Automatic Food Label Detection in Images Using Convolutional Neural Network with Food-101 Dataset

Abstract

automatic detection of food labels from digital images has emerged as a crucial application in dietary analysis, nutrition monitoring, and smart culinary systems. This study presents the implementation of a Convolutional Neural Network (CNN) model for food label recognition using the Food-101 dataset, which consists of over 101,000 images from 101 distinct food categories. The proposed system follows a systematic pipeline that includes image resizing, normalization, and data augmentation to enhance model robustness and performance. The CNN architecture is designed with multiple convolutional and pooling layers, followed by dense and softmax output layers for final classification. The training was conducted using the Adam optimizer with a learning rate of 0.0001, batch size of 32, and dropout regularization to prevent overfitting. Experimental results demonstrate a classification accuracy of 24.45% after one training epoch, highlighting both the capability and limitations of the baseline CNN model. Despite moderate accuracy, the model successfully identifies visually distinguishable food items and sets a foundation for future improvements through transfer learning and fine-tuning. This research confirms the potential of CNN-based models for food label detection and provides insights for the development of more accurate food recognition systems in health, dietary, and culinary applications