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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: train
num_bytes: 576037866.625
num_examples: 1083
- name: val
num_bytes: 64207578.0
num_examples: 123
download_size: 1304859691
dataset_size: 640245444.625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
# 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](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-nutrition-table-1.0 model](https://github.com/openfoodfacts/robotoff-models/releases/tag/tf-nutrition-table-1.0).
- `1.1`: Fixes erroneous bounding boxes due to rotated images.
About 10% of the bounding boxes were completely wrong due to the annotation software we were using.
All samples were manually reviewed again, and the erroneous bounding boxes were corrected.
Also, ~35 duplicated images were removed from the dataset (images that belong to the same product). |