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dc.contributor.advisorBolaños, Marc-
dc.contributor.advisorRadeva, Petia-
dc.contributor.authorPeracaula Prat, Joan-
dc.descriptionTreballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Marc Bolaños i Petia Radevaca
dc.description.abstract[en] Food recognition, object detection and classification applied to the food domain, is the main topic of this work. We have studied the problem of recognising food instances in tray images of self-service restaurants and have proposed a novel multimodal deep learning approach. From images and daily menus, the model presented uses two state of the art models in object detection and classification and a multimodal neural network to make significantly refined predictions compared to the baseline object detection model, achieving a class weighted average F1-score of 0.862. An ensemble model built from the proposed and the baseline models, also presented in this work, improves the results achieving a class weighted average F1-score of
dc.format.extent81 p.-
dc.rightsmemòria: cc-nc-nd (c) Joan Peracaula Prat, 2020-
dc.rightscodi: GPL (c) Joan Peracaula Prat, 2019-
dc.sourceTreballs Finals de Grau (TFG) - Enginyeria Informàtica-
dc.subject.classificationXarxes neuronals (Informàtica)ca
dc.subject.classificationAprenentatge automàticca
dc.subject.classificationTreballs de fi de grauca
dc.subject.classificationProcessament digital d'imatgesca
dc.subject.classificationVisió per ordinadorca
dc.subject.otherNeural networks (Computer science)en
dc.subject.otherMachine learningen
dc.subject.otherComputer softwareen
dc.subject.otherDigital image processingen
dc.subject.otherComputer visionen
dc.subject.otherBachelor's thesesen
dc.titleA multimodal deep learning approach for food tray recognitionca
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Matemàtiques
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

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