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--- |
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dataset_info: |
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features: |
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- name: image_id |
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dtype: string |
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- name: image |
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dtype: image |
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- name: width |
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dtype: int64 |
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- name: height |
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dtype: int64 |
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- name: meta |
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struct: |
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- name: barcode |
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dtype: string |
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- name: off_image_id |
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dtype: string |
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- name: image_url |
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dtype: string |
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- name: category_id |
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dtype: int64 |
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|
- name: category_name |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 234663276 |
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num_examples: 1000 |
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- name: test |
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num_bytes: 96022334 |
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num_examples: 400 |
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download_size: 330223452 |
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dataset_size: 330685610 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: agpl-3.0 |
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task_categories: |
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- image-classification |
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tags: |
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- food |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Front image classification dataset |
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This dataset contains Open Food Facts images, each assigned with one of the two following classes: |
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- `front` (ID 0) |
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- `other` (ID 1) |
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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). |
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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. |
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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). |
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The dataset was then manually reviewed and corrected. |
