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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Convolution Neural Network Versus Transfer Learning in Image Classification
verfasst von : O. Rama Devi, U. Surya Venkata Sekhar, S. Siva Rama Krishna, T. S. Rajarajeswari
Erschienen in: Proceedings of Third International Conference on Computing and Communication Networks
Verlag: Springer Nature Singapore
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Abstract
The objective of this research paper is comprehensive comparative analysis between two prominent approaches, namely Convolutional Neural Networks (CNN) and MobileNetV2-based transfer learning, for the task of image classification. Specifically, the focus is on determining the effectiveness of these approaches in accurately classifying images (in our case it is cat vs. dog) (Szyc in 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES). IEEE, 2018 [Szyc, K.: Comparison of different deep-learning methods for image classification. In: 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES). IEEE (2018)]). Through meticulous evaluation and comparison of results obtained from a benchmark dataset, this study aims to discern the strengths and limitations of each method. By shedding light on their respective merits, this research contributes to the advancement of image classification techniques and paves the way for further investigations in this domain.