Indian Food Image Classification Using Convolutional Neural Network

Abstract

Abstract: Food classification using Convolutional Neural Networks (CNNs) has gained significant attention due to its potential applications in dietary analysis, food recommendation systems, and nutrition monitoring. In this study, we present a CNN-based approach for food classification, leveraging image data augmentation to enhance the model's ability to generalize to new and unseen food images. We utilize a dataset containing diverse food categories and preprocess the images by resizing and normalization. The proposed CNN architecture consists of multiple convolutional and pooling layers, along with dropout and batch normalization for regularization. To prevent overfitting, we employ early stopping as a custom callback during model training. The experimental results demonstrate promising training accuracy of 90.13% and relatively low training loss of 0.3066, indicating effective learning from the training data. the validation accuracy of 69.00% and validation loss of 1.270.