Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
This provides a foundational resource for researchers in food and cooking AI, enabling high-capacity model training on aligned multimodal data.
The authors introduced Recipe1M+, a large-scale dataset with over one million recipes and 13 million images, and used it to train a neural network for cross-modal embeddings, achieving impressive results in image-recipe retrieval that rival human performance.
In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images. As the largest publicly available collection of recipe data, Recipe1M+ affords the ability to train high-capacity modelson aligned, multimodal data. Using these data, we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M+ dataset and food and cooking in general. Code, data and models are publicly available.