| --- |
| language: |
| - tr |
| - en |
| license: mit |
| task_categories: |
| - image-classification |
| - feature-extraction |
| - zero-shot-image-classification |
| tags: |
| - food |
| - nutrition |
| - vision |
| - calorie-tracking |
| - turkey |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Turkish Food & Nutrition Vision Dataset (Food v2) |
|
|
| High-resolution, human-POV Turkish food and packaged snack dataset with canonical nutrition macros and portion sizes. |
|
|
| ## 📊 Dataset Summary |
| - **Repository**: `ozertuu/foodv2` |
| - **Canonical Foods**: 50 |
| - **Images Total**: 150 |
| - **Average Images / Food**: ~3.0 |
| - **Image Sources**: Yemeksepeti / DeliveryHero, GetirYemek, Migros Sanalmarket, Getir, Nefis Yemek Tarifleri, OpenFoodFacts. |
|
|
| ## 🍽️ Features & Schema |
| - `image`: PIL Image / Raw JPEG bytes |
| - `food_id`: Unique canonical food UUID |
| - `name_tr`: Turkish food name |
| - `name_en`: English translation |
| - `category`: Macro food category (`proteins`, `sweets`, `vegetables`, `fruits`, `grains`, etc.) |
| - `calories_100g`, `protein_100g`, `carbs_100g`, `fat_100g`: Canonical macro nutrition per 100g |
| - `default_serving`, `default_serving_grams`: Standard reference portion |
| - `source_url`, `source_domain`: Source origin |
| - `sha256`, `phash`: Deduplication hashes |
|
|
| ## 💻 Quickstart in Kaggle / Python |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load entire dataset or stream |
| dataset = load_dataset("ozertuu/foodv2", split="train") |
| |
| # Inspect first item |
| item = dataset[0] |
| print(item["name_tr"], item["calories_100g"], "kcal") |
| item["image"].show() |
| ``` |
|
|
