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undergraduate thesis
Poluautomatsko označavanje podataka za duboko učenje
2020. urn:nbn:hr:190:611430
Barać, Dario
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Software Engineering

Cite this document

Barać, D. (2020). Poluautomatsko označavanje podataka za duboko učenje (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:611430

Barać, Dario. "Poluautomatsko označavanje podataka za duboko učenje." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:611430

Barać, Dario. "Poluautomatsko označavanje podataka za duboko učenje." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:611430

Barać, D. (2020). 'Poluautomatsko označavanje podataka za duboko učenje', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 02 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:611430

Barać D. Poluautomatsko označavanje podataka za duboko učenje [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2020 [cited 2024 December 02] Available at: https://urn.nsk.hr/urn:nbn:hr:190:611430

D. Barać, "Poluautomatsko označavanje podataka za duboko učenje", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:190:611430

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Metadata
TitlePoluautomatsko označavanje podataka za duboko učenje
Title (english)Semi-automatic dataset labeling for deep learning
AuthorDario Barać
MentorKristijan Lenac (mentor)
Committee memberKristijan Lenac (predsjednik povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Software Engineering)
Rijeka
Defense date and country2020-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu je predstavljeno polu-automatsko rješenje koje pomoću praćenja olakšava i ubrzava proces izrade skupa podataka za detekciju objekata. Napravljen je pregled teorije rada konvolucijkih mreža za detekciju i praćenje. Istražena su slična postojeća rješenja i opisana je implementacija. Rješenje je testirano izradom skupa podataka za detekciju utičnica i treniranjem modela na generiranom skupu podataka.
Abstract (english)
This thesis presents a semi-automatic tool that uses tracking to speed up and simplify the process of creating an object detection dataset. After an overview of the theory behind convolutional networks for object detection and tracking, existing similar tools are presented. Implementation details are also discused. The tool was tested by creating a dataset for wall outlet detection and by training a detection model using the created dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:611430
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-09-29 07:40:27