1415161718192017 of 92
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Metadata Validation Using a Convolutional Neural Network: Detection and Prediction of Fashion Products
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the e-commerce industry, importing data from third party clothing brands require validation of this data. If the validation step of this data is done manually, it is a tedious and time-consuming task. Part of this task can be replaced or assisted by using computer vision to automatically find clothing types, such as T-shirts and pants, within imported images. After a detection of clothing type is computed, it is possible to recommend the likelihood of clothing products correlating to data imported with a certain accuracy. This was done alongside a prototype interface that can be used to start training, finding clothing types in an image and to mask annotations of products. Annotations are areas describing different clothing types and are used to train an object detector model.

A model for finding clothing types is trained on Mask R-CNN object detector and achieves 0.49 mAP accuracy. A detection take just above one second on an Nvidia GTX 1070 8 GB graphics card.

Recommending one or several products based on a detection take 0.5 seconds and the algorithm used is k-nearest neighbors. If prediction is done on products of which is used to build the model of the prediction algorithm almost perfect accuracy is achieved while products in images for another products does not achieve nearly as good results.

Place, publisher, year, edition, pages
2019. , p. 86
Keywords [en]
Computer vision, Object detection, Fashion detection, Convolutional neural network, Metadata validation
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ltu:diva-75235OAI: oai:DiVA.org:ltu-75235DiVA, id: diva2:1335777
External cooperation
Spotin AB
Educational program
Computer Science and Engineering, master's level
Supervisors
Examiners
Available from: 2019-10-09 Created: 2019-07-07 Last updated: 2019-10-09Bibliographically approved

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1415161718192017 of 92
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
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  • Other locale
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Output format
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