Image similarity usage in order to find similar items in E-commerce dataset

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dc.contributor.advisorAvots, Egils, juhendaja
dc.contributor.advisorMakarov, Aleksandr, juhendaja
dc.contributor.authorDossymbekov, Adilet
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkond
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituut
dc.date.accessioned2024-06-17T11:47:38Z
dc.date.available2024-06-17T11:47:38Z
dc.date.issued2024
dc.description.abstractThis thesis looks into the e-commerce situation among small businesses and examines the possibility of the application of neural networks to business-to-consumer interactions. The goal of the study is to test the ability of NN to work with limited data and hardware. Pre-trained ResNet-50 model was used as a base, and fine-tuned with the 4 class dataset, consisting of 2906 images of clothes categorised into four classes. The experiments were conducted on a budget-friendly hardware setup. Both the dataset and hardware reflect the limited resources of the small and micro businesses The final model was evaluated, reaching an accuracy of 70-80%, These results suggest the possibility of NN usage in limited data and computational resource conditions and provide the base for further developments in implementing NN among small businesses.
dc.identifier.urihttps://hdl.handle.net/10062/99884
dc.language.isoen
dc.publisherTartu Ülikool
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Estoniaen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/
dc.subjectE-commerce
dc.subjectImage classification
dc.subjectNeural network
dc.subject.otherbakalaureusetöödet
dc.titleImage similarity usage in order to find similar items in E-commerce dataset
dc.title.alternativePildi sarnasuse kasutamine sarnaste esemete leidmiseks e-kaubanduse andmestikust
dc.typeThesis

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Science and Technology - Bachelor's theses. Kuni 2024