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: 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](https://github.com/openfoodfacts/openfoodfacts-ai/blob/dbbec40a3d964124cd7c8d838023be4a10d6c0be/front-image-classification/cli.py#L115).
The dataset was then manually reviewed and corrected. |