A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.

Original languageEnglish
Title of host publicationProceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100739
DOIs
StatePublished - Apr 2019
Event22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019 - Cluj-Napoca, Romania
Duration: 24 Apr 201926 Apr 2019

Publication series

NameProceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019

Conference

Conference22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019
Country/TerritoryRomania
CityCluj-Napoca
Period24/04/1926/04/19

Keywords

  • Deep Learning
  • Embedded System
  • Neural Network
  • Sketch Classification

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