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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Sketch Classification with Neural Networks : A Comparative Study of CNN and RNN on the Quick, Draw! data set

Andersson, Melanie, Maja, Arvola, Hedar, Sara January 2018 (has links)
The aim of the study is to apply and compare the performance of two different types of neural networks on the Quick, Draw! dataset and from this determine whether interpreting the sketches as sequences gives a higher accuracy than interpreting them as pixels. The two types of networks constructed were a recurrent neural network (RNN) and a convolutional neural network (CNN). The networks were optimised and the final architectures included five layers. The final evaluation accuracy achieved was 94.2% and 92.3% respectively, leading to the conclusion that the sequential interpretation of the Quick, Draw! dataset is favourable.
Artificial intelligence
CNN
image classification
machine learning
neural networks
RNN
sketch classification
supervised learning
Quick
Draw!
Engineering and Technology
Teknik och teknologier

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