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Kitchen Utensils Recognition using Fine Tuning and Transfer Learning

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Stephen KarungaruAuthors Info & Claims

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

Pages 19 - 22

https://doi.org/10.1145/3376067.3376104

Published: 25 February 2020 Publication History

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Abstract

To support blind persons at home especially in the kitchen, this work proposes the recognition of kitchen utensils using video sunglasses. The recognition system is based on transfer learning/fine tuning an existing deep learning algorithms, VGG16. Initially, our system can recognize 6 kitchen items using 1354 images in 6 classes. The training/validation and evaluation sets are set at 80% and 20% respectively. Most of the training data was downloaded from the Internet. In this challenging and noisy data, we achieved and accuracy of 95% using the fine tuning learning.

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Adrian Rosebrock, Deep Learning for computer vision with python, e-book, 2017.

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Cate Lawrence, Using Artificial Intelligence for Visual Impairments, https://dzone.com/articles/who-will-succeed-in-the-crowded-market-of-ai-for-v 2019.

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How to apply continual learning to your machine learning models, https://towardsdatascience.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f, 2019.

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D.Fullerton, "A visual Database of Recognizable Kitchen Utensils", Undergraduate Dissertation, School of Informatics, University of Edinburgh, 2016.

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Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.

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R. Granada, J. Monteiro, R. C. Barros, F. Meneguzzi, A Deep Neural Architecture for Kitchen Activity Recognition, Proc. 13tth of International Artificial Intelligence Research Society Conference

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Cumbajin ERodrigues NCosta PMiragaia RFrazão LCosta NFernández-Caballero ACarneiro JBuruberri LPereira A(2023)A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep LearningSensors10.3390/s2401023224:1(232)Online publication date: 31-Dec-2023
https://doi.org/10.3390/s24010232
Cumbajin ERodrigues NCosta PMiragaia RFrazão LCosta NFernández-Caballero ACarneiro JBuruberri LPereira A(2023)A Systematic Review on Deep Learning with CNNs Applied to Surface Defect DetectionJournal of Imaging10.3390/jimaging91001939:10(193)Online publication date: 25-Sep-2023
https://doi.org/10.3390/jimaging9100193
Rosello AValero-Mas JGallego ASáez-Pérez JCalvo-Zaragoza J(2023)Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptationPattern Analysis & Applications10.1007/s10044-023-01147-x26:4(1557-1569)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1007/s10044-023-01147-x

Index Terms

Kitchen Utensils Recognition using Fine Tuning and Transfer Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
        Object recognition

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ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

December 2019

270 pages

ISBN:9781450376822

DOI:10.1145/3376067

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shanghai Jiao Tong University: Shanghai Jiao Tong University
Xidian University
TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

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Published: 25 February 2020

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ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing

December 20 - 23, 2019

Shanghai, China

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Cumbajin ERodrigues NCosta PMiragaia RFrazão LCosta NFernández-Caballero ACarneiro JBuruberri LPereira A(2023)A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep LearningSensors10.3390/s2401023224:1(232)Online publication date: 31-Dec-2023
https://doi.org/10.3390/s24010232
Cumbajin ERodrigues NCosta PMiragaia RFrazão LCosta NFernández-Caballero ACarneiro JBuruberri LPereira A(2023)A Systematic Review on Deep Learning with CNNs Applied to Surface Defect DetectionJournal of Imaging10.3390/jimaging91001939:10(193)Online publication date: 25-Sep-2023
https://doi.org/10.3390/jimaging9100193
Rosello AValero-Mas JGallego ASáez-Pérez JCalvo-Zaragoza J(2023)Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptationPattern Analysis & Applications10.1007/s10044-023-01147-x26:4(1557-1569)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1007/s10044-023-01147-x

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Effectiveness of Transfer Learning and Fine Tuning in Automated Fruit Image Classification

A Deep Transfer Learning Approach to Support Opportunistic Wearable Activity Recognition

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Effectiveness of Transfer Learning and Fine Tuning in Automated Fruit Image Classification

A Deep Transfer Learning Approach to Support Opportunistic Wearable Activity Recognition

Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns

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