Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform

dc.contributor.authorNarduzzi, Simon
dc.contributor.authorTüretken, Engin
dc.contributor.authorThiran, Jean-Philippe
dc.contributor.authorDunbar, L. Andrea
dc.date.accessioned2022-10-28T12:16:28Z
dc.date.available2022-10-28T12:16:28Z
dc.date.issued2022-06
dc.description.abstractDesigning Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network topology to fit hardware constraints. In this paper, we adapt one of the most widely used architectures for mobile hardware platforms, MobileNetV2, and study the impact of changing its topology and applying post-training quantization. We discuss the impact of the adaptations and the deployment of the model on an embedded hardware platform for face detection.en_US
dc.identifier.citationIEEE Swiss Conference on Data Science (SDS) 2022, Lucerne, Switzerland. pp 1-6en_US
dc.identifier.doi10.1109/SDS54800.2022.00008
dc.identifier.isbn978-1-6654-6847-3
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1058
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9911981
dc.language.isoenen_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectDeep Learningen_US
dc.subjectFace Detectionen_US
dc.subjectKendryte K210en_US
dc.subjectMobileNeten_US
dc.subjectLow Poweren_US
dc.titleAdaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platformen_US
dc.typeProceedingsen_US
dc.type.csemdivisionsDiv-Men_US
dc.type.csemresearchareasData & AIen_US
dc.type.csemresearchareasIoT & Visionen_US
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