prikaz prve stranice dokumenta Automatska segmentacija kose
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master's thesis
Automatska segmentacija kose
2018. urn:nbn:hr:168:541168
Žuljević, Josip
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Žuljević, J. (2018). Automatska segmentacija kose (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:541168

Žuljević, Josip. "Automatska segmentacija kose." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:541168

Žuljević, Josip. "Automatska segmentacija kose." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:541168

Žuljević, J. (2018). 'Automatska segmentacija kose', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:541168

Žuljević J. Automatska segmentacija kose [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 April 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:541168

J. Žuljević, "Automatska segmentacija kose", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:541168

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Metadata
TitleAutomatska segmentacija kose
Title (english)Automatic Hair Segmentation
AuthorJosip Žuljević
MentorIgor Sunday Pandžić (mentor)
Committee memberIgor Sunday Pandžić (predsjednik povjerenstva)
Committee memberLea Skorin-Kapov (član povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada je izrada modela za klasifikaciju kose u stvarnom vremenu. Glavno sredstvo kreiranja dubokih neuronskih mreža bio je jezik Python te dodatak PyTorch. Navedena je teorijska pozadina modela DenseNet i Tiny DarkNet temeljenih na konvolucijskim neuronskim mrežama te pripadajući implementacijski detalji. Prikazani su podaci za trening kao i njihova priprema te utjecaj istih na robusnot podataka. Takod̄er napravljena je usporedba treniranih modela koji se razlikuju po hiperparametrima. Najbolji rezultati su pokazali kako je moguće ostvariti vrlo efikasno i brzo rješenje koje ispunjava uvjete pokretanja na mobilnim ured̄ajima.
Abstract (english)
The aim of this paper is to create a model for hair classification in real time. The main tool for creating deep neural networks was the Python language and the PyTorch plugin. The theoretical background of the DenseNet and Tiny DarkNet models based on the convolution neural network were described as well as the related implementation details. The data is presented for training as well as their preparation and their influence on the robust of data. Also a comparison of trained models differing in hyperparameters were presented. The best results have shown that it is possible to achieve a very efficient and fast solution that meets mobile start-up conditions.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:541168
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-14 02:28:24