Drenyovszki, Rajmund (2024) Solving a classification problem using Transfer learning on small image datasets. GRADUS, 11 (1). pp. 1-9. ISSN 2064-8014
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Abstract
In this study, we explore the efficiency of transfer learning in training convolutional neural networks (CNNs) for image classification tasks, focusing on a dataset with limited size. Utilizing the VGG16 model, we investigate the impact of varying the number of trainable layers on classification accuracy. Our specific classification challenge involves distinguishing between images of cars with open and closed hoods. The study demonstrates that employing transfer learning enables the pretrained VGG16 model to achieve over 97% accuracy in this binary classification task, even with a training set of just 1000 image samples. This research was conducted solely by the undersigned, who also represents the sole author of this paper.
Item Type: | Article |
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Uncontrolled Keywords: | Classification, Deep Learning, CNN, VGG16, Transfer Learning |
Subjects: | Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány Q Science / természettudomány > QA Mathematics / matematika > QA76.76 Software Design and Development / Szoftvertervezés és -fejlesztés T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában T Technology / alkalmazott, műszaki tudományok > TL Motor vehicles. Aeronautics. Astronautics / járműtechnika, repülés, űrhajózás |
SWORD Depositor: | MTMT SWORD |
Depositing User: | MTMT SWORD |
Date Deposited: | 22 May 2024 11:39 |
Last Modified: | 22 May 2024 11:39 |
URI: | https://real.mtak.hu/id/eprint/195426 |
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