ANALYSIS OF DIFFERENT POOLING FUNCTIONS ON A CONVOLUTION NEURAL NETWORK BASED MODEL

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dc.contributor.authorÇetiner, Halit
dc.contributor.authorMetlek, Sedat
dc.date.accessioned2026-02-08T15:03:20Z
dc.date.available2026-02-08T15:03:20Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe common denominator of deep learning models used in many different fields today is the pooling functions used in their internal architecture. These functions not only directly affect the performance of the study, but also directly affect the training time. For this reason, it is extremely important to measure the performance of different pooling functions and share their success values. In this study, the performances of commonly used soft pooling, max pooling, spatial pyramid pooling and average pooling functions were measured on a dataset used as benchmarking in the literature. For this purpose, a new CNN based architecture was developed. Accuracy, F1 score, precision, recall and categorical cross entropy metrics used in many studies in the literature were used to measure the performance of the developed architecture. As a result of the performance metrics obtained, 97.79, 92.50, 91.60 and 89.09 values from best to worst for accuracy were obtained from soft pooling, max pooling, spatial pyramid pooling and average pooling functions, respectively. In the light of these results, the pooling functions used in this study have provided a better conceptual and comparative understanding of the impact of a CNN-based model.
dc.identifier.doi10.46519/ij3dptdi.1484354
dc.identifier.endpage276
dc.identifier.issn2602-3350
dc.identifier.issn2602-3350
dc.identifier.issue2
dc.identifier.startpage266
dc.identifier.urihttps://doi.org/10.46519/ij3dptdi.1484354
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4046
dc.identifier.volume8
dc.language.isoen
dc.publisherKerim ÇETİNKAYA
dc.relation.ispartofInternational Journal of 3D Printing Technologies and Digital Industry
dc.relation.ispartofInternational Journal of 3D Printing Technologies and Digital Industry
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectSoftware Engineering (Other)
dc.subjectYazılım Mühendisliği (Diğer)
dc.titleANALYSIS OF DIFFERENT POOLING FUNCTIONS ON A CONVOLUTION NEURAL NETWORK BASED MODEL
dc.typeArticle

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