Open Food Facts Database

  What is 🍊 Open Food Facts?





  A food products database

Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.

  Made by everyone

Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database.

Open Food Facts Nutrition table detection dataset

This dataset was used to train the nutrition table object detection model running in production at Open Food Facts. Images were collected from the Open Food Facts database and labeled manually. Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).

  Fields

image_id: Unique identifier for the image, generated from the barcode and… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutrition-table-detection.

Open Food Facts gathers information and data on food products from around the world.

Spellcheck benchmark

The benchmark (v5) is composed of 152 lists of ingredients extracted from the Open Food Facts database, then corrected to ensure that each ingredient is recognized. Its purpose is to evaluate the Spellcheck on correcting products list of ingredients in respect of the OFF guidelines. A portion of the data was synthetically generated using OpenAI-GPT3.5-Turbo prompted for this task. Once composed, the benchmark was then checked with the annotation tool Argilla… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/spellcheck-benchmark.

Open Food Facts Dataset is a data set composed of food nutritional components, which contains information on nutrients, active ingredients, and allergens of more than 100,000 foods. It was produced collaboratively by volunteers from more than 150 countries.

Open Food Facts Nutriscore detection dataset

This dataset was used to train the Nutri-score object detection model running in production at Open Food Facts. Images were collected from the Open Food Facts database and labeled manually. Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).

  Fields

image_id: Unique identifier for the image, generated from the barcode and the image… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutriscore-object-detection.

Data extracted from openfood fact API.

To extract it from the endpoint run

curl "https://world.openfoodfacts.org/cgi/search.pl?action=process&tagtype_0=categories&tag_contains_0=contains&tag_0=cheeses&tagtype_1=labels&&json=1" > /tmp/openfood.json

Open Prices

What is Open Prices?

Open Prices is a project to collect and share prices of products around the world. It's a publicly available dataset that can be used for research, analysis, and more. Open Prices is developed and maintained by Open Food Facts.

There are currently few companies that own large databases of product prices at the barcode level. These prices are not freely available, but sold at a high price to private actors, researchers and other organizations that can afford them.

Open Prices aims to democratize access to price data by collecting and sharing product prices under an open licence. The data is available under the Open Database License (ODbL), which means that it can be used for any purpose, as long as you credit Open Prices and share any modifications you make to the dataset. Images submitted as proof are licensed under the Creative Commons Attribution-ShareAlike 4.0 International.

Dataset description

This dataset contains in Parquet format all price information contained in the Open Prices database. The dataset is updated daily.

Here is a description of the most important columns:

• id: The ID of the price in DB
• product_code: The barcode of the product, null if the product is a "raw" product (fruit, vegetable, etc.)
• category_tag: The category of the product, only present for "raw" products. We follow Open Food Facts category taxonomy for category IDs.
• labels_tags: The labels of the product, only present for "raw" products. We follow Open Food Facts label taxonomy for label IDs.
• origins_tags: The origins of the product, only present for "raw" products. We follow Open Food Facts origin taxonomy for origin IDs.
• price: The price of the product, with the discount if any.
• price_is_discounted: Whether the price is discounted or not.
• price_without_discount: The price of the product without discount, null if the price is not discounted.
• price_per: The unit for which the price is given (e.g. "KILOGRAM", "UNIT")
• currency: The currency of the price
• location_osm_id: The OpenStreetMap ID of the location where the price was recorded. We use OpenStreetMap to identify uniquely the store where the price was recorded.
• location_osm_type: The type of the OpenStreetMap location (e.g. "NODE", "WAY")
• location_id: The ID of the location in the Open Prices database
• date: The date when the price was recorded
• proof_id: The ID of the proof of the price in the Open Prices DB
• owner: a hash of the owner of the price, for privacy.
• created: The date when the price was created in the Open Prices DB
• updated: The date when the price was last updated in the Open Prices DB
• proof_file_path: The path to the proof file in the Open Prices DB
• proof_type: The type of the proof. Possible values are RECEIPT, PRICE_TAG, GDPR_REQUEST, SHOP_IMPORT
• proof_date: The date of the proof
• proof_currency: The currency of the proof, should be the same as the price currency
• proof_created: The datetime when the proof was created in the Open Prices DB
• proof_updated: The datetime when the proof was last updated in the Open Prices DB
• location_osm_display_name: The display name of the OpenStreetMap location
• location_osm_address_city: The city of the OpenStreetMap location
• location_osm_address_postcode: The postcode of the OpenStreetMap location

How can I download images?

All images can be accessed under the https://prices.openfoodfacts.org/img/ base URL. You just have to concatenate the proof_file_path column to this base URL to get the full URL of the image (ex: https://prices.openfoodfacts.org/img/0010/lqGHf3ZcVR.webp).

Can I contribute to Open Prices?

