Analiza rada VGG arhitekture neuronske mreže

undergraduate thesis

• Title (english): VGG neural network architecture performance analysis
• Author: Klara Trbara
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2022-09-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing; Artificial Intelligence

Abstract

Kroz ovaj rad je prikazan razvoj konvolucijske neuronske mreže primjenjujući VGG arhitekturu u problemu rješavanja klasifikacije slika. Slike pasa i mačaka su zadane iz poznate baze podataka zadane u svrhu Kaggle-ovog natjecanja iz strojnog učenja iz 2013. godine. U uvodu rada se upoznajemo s osnovnom gradivnom jedinicom neuronske mreže perceptorom i osnovnom principima njezinog rada. U nastavku će se predstaviti opća arhitektura konvolucijskih neuronskih mreža. Naglasak ovog rada je na savladavanju principa razvoja osnovnog modela VGG arhitekture neuronske mreže s kojom se želi prikazati rast preciznosti klasifikacije mreže dodavanjem novih blokova. Prikazat će se razvoj osnovnog modela kroz korištenje cloud servisa i online sučelja Google Colab koristeći se otvorenim softverskim bibliotekama za strojno učenje (Tenserflow). Potom se na istoj bazi podataka testira već unaprijed istrenirana arhitektura neuronske mreže VGG-16 te se uspoređuju rezultati s osnovnim modelom.

Abstract (english)

This paper presents the development of a convolutional neural network applying the VGG architecture to the problem of image classification. The images of dogs and cats were taken from a well-known database provided for Kaggle's 2013 machine learning competition.
In first part of the paper, we are introduced to the basic building block of the neural network, the preceptor, and the basic principles of its operation. Furthermore, the general architecture of convolutional neural networks will be presented.
The emphasis of this paper is on mastering the principles of developing the basic model of the VGG architecture of the neural network, with which we want to show the growth of the accuracy of the network classification by stacking new blocks. The development of the basic model will be presented through the use of cloud services and the Google Colab online interface using open software libraries for machine learning (TensorFlow). Then, the pre-trained VGG-16 neural network architecture is tested on the same database and the results are compared with the basic model.

• Keywords: konvolucija; CNN; VGG; umjetne neuronske mreže; VGG-16; perceptor; Tensorflow
• Keywords (english): convolution; CNN; VGG; artificial neural networks; VGG-16; preceptor; Tensorflow
• Language: croatian
• URN:NBN: urn:nbn:hr:235:078686
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2022-09-26 11:13:02