Indian food image classification using transfer learning
Joeal Bijoy Thomas, Abhijay Kshirsagar, Pavan Borate, Prathamesh Sharma
Classification, Deep learning, larger datasets, Computational resources, Convolutional neural network, Image classification, Transfer learning, Indian food dataset
Classification has been facilitated by the advancements in deep learning, coupled with the accessibility of larger datasets and enhanced computational capabilities. Among the various techniques, the convolutional neural network stands out as the predominant and widely adopted method for image classification in recent times. This project focuses on classifying images from an Indian food dataset using a range of transfer learning approaches. The significance of food in human life cannot be overstated, as it serves as a crucial source of essential nutrients. It is imperative for individuals to be mindful of their dietary choices to maintain a healthy lifestyle. Consequently, food classification plays a pivotal role in promoting overall well-being. In contrast to conventional approaches that involve building models from scratch, this project leverages pre-trained models. This not only reduces computational overhead and costs but also leads to improved outcomes. The dataset comprises 20 classes of Indian food, with 500 images allocated to each class for training and validation.
Article Details
Unique Paper ID: 162356

Publication Volume & Issue: Volume 10, Issue 9

Page(s): 289 - 292
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