More work on the literature review
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Andre Henriques 2023-12-23 14:35:46 +00:00
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@ -194,3 +194,36 @@ year = 1998
year={2009}, year={2009},
organization={Ieee} 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}
}

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@ -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. 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} \subsection{Creation Models}
The models that I will be creating will be Convolutional Neural Network(CNN) \cite{lecun1989handwritten,fukushima1980neocognitron}. The models that I will be creating will be Convolutional Neural Network(CNN) \cite{lecun1989handwritten,fukushima1980neocognitron}.