Optimized Transfer Learning for Dog Breed Classification

Authors

  • Ambuj Kumar Agarwal Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India
  • Vidhu Kiran Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
  • Rupesh Kumar Jindal Dy. Registrar, National Institute of Design, Ahmedabad
  • Deepak Chaudhary Director, Swastik Agro Foods
  • Raj Gaurang Tiwari Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Keywords:

Classification, Transfer learning, Deep learning, Image processing

Abstract

Animal breed classification using deep learning algorithms is required in presentation arenas. In this paper, a dataset of 70 dog breeds was considered for training and testing of transfer deep learning algorithms. The used dataset is a statistically stable dataset including approximately 100 images under each category of dog breeds. Then collected dataset was trained and tested using different deep learning algorithms like Convolutional Neural Network, VGG16, ResNet, DenseNet, InceptionNet, InceptionResNet, etc, were implemented. The outcome results were compared during algorithm training and testing based on parameters like accuracy, precision, recall, and area under curve. Further, one of the best algorithms was optimized by tuning through optimization algorithms or learning rate configurations. In the future, the proposed modules will be added along with implementations in events to fulfill the requirement of real-time dog breed recognition.

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References

Khullar, V., Tiwari, R.G., Agarwal, A.K. and Dutta, S., 2021. Physiological Signals Based Anxiety Detection Using Ensemble Machine Learning. In Cyber Intelligence and Information Retrieval (pp. 597-608). Springer, Singapore.

Bajpai, P., Kumar, P. and Tewari, R.G., 2017. Greedy Algorithm for Image Compression in Image Processing. International Journal of Computer Applications, 166(8).

Agarwal, A.K., Tiwari, R.G., Khullar, V. and Kaushal, R.K., 2021, August. Transfer Learning Inspired Fish Species Classification. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 1154-1159). IEEE.

Pandey, D., Tiwari, R.G. and Kumar, P., Machine Learning: Adaptive Negotiation Agents in E-Commerce, International Journal of Computer Applications, Vol 166(10), pp 21-30, 2017.

Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I. and Tuba, M., 2020. Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms, 13(3), p.67.

Shankar, K., Zhang, Y., Liu, Y., Wu, L. and Chen, C.H., 2020. Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access, 8, pp.118164-118173.

Ananthakrishnan, B., V. . Padmaja, S. . Nayagi, and V. . M. “Deep Neural Network Based Anomaly Detection for Real Time Video Surveillance”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 54-64, doi:10.17762/ijritcc.v10i4.5534.

Yang, Z., Yu, W., Liang, P., Guo, H., Xia, L., Zhang, F., Ma, Y. and Ma, J., 2019. Deep transfer learning for military object recognition under small training set condition. Neural Computing and Applications, 31(10), pp.6469-6478.

Xu, W., Wan, Y., Zuo, T.Y. and Sha, X.M., 2020. Transfer learning based data feature transfer for fault diagnosis. IEEE Access, 8, pp.76120-76129.

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H. and He, Q., 2020. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), pp.43-76.

Paithane, P. M., & Kakarwal, D. (2022). Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 98–104. https://doi.org/10.18201/ijisae.2022.272

Xuhong, L.I., Grandvalet, Y. and Davoine, F., 2018, July. Explicit inductive bias for transfer learning with convolutional networks. In International Conference on Machine Learning (pp. 2825-2834). PMLR.

Yang, L., Song, S. and Chen, C.P., 2018, October. Transductive transfer learning based on broad learning system. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 912-917). IEEE.

Sanodiya, R.K. and Yao, L., 2020. Unsupervised transfer learning via relative distance comparisons. IEEE Access, 8, pp.110290-110305.

Agarwal, D. A. . (2022). Advancing Privacy and Security of Internet of Things to Find Integrated Solutions. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 05–08. https://doi.org/10.17762/ijfrcsce.v8i2.2067

Kumar, A. and Kumar, A., 2020, December. Dog breed classifier for facial recognition using convolutional neural networks. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 508-513). IEEE.

Jain, R., Singh, A. and Kumar, P., 2020. Dog Breed Classification Using Transfer Learning. ICCII 2018, p.579.

Borwarnginn, P., Kusakunniran, W., Karnjanapreechakorn, S. and Thongkanchorn, K., 2021. Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning. International Journal of Automation and Computing, 18(1), pp.45-54.

Devikar, Pratik. 2016. Transfer Learning for Image Classification of Various Dog Breeds. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 5 (12). ISSN: 2278-1323.

Liu, Jiongxin, Angjoo Kanazawa, David Jacobs, and Peter Belhumeur. Dog Breed Classification Using Part Localization.

Dąbrowski, Marek, Tomasz Michalik. How Effective is Transfer Learning Method for Image Classification. Position Papers of the Federated Conference on Computer Science and Information Systems, vol. 12, 3–9 https://doi.org/10.15439/2017f526 ISSN 2300-5963 ACSIS

Vrbančič, G., Pečnik, Š. and Podgorelec, V., 2020, August. Identification of COVID-19 X-ray Images using CNN with Optimized Tuning of Transfer Learning. In 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.

Linda R. Musser. (2020). Older Engineering Books are Open Educational Resources. Journal of Online Engineering Education, 11(2), 08–10. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/41

Zhang, Z., 2018, June. Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1-2). IEEE.

Dog Breeds-Image Data Set, https://www.kaggle.com/gpiosenka/70-dog-breedsimage-data-set, Accessed on 26.10.2021.

Overall Methodology of Implementing Optimized Transfer Learning for Dog Breed Classification

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Published

15.10.2022

How to Cite

[1]
A. K. . Agarwal, V. . Kiran, R. K. . Jindal, D. . Chaudhary, and R. G. . Tiwari, “Optimized Transfer Learning for Dog Breed Classification”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 18–22, Oct. 2022.