Poboljšanje procjene duboke neuronske mreže kroz variranje slikovnog ulaza

undergraduate thesis

• Title (english): Improving of Deep Neural Network Estimation Through Variation of Input Images
• Author: Mislav Matić
• Mentor: Marko Subašić (mentor)
• Committee member: Marko Subašić (predsjednik povjerenstva)
• Committee member: Denis Milošević (član povjerenstva)
• Committee member: Suraja Poštić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-09-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Zadatak ovog rada je saznati na koji način bi se oblik 3D objekta izmijenio kako bi se poboljšala preciznost određivanja njegove orijentacije. Prikazivanje slika, manipuliranje 3D objektima i potreban okvir za povezivanje 2D slika i 3D objekata je ostvaren bibliotekom PyTorch3D. Opisan je diferencijabilni prikazivač, ekstraktor značajki EfficientNet, konvolucijska neuronska mreža i parametri i funkcije korišteni u funkciji gubitka neuronske mreže. Na kraju su prikazani rezultati testiranja modela i ovisnost izgleda 3D objekta o raznim kombinacijama parametara s naglaskom na funkciju gubitka.

Abstract (english)

The task of this paper is to find out how the shape of a 3D object would change in order to improve the accuracy of determining its orientation. The image generation, manipulation of 3D objects and the necessary framework for connecting 2D images and 3D objects is realized through the PyTorch3D library. A description of the differentiable renderer, EfficientNet feature extractor, convolutional neural network and the parameters and functions used in the neural network loss function is provided. In the end, the paper shows the results of the model and the dependence of the appearance of the 3D object on various combinations of parameters with emphasis on the loss function.

• Keywords: umjetne neuronske mreže; duboko učenje; diferencijabilni prikazivač; 3D objekti
• Keywords (english): artificial neural networks; deep learning; differentiable rendering; 3D objects
• Language: croatian
• URN:NBN: urn:nbn:hr:168:373934
• Study programme: Title: Computing; 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 10:47:51