Download pdf
ResNet50 Architecture Based Dog Breed Identification Using Deep Learning
- 1 Dept. of Computer Science and Design, Kongu Engineering College, Perundurai, Tamilna-du-638060, India
- 2 Dept. of Information Technology, Kongu Engineering College, Perundurai, Tamilnadu-638060, India
- 3 Dept. of Computer Science and Design, Kongu Engineering College, Perundurai, Tamilna-du-638060, India
- 4 Dept. of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu-638060, India
* Author to whom correspondence should be addressed.
Abstract
Dog breed identification is one of the challenging task in animal husbandry. Knowing what a dog was bred for can assist you in predicting and comprehending its behavior. Most breeds were created with a specific goal in mind: to have the desire to achieve something specific. Dogs retain part of that drive as pets in our homes, and without a good outlet for that energy, it might manifest in undesirable ways. Developing a deep learning model, that can identify the breed of the dog with high accuracy, using this model one can easily identify the dog breed which makes easier for them to train the dog, so that we can’t wait for the experts and no need for spending money for identifying the dog breed, using the mobile phones we can easily identify our dog breed simply using its image. This paper deals with an advanced classification model was designed which identifies the breed of the dog. A training dataset consisting of more than 10000 images is used, CNN is used for the classification and the accuracy is achieved using Resnet50 architecture which is highest when compared to other models.
Keywords
ResNet50., Dog Breed, Convolutional Neural Network
[1]. Wu, Ren, et al. "Deep image: Scaling up image recognition." arXiv preprint arXiv:1501.02876 7.8 (2015).
[2]. Jain, Anil K., Jianchang Mao, and K. Moidin Mohiuddin. "Artificial neural networks: A tutori-al." Computer 29.3 (1996): 31-44.
[3]. How Many Dogs Are There In The World?, Mar 2019, [online] Available: https://www.worldatlas.com/articles/how-many-dogs-are-there-in-theworld.html.
[4]. P. Borwarnginn, K. Thongkanchorn, S. Kanchanapreechakorn and W. Kusakunniran, "Break-through Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning", 2019 11th Int. Conf. Inf. Technol. Electr. Eng. ICITEE 2019, vol. 7, pp. 1-5, 2019.
[5]. M. Chanvichitkul, P. Kumhom and K. Chamnongthai, "Face recognition based dog breed clas-sification using coarse-to-fine concept and PCA", 2007 Asia-Pacific Conf. Commun. APCC, pp. 25-29, 2007.
[6]. J. Liu, A. Kanazawa, D. Jacobs and P. Belhumeur, "Dog Breed Classification Using Part Lo-calization", European Conference on Computer Vision, pp. 172-185, 2012.
[7]. J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierar-chical image database", 2009 IEEE Conference on Computer Vision and Pattern Recogni-tion, pp. 248-255, 2009.
[8]. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., "ImageNet Large Scale Visual Recognition Challenge", International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, Dec. 2015.
[9]. B. A. M. Ashqar and S. S. Abu-Naser, "Identifying Images of Invasive Hydrangea Using Pre-Trained Deep Convolutional Neural Networks", Int. J. Control Autom, vol. 12, no. 4, pp. 15-28, 2019.
[10]. Hsu, David. "Using convolutional neural networks to classify dog breeds." CS231n: Convolu-tional Neural Networks for Visual Recognition [course webpage] 2 (2015)
[11]. Günel, Mehmet. "GoogLeNet." (2016).
[12]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
[13]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand pre-diction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.
[14]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.
[15]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.
[16]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.
[17]. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.
[18]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisa-tion for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.
[19]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.
[20]. Easwaramoorthy, S., Moorthy, U., Kumar, C. A., Bhushan, S. B., & Sadagopan, V. (2017, January). Content based image retrieval with enhanced privacy in cloud using apache spark. In International Conference on Data Science Analytics and Applications (pp. 114-128). Springer, Singapore.
[21]. Sathishkumar, V. E., Agrawal, P., Park, J., & Cho, Y. (2020, April). Bike Sharing Demand Prediction Using Multiheaded Convolution Neural Networks. In Basic & Clinical Pharma-cology & Toxicology (Vol. 126, pp. 264-265). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[22]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2021). The role of contemporary digital tools and technologies in Covid‐19 cri-sis: An exploratory analysis. Expert systems.
[23]. Sathishkumar, V. E., Rahman, A. B. M., Park, J., Shin, C., & Cho, Y. (2020, April). Using machine learning algorithms for fruit disease classification. In Basic & clinical pharmacolo-gy & toxicology (Vol. 126, pp. 253-253). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[24]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[25]. Easwaramoorthy, S., Sophia, F., & Prathik, A. (2016, February). Biometric Authentication using finger nails. In 2016 international conference on emerging trends in engineering, technology and science (ICETETS) (pp. 1-6). IEEE.
[26]. Babu, J. C., Kumar, M. S., Jayagopal, P., Sathishkumar, V. E., Rajendran, S., Kumar, S., ... & Mahseena, A. M. (2022). IoT-Based Intelligent System for Internal Crack Detection in Building Blocks. Journal of Nanomaterials, 2022.
[27]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.
[28]. Rajalaxmi, R. R., Narasimha Prasad, L. V., Janakiramaiah, B., Pavankumar, C. S., Neelima, N., & Sathishkumar, V. E. (2022). Optimizing Hyperparameters and Performance Analysis of LSTM Model in Detecting Fake News on Social media. Transactions on Asian and Low-Resource Language Information Processing.
Cite this article
S.,S.;R.,T.;S.,A.;S.,Y.P. (2023). ResNet50 Architecture Based Dog Breed Identification Using Deep Learning. Applied and Computational Engineering,2,503-511.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).