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IKEA furniture finder

The aim of this project was to develop an app that provides users with furniture recommendations based on the category and color of the furniture they are interested in. Deep learning techniques were used to train a TensorFlow model to accurately classify images of furniture. The model was trained o...

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Bibliographic Details
Main Author: Tan, Meng Sheng
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.utar.edu.my/5564/1/fyp_IA_2023_TMS.pdf
http://eprints.utar.edu.my/5564/
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id my-utar-eprints.5564
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spelling my-utar-eprints.55642023-08-18T08:49:38Z IKEA furniture finder Tan, Meng Sheng T Technology (General) The aim of this project was to develop an app that provides users with furniture recommendations based on the category and color of the furniture they are interested in. Deep learning techniques were used to train a TensorFlow model to accurately classify images of furniture. The model was trained on a large dataset of furniture images that were collected and labeled using a PowerShell script for automatic dataset labeling. A Flask web application was built using this model to predict the category of furniture in images sent by the Android client app. Additionally, a REST API endpoint was implemented in Flask to retrieve random furniture images from Firebase, which were used to display recommendations to users. To ensure scalability and consistency across environments, the Flask app was deployed on a cloud platform using Docker. User testing was conducted to evaluate the accuracy and usability of the app, and feedback was solicited from users to identify areas for improvement. Overall, the results demonstrate that the Ikea Furniture Finder app is an effective tool for assisting users in finding furniture based on their preferences. 2023-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5564/1/fyp_IA_2023_TMS.pdf Tan, Meng Sheng (2023) IKEA furniture finder. Final Year Project, UTAR. http://eprints.utar.edu.my/5564/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
spellingShingle T Technology (General)
Tan, Meng Sheng
IKEA furniture finder
description The aim of this project was to develop an app that provides users with furniture recommendations based on the category and color of the furniture they are interested in. Deep learning techniques were used to train a TensorFlow model to accurately classify images of furniture. The model was trained on a large dataset of furniture images that were collected and labeled using a PowerShell script for automatic dataset labeling. A Flask web application was built using this model to predict the category of furniture in images sent by the Android client app. Additionally, a REST API endpoint was implemented in Flask to retrieve random furniture images from Firebase, which were used to display recommendations to users. To ensure scalability and consistency across environments, the Flask app was deployed on a cloud platform using Docker. User testing was conducted to evaluate the accuracy and usability of the app, and feedback was solicited from users to identify areas for improvement. Overall, the results demonstrate that the Ikea Furniture Finder app is an effective tool for assisting users in finding furniture based on their preferences.
format Final Year Project / Dissertation / Thesis
author Tan, Meng Sheng
author_facet Tan, Meng Sheng
author_sort Tan, Meng Sheng
title IKEA furniture finder
title_short IKEA furniture finder
title_full IKEA furniture finder
title_fullStr IKEA furniture finder
title_full_unstemmed IKEA furniture finder
title_sort ikea furniture finder
publishDate 2023
url http://eprints.utar.edu.my/5564/1/fyp_IA_2023_TMS.pdf
http://eprints.utar.edu.my/5564/
_version_ 1775627977245065216
score 13.211869

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