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DC Field | Value | Language |
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dc.date.accessioned | 2023-03-03T13:16:40Z | - |
dc.date.available | 2023-03-03T13:16:40Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Dalli, L. (2022). Convolutional neural network for ingredient detection (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/107010 | - |
dc.description | B.Sc. (Hons)(Melit.) | en_GB |
dc.description.abstract | Detecting ingredients from ready-made food dishes is an exceptional way of monitoring one’s daily food intake. It lays the foundation for solving other culinary, vision-related problems throughout the whole food supply chain. We attempt to design a convolutional neural network which conducts classification on a Chinese cuisine dataset, VIREO-251. Such a task is rather complex, given that ingredients lie within a multi-labelled setting, and even more so when considering the highly versatile shapes that certain ingredients possess. An 18-layer ResNet model is trained to classify a subset of 15 ingredients, using a threshold probability to determine their presence. The system achieves an F1-score of 15.1%, as well as top-1 and top-2 scores of 14.8% and 25.9% respectively. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Cooking, Chinese | en_GB |
dc.subject | Data sets | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | Convolutional neural network for ingredient detection | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Computer Science | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Dalli, Luke (2022) | - |
Appears in Collections: | Dissertations - FacICT - 2022 Dissertations - FacICTCS - 2022 |
Files in This Item:
File | Description | Size | Format | |
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21BCS005 - Dalli Luke.pdf Restricted Access | 13.8 MB | Adobe PDF | View/Open Request a copy |
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