Food Image Classification

Food image classification uses computer vision to automatically identify food items in images, aiming to improve dietary assessment, reduce food waste, and enhance food production efficiency. Current research focuses on improving accuracy and speed, often employing deep learning architectures like EfficientNet and incorporating techniques such as transfer learning, attention mechanisms (like CBAM), and data augmentation to handle the inherent variability in food images. This field is significant for its potential applications in personalized nutrition, automated dietary analysis, and the development of more efficient and sustainable food systems.