Published: 2023-12-30
Use ResNet50V2 Deep Learning Model to Classify Five Animal Species
DOI: 10.35870/jtik.v7i4.1845
Downloads
Abstract
This study employs the ResNet50V2 Deep Learning model for the purpose of classifying five distinct animal species. To gain insights into the model's proficiency in visual recognition, we conducted training and testing procedures on a dataset comprising diverse images of animal species. The utilization of ResNet50V2 in this classification task is intended to discern visual distinctions among these species by leveraging the distinctive characteristics present in the input images. A meticulous and comprehensive training procedure was undertaken on the model, employing fine-tuning techniques to adjust its internal representation in order to accommodate diverse animal characteristics. The experimental findings illustrate the model's capacity to effectively discern and categorize various animal species with a notable degree of precision, thereby presenting encouraging outcomes for the potential utilization of this model in broader animal classification contexts. This study emphasizes the significant potential of employing Deep Learning models, specifically ResNet50V2, for the purpose of comprehending and identifying diverse fauna through visual cues.
Keywords
No keywords available.
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 7 No. 4 (2023)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2023
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/jtik.v7i4.1845
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Djarot Hindarto
nformatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, South Jakarta City, Special Capital Region of Jakarta, Indonesia
-
Hindarto, D., 2023. Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2819-2826. DOI: https://doi.org/10.33395/sinkron.v8i4.13130.
-
Hindarto, D., 2023. Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2810-2818. DOI: https://doi.org/10.33395/sinkron.v8i4.13124.
-
Sze, E., Santoso, H. and Hindarto, D., 2022. Review Star Hotels Using Convolutional Neural Network. Sinkron: jurnal dan penelitian teknik informatika, 7(4), pp.2469-2477. DOI: https://doi.org/10.33395/sinkron.v7i4.11836.
-
Iswahyudi, I., Hindarto, D. and Santoso, H., 2023. PyTorch Deep Learning for Food Image Classification with Food Dataset. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2651-2661. DOI: https://doi.org/10.33395/sinkron.v8i4.12987.
-
Hindarto, D. and Santoso, H., 2022. Performance Comparison of Supervised Learning Using Non-Neural Network and Neural Network. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 11(1), pp.49-62. DOI: https://doi.org/10.23887/janapati.v11i1.40768.
-
Hindarto, D., 2023. Comparison of Detection with Transfer Learning Architecture RestNet18, RestNet50, RestNet101 on Corn Leaf Disease. Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), 8(2), pp.41-48. DOI: https://doi.org/10.20527/jtiulm.v8i2.174.
-
Hindarto, D., 2023. Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2776-2785. DOI: https://doi.org/10.33395/sinkron.v8i4.13100.
-
Du, L., Lu, Z. and Li, D., 2022. Broodstock breeding behaviour recognition based on Resnet50-LSTM with CBAM attention mechanism. Computers and Electronics in Agriculture, 202, p.107404. DOI: https://doi.org/10.1016/j.compag.2022.107404.
-
Boulila, W., Alzahem, A., Koubaa, A., Benjdira, B. and Ammar, A., 2023. Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Computers and Electronics in Agriculture, 212, p.108154. DOI: https://doi.org/10.1016/j.compag.2023.108154.
-
Panthakkan, A., Anzar, S.M., Jamal, S. and Mansoor, W., 2022. Concatenated Xception-ResNet50—A novel hybrid approach for accurate skin cancer prediction. Computers in Biology and Medicine, 150, p.106170. DOI: https://doi.org/10.1016/j.compbiomed.2022.106170.
-
Sharma, A.K., Nandal, A., Dhaka, A., Zhou, L., Alhudhaif, A., Alenezi, F. and Polat, K., 2023. Brain tumor classification using the modified ResNet50 model based on transfer learning. Biomedical Signal Processing and Control, 86, p.105299. DOI: https://doi.org/10.1016/j.bspc.2023.105299.
-
Sharma, A.K., Nandal, A., Dhaka, A., Polat, K., Alwadie, R., Alenezi, F. and Alhudhaif, A., 2023. HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection. Biomedical Signal Processing and Control, 84, p.104737. DOI: https://doi.org/10.1016/j.bspc.2023.104737.
-
Rahimzadeh, M. and Attar, A., 2020. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in medicine unlocked, 19, p.100360. DOI: https://doi.org/10.1016/j.imu.2020.100360.
-
Reis, H.C. and Turk, V., 2023. Detection of forest fire using deep convolutional neural networks with transfer learning approach. Applied Soft Computing, 143, p.110362. DOI: https://doi.org/10.1016/j.asoc.2023.110362.
-
Hitelman, A., Edan, Y., Godo, A., Berenstein, R., Lepar, J. and Halachmi, I., 2022. Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture, 194, p.106713. DOI: https://doi.org/10.1016/j.compag.2022.106713.
-
de Jesus Silva, L.F., Cortes, O.A.C. and Diniz, J.O.B., 2023. A novel ensemble CNN model for COVID-19 classification in computerized tomography scans. Results in Control and Optimization, 11, p.100215. DOI: https://doi.org/10.1016/j.rico.2023.100215.
-
Clark, M.L., Salas, L., Baligar, S., Quinn, C.A., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J. and Newsam, S., 2023. The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. Ecological Informatics, 75, p.102065. DOI: https://doi.org/10.1016/j.ecoinf.2023.102065.
-
Rahimzadeh, M., Attar, A. and Sakhaei, S.M., 2021. A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomedical Signal Processing and Control, 68, p.102588. DOI: https://doi.org/10.1016/j.bspc.2021.102588.
-
Hitelman, A., Edan, Y., Godo, A., Berenstein, R., Lepar, J. and Halachmi, I., 2022. The effect of age on young sheep biometric identification. Animal, 16(2), p.100452. DOI: https://doi.org/10.1016/j.animal.2021.100452
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.