Papers › Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
Javier Marin, Aritro Biswas, Ferda Ofli, Nicholas Hynes, Amaia Salvador, Yusuf Aytar, Ingmar Weber, Antonio Torralba
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Modal Retrieval | Recipe1M+ | Marin et al. | Image-to-text R@1 | 17 | #2 of 2 | Archive leaderboard | report |
| Cross-Modal Retrieval | Recipe1M+ | Marin et al. | Text-to-image R@1 | 21 | #2 of 2 | Archive leaderboard | report |
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