Datasets:

openfoodfacts
/

front_image_classification

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: category_id
      dtype: int64
    - name: category_name
      dtype: string
  splits:
    - name: train
      num_bytes: 234663276
      num_examples: 1000
    - name: test
      num_bytes: 96022334
      num_examples: 400
  download_size: 330223452
  dataset_size: 330685610
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: agpl-3.0
task_categories:
  - image-classification
tags:
  - food
size_categories:
  - 1K<n<10K

Front image classification dataset

This dataset contains Open Food Facts images, each assigned with one of the two following classes:

front (ID 0)
other (ID 1)

Front images are the "default" image of a product, displayed on Open Food Facts product page. A front image is most of the time a photo of the front side of the product packaging. It's useful to be able to detect front images so that we can update the front image with a newer version (when the packaging changes for example).

Random images were fetched from Open Food Facts using the Parquet export, and pre-annotated with their class, depending on whether the image was selected as a front image or not. The CLI command used to generate the pre-annotated dataset can be found here.

The dataset was then manually reviewed and corrected.