Datasets:
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---
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: meta
struct:
- name: barcode
dtype: string
- name: off_image_id
dtype: string
- name: image_url
dtype: string
- name: objects
struct:
- name: bbox
sequence:
sequence: float32
- name: category_id
sequence: int64
- name: category_name
sequence: string
splits:
- name: val
num_bytes: 32285921
num_examples: 82
- name: train
num_bytes: 178448483
num_examples: 502
download_size: 352038777
dataset_size: 210734404
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
license: cc-by-sa-3.0
task_categories:
- object-detection
tags:
- food
size_categories:
- n<1K
---
# 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](https://world.openfoodfacts.org/data), 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 number.
- `image`: Image data.
- `width`: Image original width in pixels.
- `height`: Image original height in pixels.
- `meta`: Additional metadata.
- `barcode`: Product barcode.
- `off_image_id`: Open Food Facts image number.
- `image_url`: URL to the image on the Open Food Facts website.
- `objects`: Object detection annotations.
- `bbox`: List of bounding boxes in the format (y_min, x_min, y_max, x_max).
Coordinates are normalized between 0 and 1, using the top-left corner as the origin.
- `category_id`: List of category IDs.
- `category_name`: List of category names.
## Versions
- `1.0`: Original data used to train the [tf-nutriscore-1.0 model](https://github.com/openfoodfacts/robotoff-models/releases/tag/tf-nutriscore-1.0).
- `2.0`: New version of the dataset with improvements over the original version: the bounding boxes are more tightly cropped around the Nutri-score, some labeling errors were corrected, and images for which the model was failing were added to the dataset to improve future versions of the model. |