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  • RecipeQA: A Dataset for Multimodal Comprehension of Cooking Recipes

  • Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

  • Recipe5K: dataset for ingredients recognition with 4,826 unique recipes composed of an image and the corresponding list of ingredients.

  • YouCook2 is one of the largest task-oriented, instructional video dataset in the vision community

  • Food.com Crawled data from Food.com (GeniusKitchen) online recipe aggregator

  • Food related datasets at data.world

  • The Rakuten dataset with about 800,000 recipes and images

  • Open Food Data, includes, amongst others, a link to a very large database of food facts as MongoDB, JSONL, excel, substitute foods, recipes, and beers.

Representing recipes

Google provides a useful schema for representing recipes: https://developers.google.com/search/docs/data-types/recipe#recipe-properties. It includes, amongst others, the following elements:

  • name of the dish

  • cooking time, i.e. the time it takes to actually cook the dish (in ISO 8601 format)

  • preparation time, i.e. the time it takes to prepare ingredients and workspace for the dish

  • ingredients used in the recipe

  • recipe instructions, i.e. the steps to make the dish

  • yield, i.e. the quantity produced by the recipe, either the number of servings produced or a different unit (for example, number of items)

Recipe selection:

  • category, i.e. the type of meal or course your recipe is about, e.g., dinner, main course, dessert, snack

  • cuisine, i.e. the region associated with your recipe, e.g., French, Mediterranean, or American

Visual support:

  • image (of the completed dish)

  • video depicting the steps to make the dish

Additional info:

  • summary describing the dish

  • average review score

  • author

  • date the recipe was published

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