---
license: cc-by-2.0
task_categories:
- image-classification
tags:
- face-recognition
- age-estimation
- gender-estimation
size_categories:
- 10k<n<100k
pretty_name: LAGENDA (LayerTeam Age and Gender Dataset)
---

# LAGENDA Dataset

> This is a community mirror of the **LAGENDA** dataset created by **LayerTeam**. It has been uploaded here for easier access and integration with the Hugging Face `datasets` library. 
>
> **All credit, rights, and accolades belong to the original authors.** Please see the citation section below.

## Dataset Description
**LAGENDA** (Large Age and Gender Dataset) is a dataset designed for age and gender recognition tasks. It addresses common biases in existing datasets by ensuring a near-perfect balance for all ages up to ~65 years.

*   **Original Creator:** LayerTeam
*   **Source:** [Original Project Page / GitHub](https://wildchlamydia.github.io/lagenda/)
*   **Total Images:** 67,159 (sourced from Open Images Dataset)
*   **Total Individuals:** 84,192
*   **Age Range:** 0 to 95 years

## Data Structure
The dataset includes images and an associated annotation structure (originally CSV) containing:
*   `img_name`: The identifier of the image.
*   `age`: Estimated age.
*   `gender`: Estimated gender.
*   `face_x0, face_y0, face_x1, face_y1`: Bounding box for the face.
*   `person_x0, person_y0, person_x1, person_y1`: Bounding box for the person.

*(Note: values of -1 indicate no ground truth answer for that specific field).*

## License
The dataset is released under the **CC BY 2.0** license.
*   You are free to share and adapt the material.
*   Attribution is required.

## Citation

```bibtex
@article{mivolo2023,
  Author = {Maksim Kuprashevich and Irina Tolstykh},
  Title = {MiVOLO: Multi-input Transformer for Age and Gender Estimation},
  Year = {2023},
  Eprint = {arXiv:2307.04616},
}

@article{mivolo2024,
  Author = {Maksim Kuprashevich and Grigorii Alekseenko and Irina Tolstykh},
  Title = {Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation},
  Year = {2024},
  Eprint = {arXiv:2403.02302},
}