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image | age
int64 | gender
int64 | race
int64 |
---|---|---|---|
100 | 1 | 0 |
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100 | 1 | 2 |
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100 | 1 | 2 |
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101 | 1 | 2 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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10 | 0 | 0 |
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in Data Studio
UTKFace Demographics V1 Dataset
About
The UTKFace Demographics V1 dataset contains over 10,000+ facial images labeled with:
- Age (0 to 116 years)
- Gender (0 = Male, 1 = Female)
- Race (0 = White, 1 = Black, 2 = Asian, 3 = Indian, 4 = Others)
All faces are aligned and cropped to 200x200 resolution, collected in uncontrolled conditions from the wild.
📦 This version restructures the original UTKFace dataset for seamless streaming, preview, and filtering on the Hugging Face Hub.
License
This dataset is distributed for research and educational use only. Please cite the original paper if you use this data.
More details and conditions: https://susanqq.github.io/UTKFace/
Citation
@inproceedings{zhifei2017cvpr,
title={Age Progression/Regression by Conditional Adversarial Autoencoder},
author={Zhang, Zhifei, Song, Yang, and Qi, Hairong},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2017},
organization={IEEE}
}
Acknowledgements
Original authors: Zhifei Zhang, Yang Song, and Hairong Qi Project page: https://susanqq.github.io/UTKFace/
I've curated and restructured this version.
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