Transfer learning and fine-tuned transfer learning methods' effectiveness analyse in the CNN-based deep learning models

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dc.authoridOzturk, Celal/0000-0003-3798-8123
dc.authoridTasyurek, Murat/0000-0001-5623-8577
dc.authoridTurkdamar, Mehmet Ugur/0000-0002-7745-6319
dc.contributor.authorOzturk, Celal
dc.contributor.authorTasyurek, Murat
dc.contributor.authorTurkdamar, Mehmet Ugur
dc.date.accessioned2024-11-07T13:32:37Z
dc.date.available2024-11-07T13:32:37Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractObject detection is a type of application that includes computer vision and image processing technologies, which deal with detecting, tracking, and classifying desired objects in images. Computer vision is a field of artificial intelligence that enables computers and systems to derive information from digital images and take action or suggestions based on that information. CNN is one of the current methods of object detection due to its ease of use and GPU-supported parallel working features. Due to the aim of completing deep learning model training quickly or due to insufficient dataset, many studies using the transfer learning method are carried out in fields such as medicine, agriculture, and weapons. However, there are very few studies that use the fine-tuning method and compare transfer learning in terms of effectiveness. By paying attention to the balanced distribution of the data, approximately 100 images of each chess piece type were included in the analysis and a dataset of at least 1000 images was created. The without transfer learning fine-tune, fine-tuned transfer learning, transfer learning, fully supervised learning (FSL) and weakly supervised learning (WSL) applied models performances compared. Experimental results show that the fine-tuned transfer learning applied YOLO V4 model produces more accurate results than the other models in FSL and the transfer learning applied Faster R-CNN model produces more accurate results than the other models in WSL.
dc.identifier.doi10.1002/cpe.7542
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85142914413
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1002/cpe.7542
dc.identifier.urihttps://hdl.handle.net/11480/15524
dc.identifier.volume35
dc.identifier.wosWOS:000891847600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofConcurrency and Computation-Practice & Experience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectCNN
dc.subjectdeep learning
dc.subjectfine-tuned transfer learning
dc.titleTransfer learning and fine-tuned transfer learning methods' effectiveness analyse in the CNN-based deep learning models
dc.typeArticle

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