Multiple classification of flower images using transfer learning

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dc.contributor.authorCengil, Emine
dc.contributor.authorCinar, Ahmet
dc.date.accessioned2026-08-12T16:08:33Z
dc.date.issued2019
dc.departmentFırat Üniversitesi
dc.description2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- Malatya -- 153040
dc.description.abstractDeep learning technologies have been successful in many fields in recent years. Image classification problem is one of the areas where the use of the results is successful. The study draws attention to the use of pretrained models in problem solving. With the approach called transfer learning, frequently used pretrained deep learning models such as Alexnet, Googlenet, VGG16, DenseNet and ResNet are used for image classification. The results show that the models used achieve acceptable performance rates while the highest performance is achieved with the VGG16 model. © 2019 IEEE.
dc.identifier.doi10.1109/IDAP.2019.8875953
dc.identifier.isbn978-172812932-7
dc.identifier.scopus2-s2.0-85074886566
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP.2019.8875953
dc.identifier.urihttps://hdl.handle.net/11508/41295
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260511
dc.subjectConvolutional Neural Networks; Deep Learning; Image Classification; Transfer Learning
dc.titleMultiple classification of flower images using transfer learning
dc.typeConference Object

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