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undergraduate thesis
Primjena modela neuronske mreže na FSOCO skupu slika
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:663552
Sitar, Nikolina
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Sitar, N. (2022). Primjena modela neuronske mreže na FSOCO skupu slika (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:663552

Sitar, Nikolina. "Primjena modela neuronske mreže na FSOCO skupu slika." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:663552

Sitar, Nikolina. "Primjena modela neuronske mreže na FSOCO skupu slika." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:663552

Sitar, N. (2022). 'Primjena modela neuronske mreže na FSOCO skupu slika', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:663552

Sitar N. Primjena modela neuronske mreže na FSOCO skupu slika [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2022 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:235:663552

N. Sitar, "Primjena modela neuronske mreže na FSOCO skupu slika", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:663552

Metadata
TitlePrimjena modela neuronske mreže na FSOCO skupu slika
Title (english)Application of neural network models to the FSOCO image set
AuthorNikolina Sitar
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
U ovom radu dan je teorijski pregled strojnog učenja, konvolucijske neuronske mreže te pojmova bitnih za razumijevanje same evaluacije dobivenih rezultata. Nakon prikladnog uvođenja pojmova, rad se nastavlja s praktičnim dijelom zadatka koji kreće s odabirom prikladnog modela postojeće mreže. Nastavno na to, odabrani se model trenira na FSOCO skupu podataka. Navedeni skup podataka je jedini prikladan za ovaj zadatak zbog specifičnosti objekata koje je potrebno detektirati i klasificirati. Na kraju se provodi evaluacija dobivenih rezultata u kontekstu buduće primjene treniranog modela, s fokusom na vrijednosti srednje prosječne preciznosti (mAP).
Abstract (english)
In this paper, theoretical background is given regarding the machine learning, convolutional neural networks and main expressions crucial for understanding the evaluation of the results. After appropriatelly introducing key expressions, paper is continued with practical part which starts with choosing the right model of existing network. Afterwards, chosen model is being trained on FSOCO dataset. Mentioned dataset is the only appropriate for this problem due to specific object that need to be detected and classified. At the end, evaluation of the results is made keeping in mind future use of trained model, with focuse on mean average precision (mAP).
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:663552
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Embargo expiration date: 2024-09-22
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Created on2022-09-23 10:58:29