Update README.md

README.md

@@ -23,13 +23,13 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 234663276.0
+    num_bytes: 234663276
     num_examples: 1000
   - name: test
-    num_bytes: 96022334.0
+    num_bytes: 96022334
     num_examples: 400
   download_size: 330223452
-  dataset_size: 330685610.0
+  dataset_size: 330685610
 configs:
 - config_name: default
   data_files:
@@ -37,4 +37,25 @@ configs:
     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.