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README.md

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+---
+license: other
+task_categories:
+- image-classification
+- image-to-image
+tags:
+- face-recognition
+- age-estimation
+- agedb
+- imagefolder
+pretty_name: AgeDB HF-ready
+size_categories:
+- 10K<n<100K
+---
+
+# AgeDB HF-ready
+
+This folder packages the local AgeDB images as a Hugging Face `imagefolder` dataset.
+
+## Dataset Structure
+
+- `train/images/<shard>/`: AgeDB image files split into shard directories.
+- `train/metadata.csv`: per-image labels and metadata.
+
+The labels are derived from the AgeDB filename pattern:
+
+```text
+<image_id>_<identity>_<age>_<gender>.jpg
+```
+
+## Columns
+
+- `file_name`: relative image path used by Hugging Face `ImageFolder`, such as `images/000/example.jpg`.
+- `label`: numeric identity label.
+- `label_name`: identity name corresponding to `label`.
+- `identity`: normalized identity name.
+- `image_id`: numeric id from the original filename.
+- `age`: age annotation.
+- `age_decade`: decade bucket, such as `20s`.
+- `age_group`: broad age bucket: `child`, `teen`, `young_adult`, `adult`, or `senior`.
+- `gender`: original compact gender label, `f` or `m`.
+- `gender_label`: expanded gender label.
+- `source_filename`: original AgeDB filename.
+
+## Local Stats
+
+- Images: 16488
+- Identities: 567
+- Age range: 1-101
+- Female images: 6700
+- Male images: 9788
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("imagefolder", data_dir="data/evaluation/huggingface/agedb")
+train = dataset["train"]
+```
+
+## Notes
+
+AgeDB is described by its authors as an in-the-wild face dataset annotated with identity, age, and gender. Check the original dataset terms before publishing or redistributing it.