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MegaAge
2D Classification
Face
|...
许可协议: Research Only

Overview

that consists of 41,941 faces annotated with age posterior distributions.

MegaAge contains 41,941 images encompassing ages from 0 to 70. We reserve 8,530 images as test data. Each sample was labelled with age posterior as the ground-truth.

Citation

@inproceedings{huang2016unsupervised,
 author = {Yunxuan Zhang and Li Liu and Cheng Li and Chen Change Loy},
 title = {Quantifying Facial Age by Posterior of Age Comparisons},
 booktitle = {British Machine Vision Conference (BMVC)},
 year = {2017}}

License

You need to comply the following agreement to download or use the dataset:

  1. The MegaAge/MegaAge-Asian database is available for non-commercial research purposes only.
  2. All images of the MegaAge/MegaAge-Asian database are obtained from the Internet which are not property of MMLAB, The Chinese University of Hong Kong or SenseTime. The MMLAB and SenseTime are not responsible for the content nor the meaning of these images.
  3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  4. You agree not to further copy, publish or distribute any portion of the MegaAge database. Except, for internal use at a single site within the same organization it is allowed to make copies of the database.
  5. MMLAB and SenseTime reserve the right to terminate your access to the database at any time.
  6. All submitted papers or any publicly available text using the MegaAge/MegaAge-Asian database must cite the following paper:
    Yunxuan Zhang, Li Liu, Cheng Li, and Chen Change Loy. Quantifying Facial Age by Posterior of Age Comparisons, In British Machine Vision Conference (BMVC), 2017.
数据概要
数据格式
image,
数据量
41.941K
文件大小
220.28MB
发布方
CUHK Multimedia Lab (MMLab)
The CUHK Multimedia Lab (MMLab) is one of the pioneering institutes on deep learning. In GPU Technology Conference (GTC) 2016, a world-wide technology summit, our lab is recognized as one of the top ten AI pioneers, and listed together with top research groups in the world (e.g. MIT, Stanford, Berkeley, and Univ. of Toronto). Today, we remain one of the most active research labs in computer vision and deep learning, publishing over 40 papers on top conferences (CVPR/ICCV/ECCV/NIPS) every year.
| 数据量 41.941K | 大小 220.28MB
MegaAge
2D Classification
Face
许可协议: Research Only

Overview

that consists of 41,941 faces annotated with age posterior distributions.

MegaAge contains 41,941 images encompassing ages from 0 to 70. We reserve 8,530 images as test data. Each sample was labelled with age posterior as the ground-truth.

Citation

@inproceedings{huang2016unsupervised,
 author = {Yunxuan Zhang and Li Liu and Cheng Li and Chen Change Loy},
 title = {Quantifying Facial Age by Posterior of Age Comparisons},
 booktitle = {British Machine Vision Conference (BMVC)},
 year = {2017}}

License

You need to comply the following agreement to download or use the dataset:

  1. The MegaAge/MegaAge-Asian database is available for non-commercial research purposes only.
  2. All images of the MegaAge/MegaAge-Asian database are obtained from the Internet which are not property of MMLAB, The Chinese University of Hong Kong or SenseTime. The MMLAB and SenseTime are not responsible for the content nor the meaning of these images.
  3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  4. You agree not to further copy, publish or distribute any portion of the MegaAge database. Except, for internal use at a single site within the same organization it is allowed to make copies of the database.
  5. MMLAB and SenseTime reserve the right to terminate your access to the database at any time.
  6. All submitted papers or any publicly available text using the MegaAge/MegaAge-Asian database must cite the following paper:
    Yunxuan Zhang, Li Liu, Cheng Li, and Chen Change Loy. Quantifying Facial Age by Posterior of Age Comparisons, In British Machine Vision Conference (BMVC), 2017.
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