A brief analysis on damaged building classification: optimizer and learning rate

Ruixin Qiao; Ruhan Wang; Yukang Zou

doi:10.1117/12.2640965

13 October 2022 A brief analysis on damaged building classification: optimizer and learning rate

Ruixin Qiao, Ruhan Wang, Yukang Zou

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871H (2022) https://doi.org/10.1117/12.2640965
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. Using a suitable optimizer and useful learning rate adjustment strategy is very important in this task. Recently some papers have discussed the effects of different learning rate adjustment strategies combined with different neural networks. However, there is little discussion about the effects of learning rate adjustment strategies combined with different optimizers. In this paper, we compare the effects of different combinations of different optimizers and learning rate adjustment strategies on image classification task. After analyzing the experimental results, we found that the combination of RMSProp and ExponentialLR obtained the best result. The combination increases the training speed and achieves high accuracy.

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Ruixin Qiao, Ruhan Wang, and Yukang Zou "A brief analysis on damaged building classification: optimizer and learning rate", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871H (13 October 2022); https://doi.org/10.1117/12.2640965

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KEYWORDS

Data modeling

Image classification

Signal attenuation

Convolution

Visual process modeling

Visualization

Process modeling

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