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https://www.um.edu.mt/library/oar/handle/123456789/107010
Title: | Convolutional neural network for ingredient detection |
Authors: | Dalli, Luke (2022) |
Keywords: | Cooking, Chinese Data sets Neural networks (Computer science) |
Issue Date: | 2022 |
Citation: | Dalli, L. (2022). Convolutional neural network for ingredient detection (Bachelor's dissertation). |
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. |
Description: | B.Sc. (Hons)(Melit.) |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/107010 |
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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