IMDb-Face
2D Box
Classification
Face
|...
许可协议: Unknown

Overview

IMDb-Face is a new large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website. A detailed introduction of IMDb-Face can be found in the paper click here.

We hope that the IMDb-Face dataset could shed lights on the influences of data noise to the face recognition task, and point to potential labelling strategies to mitigate some of the problems. It could serve as a relatively clean data to facilitate future studies of noises in large-scale face recognition.

Citation

If you find IMDb-Face useful in your research, please cite:

@article{wang2018devil,
title={The Devil of Face Recognition is in the Noise},
author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian,
Chen and Loy, Chen Change},
journal={arXiv preprint arXiv:1807.11649},
year={2018}
}
数据概要
数据格式
Image,
数据量
1700K
文件大小
--
发布方
Sensetime
SenseTime is a leading global company focused on developing AI technologies that advance the world’s economies, society and humanity for a better tomorrow. It is also the world’s most-funded AI pure-play with the highest valuation.
数据集反馈
| 74 | 数据量 1700K | 大小 --
IMDb-Face
2D Box Classification
Face
许可协议: Unknown

Overview

IMDb-Face is a new large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website. A detailed introduction of IMDb-Face can be found in the paper click here.

We hope that the IMDb-Face dataset could shed lights on the influences of data noise to the face recognition task, and point to potential labelling strategies to mitigate some of the problems. It could serve as a relatively clean data to facilitate future studies of noises in large-scale face recognition.

Citation

If you find IMDb-Face useful in your research, please cite:

@article{wang2018devil,
title={The Devil of Face Recognition is in the Noise},
author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian,
Chen and Loy, Chen Change},
journal={arXiv preprint arXiv:1807.11649},
year={2018}
}
数据集反馈
0
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