Of course! You can contribute by adding prices, trough the Open Prices website or through Open Food Facts mobile app.

To participate in the technical development, you can check the Open Prices GitHub repository.

The site less-de-sel.fr operates an adapted version of the database Open Food Facts to highlight the salt content of industrial products. The site thus allows the consumer to better choose among these products, especially in the context of a low-salt diet.

Nutrient extraction dataset

This dataset contains annotated images of nutrition tables. The goal of this dataset was to train a model to extract nutrient values from nutrition tables, as part of the Nutrisight project. It contains ~3k samples in total (2.8k for training and 199 for testing). For more information about the project, please refer to the nutrisight directory in the openfoodfacts-ai GitHub repository. The images were collected from the Open Food Facts database, and… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutrient-detection-layout.

Open Food Facts répertorie les informations sur les produits alimentaires : ingrédients, informations nutritionnelles, labels etc. Les données proviennent majoritairement de la collecte citoyenne (crowdsourcing) des informations.

ce fichier contient la liste des produits référencés sur la base de données open food facts pour lesquels le code emballeur fait référence à la région aquitaine. Ce tableur contient l'ensemble des informations extraites ou saisies par les contributeurs

Spellcheck dataset

This dataset is used to train a Seq2Seq model designed to fix ingredient lists of Open Food Facts products. Products were extracted from the Open Food Facts database (JSONL) along the lang and the list of ingredients. These products were selected in respect of some criteria:

20 to 40% unknown ingredients computed during the Ingredient Extraction Analysis, No duplicate in the list of ingredients, No duplicate with the spellcheck-benchmark

Once extracted… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/spellcheck-dataset.

Open Products Facts est une base de données collaborative, gratuite et ouverte qui rassemble des informations sur une multitude de produits à travers le monde. Les données sont publiées sous la licence "Open Database License", ce qui vous autorise à les réutiliser, y compris pour un usage commercial, à condition de mentionner la source et de partager vos modifications sous les mêmes conditions. Vous pouvez accéder aux données de plusieurs manières : * Exports de données : Des exports complets de la base de données sont disponibles aux formats MongoDB, JSONL, CSV et RDF, avec des mises à jour quotidiennes. Des exports "delta" contenant uniquement les modifications des 14 derniers jours sont également proposés. * API : Une API JSON et une API XML expérimentale vous permettent d'accéder directement aux données d'un produit. * Kits de développement (SDK) : Des outils pour différents langages de programmation sont à votre disposition pour faciliter l'intégration des données dans vos applications. La communauté Open Products Facts est active et vous pouvez échanger avec d'autres utilisateurs et les développeurs sur notre forum ou chat. Vous pouvez également contribuer au projet en faisant un don ou en aidant à améliorer la documentation. L'application mobile Open Food Facts (compatible avec Open Products Facts) vous permet de scanner des produits, de consulter leurs informations et d'ajouter de nouveaux produits à la base de données.

Open Food Facts répertorie les informations sur les produits cosmétiques : ingrédients, additifs, labels etc. Les données proviennent majoritairement de la collecte citoyenne (crowdsourcing) des informations.

Dataset for a Food Ontology Integrating HUMMUS and Open Food Facts with Extended User Attributes

This dataset supports a food ontology that semantically integrates two existing resources: the HUMMUS knowledge graph (focused on recipes, users, and reviews) and Open Food Facts (OFF, focused on nutritional information for packaged foods). In addition to aligning entities and concepts across the two sources, the ontology introduces new user-specific attributes to enable more fine-grained modeling of food preferences, constraints, and behaviors. The resource is intended for use in research on personalized nutrition, food recommendation systems, and knowledge-based AI applications.

File explanation:

• off: Open Food Facts dataset with product name normalized (more or less 10GB)
• hummus: HUMMUS recipe with recipe name normalized (more or less 2GB)
• hummus_review: HUMMUS review with inferred info (more or less 0.5GB)
• hummus_member: HUMMUS member info with inferred info (more or less 0.5GB)
• merging_file: file needed to merge the ontologies (more or less 6GB)
• food_ontology_complete: the complete merged ontology file, merged with a threshold of 0.975 (more or less 150GB)
• food_ontology_complete_085: the complete merged ontology file, merged with a threshold of 0.85 (more or less 270GB)

Prezzi aperti

Che cosa sono i prezzi aperti?

Open Prices è un progetto per raccogliere e condividere i prezzi dei prodotti in tutto il mondo. Si tratta di un set di dati pubblicamente disponibile che può essere utilizzato per la ricerca, l'analisi e altro ancora. Open Prices è sviluppato e mantenuto da Open Food Facts. Attualmente ci sono poche aziende che possiedono grandi banche dati dei prezzi dei prodotti a livello di codici a barre. Questi prezzi non sono liberamente disponibili, ma venduti ad un prezzo elevato ad attori privati, ricercatori e altre organizzazioni che possono permetterseli.

