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Sketch Classification with Neural Networks: A Comparative Study of CNN and RNN on the Quick, Draw! data set
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. , p. 31
Series
TVE-F ; 18 007
Keywords [en]
Artificial intelligence, CNN, image classification, machine learning, neural networks, RNN, sketch classification, supervised learning, Quick, Draw!
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-353504OAI: oai:DiVA.org:uu-353504DiVA, id: diva2:1218490
External cooperation
Precisit AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-14 Last updated: 2018-06-19Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf