Data enhancement analysis for deep-based image classification

Zikang Lin; Quanze Wang

doi:10.1117/12.2640903

13 October 2022 Data enhancement analysis for deep-based image classification

Zikang Lin, Quanze Wang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871B (2022) https://doi.org/10.1117/12.2640903
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Previous works have shown that the effectiveness and reliability of CNN used in computer vision is closely dependent on whether the dataset is adequate. In this paper, we illustrated how data augmentation, a technique which expands original datasets through transformations, could help improve CNN models’ performance and prevent overfitting through several experiments. After importing two extremely small datasets found on Kaggle, we carried out experiments by comparing the performance of models employing and not employing data augmentation techniques, while keeping track of their training accuracy, validation accuracy and their final test accuracy respectively. Eventually, noticeable improvement could be witnessed when employing appropriate data augmentation techniques on small datasets in our experiments, as such methods could generate a more comprehensive and sufficient dataset to help models learn the key features.

Citation Download Citation

Zikang Lin and Quanze Wang "Data enhancement analysis for deep-based image classification", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871B (13 October 2022); https://doi.org/10.1117/12.2640903

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KEYWORDS

Data modeling

Image analysis

Image enhancement

Image processing

Performance modeling

Convolution

RGB color model

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