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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-353504 |
| Date | January 2018 |
| Creators | Andersson, Melanie, Maja, Arvola, Hedar, Sara |
| Publisher | Uppsala universitet, Institutionen för teknikvetenskaper, Uppsala universitet, Institutionen för teknikvetenskaper, Uppsala universitet, Institutionen för teknikvetenskaper |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TVE-F ; 18 007 |
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