---
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.