Image to Recipe

Image-to-recipe research aims to automatically generate recipes from food images, bridging the gap between visual food data and culinary instructions. Current approaches leverage deep learning, employing convolutional neural networks for image processing and recurrent or transformer-based architectures for recipe generation (including ingredients and steps), often incorporating transfer learning and pre-trained models like ResNet and CLIP to improve efficiency and accuracy. This field is advancing through the development of more robust and efficient models, focusing on handling diverse datasets, improving ingredient substitution capabilities, and enabling cross-lingual recipe retrieval. The ultimate goal is to create personalized and accessible cooking experiences through intelligent systems.