiv.org/abs/1910.03151},
+  eprinttype    = {arXiv},
+  eprint       = {1910.03151},
+  timestamp    = {Mon, 04 Dec 2023 21:30:01 +0100},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1910-03151.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+@article{efficientnet,
+  author       = {Mingxing Tan and
+                  Quoc V. Le},
+  title        = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
+  journal      = {CoRR},
+  volume       = {abs/1905.11946},
+  year         = {2019},
+  url          = {http://arxiv.org/abs/1905.11946},
+  eprinttype    = {arXiv},
+  eprint       = {1905.11946},
+  timestamp    = {Mon, 03 Jun 2019 13:42:33 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-11946.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
diff --git a/report/report.tex b/report/report.tex
index 00573dc..04bb1fc 100644
--- a/report/report.tex
+++ b/report/report.tex
@@ -123,11 +123,14 @@
 
 			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.
+			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 14 million images. The contest has been running since 2010 to 2017.
 
-			% TODO talk about imagenet
+			The models that participated in the contest tended to use more and more Deep convlution neural networks, out of various model that where generated there are a few landmark models that were able to acchive high acurracies, including AlexNet \cite{krizhevsky2012imagenet}, VVG, ResNet-152\cite{resnet-152}, EfficientNet\cite{efficientnet}.
+			% TODO find vgg to cite
 
-			When talking about general image classification we have to talk about imagenet. 
+			These models can used in two ways in the system, they can be used to generate the models via transferlearning and by using the model structure as a basis to generate a complete new model.
+
+			% TODO compare the models
 
     	\subsection{Creation Models}
     		The models that I will be creating will be Convolutional Neural Network(CNN) \cite{lecun1989handwritten,fukushima1980neocognitron}.