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dc.contributor.advisorBernt, Bremdal
dc.contributor.authorHanssen, Magnus Fredheim
dc.date.accessioned2022-07-07T05:45:53Z
dc.date.available2022-07-07T05:45:53Z
dc.date.issued2022-05-15en
dc.description.abstractIn this thesis, we examine the viability of training a convolutional neural network using synthetic data. The cnn is used for image recognition in an rms. We create a program that can render 3D models from STL files as images with varied backgrounds. The process of creating and training an image recognition model is also automated. Lastly, the model is used for image recognition. The report compares different methods and hyperparameters used in training a model. Transfer learning is found to be suited for synthetic datasets. Using the pre-trained feature extraction layers of the VGG-16 model, we train an image recognition model with better than 90% accuracy in the laboratory. We then demonstrate the use of this model for object detection, and suggest avenues for further development.en_US
dc.identifier.urihttps://hdl.handle.net/10037/25768
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2022 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDDTE-3900
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.titleAutomatic Generation of Custom Image Recognition Modelsen_US
dc.typeMaster thesisen
dc.typeMastergradsoppgaveno


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)