Use ResNet50V2 Deep Learning Model to Classify Five Animal Species
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Published:
Dec 30, 2023
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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.
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How to Cite
Hindarto, D. (2023). Use ResNet50V2 Deep Learning Model to Classify Five Animal Species. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(4), 758–768. https://doi.org/10.35870/jtik.v7i4.1845
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Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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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