Analysis of RepVGG on small sized dandelion images dataset in terms of transfer learning, regularization, spatial attention as well as Squeeze and Excitation Blocks

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dc.contributor.authorNergiz, Mehmet
dc.date.accessioned2024-04-24T17:56:28Z
dc.date.available2024-04-24T17:56:28Z
dc.date.issued2021
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826en_US
dc.description.abstractThe automated weed detection is an important research field in terms of agricultural productivity and economy. This study aims to apply RepVGG which is a new deep learning architecture developed on PyTorch framework and has promising results when trained and tested on ImageNet-1K dataset. 920 images of the small sized Dandelion Images dataset is used for this study. Pretrained vanilla, pretrained and dropout regularized, squeeze and excitation block added and spatial attention block added versions of RepVGG are tested on the dataset. VGG16 method is also applied to the dataset and the results of the MobileNetV2 method is taken from the Kaggle Competition to get an insight about the baseline results of the classical state of the art models. The proposed RepVGG modifications could not outperform the state of the art methods on this dataset but the effect of the modifications are deeply analyzed and the best configuration is obtained by Squeeze and Excitation block added RepVGG-A0 architecture which is trained from scratch for 5 epochs and provided results of 0,875, 0,665, 0,89 and 0,74 for Accuracy, Recall, Precision and F1 metrics respectively.en_US
dc.identifier.citationNergiz, M. (2021). Analysis of RepVGG on small sized dandelion images dataset in terms of transfer learning, regularization, spatial attention as well as Squeeze and Excitation Blocks. Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021, 378-382.
dc.identifier.doi10.1109/UBMK52708.2021.9558941
dc.identifier.endpage382en_US
dc.identifier.isbn9781665429085
dc.identifier.scopus2-s2.0-85125873630
dc.identifier.scopusqualityN/A
dc.identifier.startpage378en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558941
dc.identifier.urihttps://hdl.handle.net/11468/23526
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComponenten_US
dc.subjectFormattingen_US
dc.subjectInserten_US
dc.subjectStyleen_US
dc.subjectStylingen_US
dc.titleAnalysis of RepVGG on small sized dandelion images dataset in terms of transfer learning, regularization, spatial attention as well as Squeeze and Excitation Blocksen_US
dc.titleAnalysis of RepVGG on small sized dandelion images dataset in terms of transfer learning, regularization, spatial attention as well as Squeeze and Excitation Blocks
dc.typeConference Objecten_US

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