Create README.md

README.md

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+---
+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},
+}