{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/food-ingredients-recognition-through-multi","title":"Food Ingredients Recognition through Multi-label Learning","arxiv_id":"1707.08816","date":"2017-07-27","proceeding":null,"authors":["Marc Bolaños","Aina Ferrà","Petia Radeva"],"abstract":"Automatically constructing a food diary that tracks the ingredients consumed\ncan help people follow a healthy diet. We tackle the problem of food\ningredients recognition as a multi-label learning problem. We propose a method\nfor adapting a highly performing state of the art CNN in order to act as a\nmulti-label predictor for learning recipes in terms of their list of\ningredients. We prove that our model is able to, given a picture, predict its\nlist of ingredients, even if the recipe corresponding to the picture has never\nbeen seen by the model. We make public two new datasets suitable for this\npurpose. Furthermore, we prove that a model trained with a high variability of\nrecipes and ingredients is able to generalize better on new data, and visualize\nhow it specializes each of its neurons to different ingredients.","url_abs":"http://arxiv.org/abs/1707.08816v1","url_pdf":"http://arxiv.org/pdf/1707.08816v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"food-ingredients-recognition-through-multi","repo_url":"https://github.com/kshen3778/Ingredient-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"food-ingredients-recognition-through-multi","repo_url":"https://github.com/MarcBS/food_ingredients_recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[{"slug":"food-image-classification-dataset","name":"Food Image Classification Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}