| --- |
| pipeline_tag: text-classification |
| language: |
| - de |
| - en |
| tags: |
| - food-classification |
| - openfoodfacts |
| - product-categorization |
| - class-mapping |
| --- |
| |
| # FoodCLS |
|
|
| **FoodCLS** is to map product category tags from [OpenFoodFacts](https://world.openfoodfacts.org/) to a set of predefined, user-friendly food categories. |
|
|
| The model uses the `categories_tags` attribute from the OpenFoodFacts product database to classify products into **12 broader food categories**. |
|
|
| This allows for simplified product categorization, easier data aggregation, and more intuitive overviews of food products. |
|
|
| ### Dataset |
|
|
| The model is based on the [OpenFoodFacts Product Database](https://huggingface.co/datasets/openfoodfacts/product-database), using the state from **November 9, 2026**. |
|
|
| ## Target Classes |
|
|
| The model predicts one of 12 predefined categories. Each category is assigned a numeric class ID. |
|
|
| | Class ID | German Category | English Equivalent | |
| |:---:|---|---| |
| | 0 | Obst & Gemüse | Fruits & Vegetables | |
| | 1 | Fleisch, Fisch & Alternativen | Meat, Fish & Alternatives | |
| | 2 | Milchprodukte & Eier | Dairy Products & Eggs | |
| | 3 | Brot & Backwaren | Bread & Bakery Products | |
| | 4 | Nudeln, Reis & Getreide | Pasta, Rice & Grains | |
| | 5 | Fertigprodukte | Ready-Made Meals & Convenience Foods | |
| | 6 | Backzutaten | Baking Ingredients | |
| | 7 | Getränke | Beverages | |
| | 8 | Snacks & Süßigkeiten | Snacks & Sweets | |
| | 9 | Öle, Gewürze & Saucen | Oils, Spices & Sauces | |
| | 10 | Brotaufstriche | Spreads | |
| | 11 | Sonstiges | Other | |
|
|
| ## Usage |
| ### 1. Grab files |
| First, download the `food_classifier.py` and `requirements.txt`. |
| If you want to use only local files (and not rely on internet connection in ordedr to load weights), ensure to download all files and explicitly setting the `local_file_only` parameter when calling the `from_pretrained()`method! |
| <br> |
|
|
| ### 2. Install prerequisites |
| Next, install the libraries required for running the model: |
| ``` |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 3. Enjoy usage |
| For example, like this: |
| ```python |
| from food_classifier import FoodClassifier |
| |
| |
| classifier = FoodClassifier.from_pretrained("emiloetting/FoodCLS") |
| |
| category = classifier.predict( |
| {"categories_tags": ["en:milks", "en:dairies"]}, |
| string_classes=True) |
| print(category) |
| ``` |
| When setting `string_classes`, the ouput will not be the integer class encoding but the respective string stored in the model's attribute `self.class_mapping` (a dictionary containing the integer-string mapping) |
|
|
| ## About |
| This model was made for use in a student project, aiming at providing an intuitive overview over food products. |
