Optimizing ResNet-50 for Multiclass Classification: A Multi-Stage Learning Approach

In this study, we present a multistage learning pipeline that utilizes the ResNet-50 architecture as a static feature extractor for multiclass image classification problems. This methodology integrates transfer learning, data augmentation, and adaptive learning techniques to enhance generalization a...

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Autors principals:	Mustafa Kemal Ambar, HuseyiN Oztoprak, Kamil Yurtkan
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2025-01-01
Col·lecció:	IEEE Access
Matèries:	CNNs data augmentation deep learning dataset imbalance multiclass classification ResNet-50
Accés en línia:	https://ieeexplore.ieee.org/document/11121145/
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