Cat classification based on improved ResNet50

Research Article
Open access

Cat classification based on improved ResNet50

Shipeng Sun 1*
  • 1 Guangdong University of Technology    
  • *corresponding author 3120007004@mail2.gdut.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230802
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

Cat species recognition holds significant potential in many fields. The primary objective of this research is to develop an automated algorithm for recognizing the presence of cats in images. The application prospects of this algorithm are diverse and include security, image search, and social media. Hence, this research has considerable practical value in various domains. In this study, we propose a cat image recognition algorithm based on the PyTorch, with ResNet50 as the foundational network architecture, and an attention mechanism (Efficient Channel Attention) integrated into the model for improved performance. We first introduced the Resnet network, and then introduced the combination of attention mechanism and Resnet in detail The proposed model achieved a 92.37% accuracy rate in classifying the 12 cat species, demonstrating its efficacy in accurately classifying and recognizing the collected images. The research conclusion of this paper has certain reference value.

Keywords:

cat classification, attention, Resnet, efficient channel attention

Sun,S. (2023). Cat classification based on improved ResNet50. Applied and Computational Engineering,15,11-16.
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References

[1]. Edgar Solomonik, Grey Ballard. A Communication-Avoiding Parallel Algorithm for the Symmetric Eigenvalue Problem. 2017 Sym. Para. Alg. Arch. 111–121.

[2]. An Zeng, Qi-Gang Gao, and Dan Pan. A global unsupervised data discretization algorithm based on collective correlation coefficient. 2011 Conf. Ind. Eng. Appl. Intel. Sys. 146–155.

[3]. Susan Dumais and Hao Chen. Hierarchical classification of Web content. 2000 Conf. Res. Deve. Infor. Ret. 256–263.

[4]. Glenn Fung and Olvi L. Mangasarian. Proximal support vector machine classifiers. 2001 Conf. Knowl. Disc. Data Min., 77–86.

[5]. Nestler E G, Osqui M M, Bernstein J G. Convolutional Neural Network, 2017, Wire. Net., 201 (74),137-151.

[6]. Weijie Liu, Weiwei Chen, and Xinmiao Dai. Capsule Embedded ResNet for Image Classification. 2021 Conf. Com. Sci. Arti. Intel., 143–149.

[7]. Kaiming He, Xiangyu, and Jian Sun. Deep residual learning for image recognition. 2016 Computer Vision and Pattern Recognition, 1-10.

[8]. Wu Z, Shen C, Hengel A. Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 2016 Comp. Vis. Pat. Rec.,1-12.

[9]. Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. 2018, Comp. Vis. Pat. Rec, 201-211.

[10]. Santosh Kumar Mishra, Gaurav Rai, Sriparna Saha, and Pushpak Bhattacharyya. 2021. Effic. Cha. Att. En., 21 3, Article 49, 17

[11]. Chen P. Efficient Channel Allocation Tree Generation for Data Broadcasting in a Mobile Computing Environment. 2003, Wire. Net., 200-213.

Cite this article

Sun,S. (2023). Cat classification based on improved ResNet50. Applied and Computational Engineering,15,11-16.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor：Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Edgar Solomonik, Grey Ballard. A Communication-Avoiding Parallel Algorithm for the Symmetric Eigenvalue Problem. 2017 Sym. Para. Alg. Arch. 111–121.

[2]. An Zeng, Qi-Gang Gao, and Dan Pan. A global unsupervised data discretization algorithm based on collective correlation coefficient. 2011 Conf. Ind. Eng. Appl. Intel. Sys. 146–155.

[3]. Susan Dumais and Hao Chen. Hierarchical classification of Web content. 2000 Conf. Res. Deve. Infor. Ret. 256–263.

[4]. Glenn Fung and Olvi L. Mangasarian. Proximal support vector machine classifiers. 2001 Conf. Knowl. Disc. Data Min., 77–86.

[5]. Nestler E G, Osqui M M, Bernstein J G. Convolutional Neural Network, 2017, Wire. Net., 201 (74),137-151.

[6]. Weijie Liu, Weiwei Chen, and Xinmiao Dai. Capsule Embedded ResNet for Image Classification. 2021 Conf. Com. Sci. Arti. Intel., 143–149.

[7]. Kaiming He, Xiangyu, and Jian Sun. Deep residual learning for image recognition. 2016 Computer Vision and Pattern Recognition, 1-10.

[8]. Wu Z, Shen C, Hengel A. Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 2016 Comp. Vis. Pat. Rec.,1-12.

[9]. Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. 2018, Comp. Vis. Pat. Rec, 201-211.

[10]. Santosh Kumar Mishra, Gaurav Rai, Sriparna Saha, and Pushpak Bhattacharyya. 2021. Effic. Cha. Att. En., 21 3, Article 49, 17

[11]. Chen P. Efficient Channel Allocation Tree Generation for Data Broadcasting in a Mobile Computing Environment. 2003, Wire. Net., 200-213.

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