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Maskininlärningsalgoritmer för klassificering av barnkläder efter kön
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor of Fine Arts), 10 credits / 15 HE creditsStudent thesisAlternative title
Performance of Machine Learning Algorithms When Classifying Children’s Clothes According to (English)
Abstract [sv]

I denna rapport undersöker vi en maskins förmåga att klassificera barnkläder efter kön. Detta genomfördes genom att implementera tre maskininlärningsalgoritmer; kernel ridge regression, regularized extreme learning machine och support vector machine. En Gaussisk RBF kärna användes för både ridge regression och support vector machine. För extreme learning machine användes softplus som aktiveringsfunktion. Algoritmerna tränades och testades på ett dataset bestående av 1000 bilder hämtade från det svenska klädföretaget H&M. Kläderna var kategoriserade som för barn mellan åldrarna 18 månader och tio år. Vi fann att support vector machine uppnådde bäst resultat på datasetet med en klassificeringsprecision på 76.9%. De två andra metoderna uppnådde dock liknande precision; 76.6% för kernel ridge regression och 76.7% för regularized extreme learning machine.

Abstract [en]

In this paper, we investigate a machine’s ability to classify children’s clothes according to gender. This was done by implementing three different machine learning algorithms; kernel ridge regression, regularized extreme learning machine, and support vector machine. A Gaussian radial basis function kernel was used for both ridge regression and support vector machine, and for extreme learning machine the softplus function was used as activation. The algorithms were trained and tested on a data set consisting of one thousand images gathered from the Swedish clothing-retail company H&M. The clothes were categorized as being for children from the ages of eighteen months to ten years.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:247
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255827OAI: oai:DiVA.org:kth-255827DiVA, id: diva2:1342270
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Examiners
Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved

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CiteExportLink to record
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  • apa
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  • Other locale
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Output format
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