Nanda Pratama
Attention is Everything You Need: Case on Face Mask Classification
Pratama, Nanda; Harianto, Dody; Filbert, Stefan; Warnars, Harco Leslie Hendric Spits; Muyeba, Maybin K.
Authors
Dody Harianto
Stefan Filbert
Harco Leslie Hendric Spits Warnars
Dr Maybin Muyeba K.M.Muyeba@salford.ac.uk
Teaching Fellow
Contributors
Alexander Agung Santoso Gunawan
Other
Abstract
Automated face mask classification has surfaced recently following the COVID-19 mask wearing regulations. The current State-of-The-Art of this problem uses CNN-based methods such as ResNet. However, attention-based models such as Transformers emerged as one of the alternatives to the status quo. We explored the Transformer-based model on the face mask classification task using three models: Vision Transformer (ViT), Swin Transformer, and MobileViT. Each model is evaluated with a top-1 accuracy score of 0.9996, 0.9983, and 0.9969, respectively. We concluded that the Transformer-based model has the potential to be explored further. We recommended that the research community and industry explore its integration implementation with CCTV
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 6, 2024 |
Online Publication Date | Nov 25, 2023 |
Publication Date | Nov 25, 2023 |
Deposit Date | Oct 4, 2024 |
Publicly Available Date | Oct 4, 2024 |
Journal | Procedia Computer Science |
Print ISSN | 1877-0509 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 227 |
Pages | 372-380 |
DOI | https://doi.org/10.1016/j.procs.2023.10.536 |
Keywords | Face Mask, Classification, Convolutional Neural Network, Attention, Transformer, Deep Learning |
Files
Published Version
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PDF
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
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