PyTorch Deep Learning for Food Image Classification with Food Dataset
DOI:
10.33395/sinkron.v8i4.12987Keywords:
Convolutional Neural Networks, Classification, Food Image, PyTorch, Image DatasetsAbstract
Classification of food images is crucial in today's increasingly digitally connected world. In the rapidly evolving mobile applications and social media era, the demand for an automated system that can recognize food types from an image is intensifying. This study employs deep learning and the PyTorch framework to develop a dependable and efficient solution for classifying food images. This research is motivated by the growing complexity of food introduction challenges. The primary challenge is improving the accuracy of food type recognition and overcoming variations in the visual presentation of food, such as lighting, shooting angles, and proportional and textural differences. Convolutional Neural Networks (CNN) are effective for image classification and are incorporated into the methods utilized. In addition, we employ ResNet101 transfer learning techniques to capitalize on the knowledge of trained models for large image datasets. The primary objective of this study is to develop a food image classification model that is accurate, training-efficient, and capable of accurately recognizing various types of food. In testing and evaluation, the developed model could realize multiple types of food with satisfactory accuracy. The accuracy of training reached 99.35%, while the accuracy of testing reached 94.65%. This study also reveals how Resnet101 transfer learning is utilized by deep learning technology.
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Adedeji, B. P. (2023). Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles. Results in Engineering, 19(July), 101283. https://doi.org/10.1016/j.rineng.2023.101283
Chena, Y., Lina, Y., Xua, X., Dinga, J., Li, C., Zenga, Y., Liue, W., Xie, W., & Huang, J. (2022). Classification of lungs infected COVID-19 images based on inception-ResNet. Computer Methods and Programs in Biomedicine.
Czinege, I., & Harangozó, D. (2023). Application of artificial neural networks for characterisation of formability properties of sheet metals. International Journal of Lightweight Materials and Manufacture. https://doi.org/10.1016/j.ijlmm.2023.08.003
Dhanush, G., Khatri, N., Kumar, S., & Shukla, P. K. (2023). A comprehensive review of machine vision systems and artificial intelligence algorithms for the detection and harvesting of agricultural produce. Scientific African, 21(July), e01798. https://doi.org/10.1016/j.sciaf.2023.e01798
Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems, 139, 100–108. https://doi.org/10.1016/j.future.2022.09.018
Gawade, S., Bhansali, A., Patil, K., & Shaikh, D. (2023). Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection. Healthcare Analytics, 3(February), 100153. https://doi.org/10.1016/j.health.2023.100153
Hindarto, D., & Santoso, H. (2021). Plat Nomor Kendaraan dengan Convolution Neural Network. Jurnal Inovasi Informatika, 6(2), 1–12. https://doi.org/10.51170/jii.v6i2.202
Kumar, A., & Seeja, K. R. (2023). Periocular Region based Gender Identification using Transfer Learning. International Journal of Cognitive Computing in Engineering, 4(July), 277–286. https://doi.org/10.1016/j.ijcce.2023.07.003
Niswati, Z., Hardatin, R., Muslimah, M. N., & Hasanah, S. N. (2021). Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear. Faktor Exacta, 14(3), 160. https://doi.org/10.30998/faktorexacta.v14i3.10010
Ozbayoglu, A. M., & Yuksel, H. E. (2012). Analysis of gas-liquid behavior in eccentric horizontal annuli with image processing and artificial intelligence techniques. Journal of Petroleum Science and Engineering, 81, 31–40. https://doi.org/10.1016/j.petrol.2011.12.008
Pérez-García, F., Sparks, R., & Ourselin, S. (2021). TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine, 208. https://doi.org/10.1016/j.cmpb.2021.106236
Pribanić, I., Simić, S. D., Tanković, N., Debeljuh, D. D., & Jurković, S. (2023). Reduction of SPECT acquisition time using deep learning: A phantom study. Physica Medica, 111(January). https://doi.org/10.1016/j.ejmp.2023.102615
Saputra, A. D., Hindarto, D., & Santoso, H. (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron, 8(1), 48–55. https://doi.org/10.33395/sinkron.v8i1.11906
Swift, J. R., Turner, M. A., & Reynolds, J. C. (2023). A rapid dynamic headspace method for authentication of whiskies using artificial neural networks. Food Chemistry Advances, 3(June), 100417. https://doi.org/10.1016/j.focha.2023.100417
Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.
Zimmermann, L., Buschmann, M., Herrmann, H., Heilemann, G., Kuess, P., Goldner, G., Nyholm, T., Georg, D., & Nesvacil, N. (2021). An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR. Zeitschrift Fur Medizinische Physik, 31(1), 78–88. https://doi.org/10.1016/j.zemedi.2020.10.004
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Copyright (c) 2023 Iswahyudi, Djarot Hindarto, Handri Santoso
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