Classification of images using EfficientNet CNN model with convolutional block attention module (CBAM) and spatial group-wise enhance module (SGE)

Bo Pang

doi:10.1117/12.2636811

29 April 2022 Classification of images using EfficientNet CNN model with convolutional block attention module (CBAM) and spatial group-wise enhance module (SGE)

Bo Pang

Author Affiliations +

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 1224707 (2022) https://doi.org/10.1117/12.2636811
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Classification of images is highly useful in medical, agriculture, industry, and other fields. Improving the accuracy of classification with a small quality of parameters is a challenging problem. This paper performs a study that relied on the use of EfficientNet and convolutional block attention module (CBAM). Especially, Spatial Group-wise Enhance (SGE) module is used to adjust the importance of sub features and suppress possible noise. Stochastic gradient 1. descent (SGD) is chosen as the optimizer. After experiments, the EfficientNet model with CBAM module and SGE module can achieve higher accuracy in image classification. This module has achieved high accuracy on Flowers (98.53%).

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Bo Pang "Classification of images using EfficientNet CNN model with convolutional block attention module (CBAM) and spatial group-wise enhance module (SGE)", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 1224707 (29 April 2022); https://doi.org/10.1117/12.2636811

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KEYWORDS

Data modeling

Image classification

Convolution

3D modeling

Performance modeling

Content addressable memory

Convolutional neural networks

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