Open Prices mira a democratizzare l'accesso ai dati sui prezzi raccogliendo e condividendo i prezzi dei prodotti sotto una licenza aperta. I dati sono disponibili sotto la Open Database License (ODbL), il che significa che possono essere utilizzati per qualsiasi scopo, purché vengano accreditati i Prezzi Aperti e le eventuali modifiche apportate al set di dati.Le immagini inviate come prova sono sotto licenza Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/). ## Descrizione del set di dati

Questo set di dati contiene in formato Parquet tutte le informazioni sui prezzi contenute nel database Open Prices. Il dataset viene aggiornato quotidianamente.

Ecco una descrizione delle colonne più importanti:

• «id»: L'ID del prezzo in DB

• «codice_prodotto»: Il codice a barre del prodotto, nullo se il prodotto è un prodotto "grezzo" (frutta, verdura, ecc.)

• "category_tag": La categoria del prodotto, presente solo per i prodotti "grezzi". Seguiamo la tassonomia di categoria Open Food Facts per gli ID di categoria.

• "labels_tags": Le etichette del prodotto, presenti solo per i prodotti "grezzi". Seguiamo la tassonomia delle etichette Open Food Facts per gli ID delle etichette.

• «origins_tags»: Le origini del prodotto, presenti solo per i prodotti "grezzi". Seguiamo la tassonomia di origine Open Food Facts per gli ID di origine.

• «prezzo»: Il prezzo del prodotto, con l'eventuale sconto.

• «prezzo_è_attualizzato»: Se il prezzo è scontato o meno.

• «prezzo_senza_sconto»: Il prezzo del prodotto senza sconto, nullo se il prezzo non è scontato.

• «price_per»: L'unità per la quale è indicato il prezzo (ad esempio "KILOGRAM", "UNIT")

• "valuta": La valuta del prezzo

• "location_osm_id": L'ID OpenStreetMap del luogo in cui è stato registrato il prezzo. Usiamo OpenStreetMap per identificare in modo univoco il negozio in cui è stato registrato il prezzo.

• "location_osm_type": Il tipo di posizione OpenStreetMap (ad esempio "NODE", "WAY")

• "location_id": L'ID della posizione nel database Open Prices

• «data»: La data in cui è stato registrato il prezzo

• «proof_id»: L'ID della prova del prezzo nei Prezzi Aperti DB

• «proprietario»: un hash del proprietario del prezzo, per la privacy.

• «creato»: La data in cui il prezzo è stato creato nell'Open Prices DB

• «aggiornato»: La data in cui il prezzo è stato aggiornato l'ultima volta nei Prezzi Aperti DB

• "proof_file_path": Il percorso del file di prova nell'Open Prices DB

• «proof_type»: Il tipo di prova. I valori possibili sono "RECEIPT", "PRICE_TAG", "GDPR_REQUEST", "SHOP_IMPORT".

• «proof_date»: La data della prova

• «proof_currency»: La valuta della prova, dovrebbe essere la stessa della valuta del prezzo

• «proof_created»: La data in cui è stata creata la prova nell'Open Prices DB

• «proof_updated»: La data in cui la prova è stata aggiornata l'ultima volta nell'Open Prices DB

• "location_osm_display_name": Il nome visualizzato della posizione OpenStreetMap

• "location_osm_address_city": La città della posizione OpenStreetMap

• "location_osm_address_postcode": Il codice postale della posizione OpenStreetMap

Come posso scaricare le immagini?

Tutte le immagini sono accessibili alla pagina URL «https://prices.openfoodfacts.org/img/». Basta concatenare la colonna "proof_file_path" a questo URL di base per ottenere l'URL completo dell'immagine (es: https://prices.openfoodfacts.org/img/0010/lqGHf3ZcVR.webp).

Posso contribuire all'apertura dei prezzi?

Certo! Puoi contribuire aggiungendo i prezzi, attraverso il Open Prices website o attraverso l'app mobile Open Food Facts.

Per partecipare allo sviluppo tecnico, è possibile controllare il repository Open Prices GitHub.

OpenFood Classification

A subset of openfoodfacts/product-database selecting only a balanced set of countries and food categories.

  Labels

There are 2 main labels:

Country single label: The corresponding country of the food/dish among 8 possible values: italy, spain, germany, france, united-states, belgium, united-kingdom and switzerland. Category multi-label: The category it belongs to among 8 possible values: snacks, beverages, cereals-and-potatoes, plant-based-foods… See the full description on the dataset page: https://huggingface.co/datasets/pandora-s/openfood-classification.

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

Open Food Facts listet Informationen zu Kosmetika auf: Zutaten, Zusatzstoffe, Labels etc. Die Daten stammen überwiegend aus der Bürgererhebung (Crowdsourcing) von Informationen.