Select Language

AI社区

公开数据集

深度卷积网络-人脸点检测数据集

深度卷积网络-人脸点检测数据集

217.9M
915 浏览
3 喜欢
14 次下载
0 条讨论
Face 2D Keypoints

This web page provides the executable files and datasets of our CVPR 2013 paper, so that researchers can repeat our expe......

数据结构 ? 217.9M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    This web page provides the executable files and datasets of our CVPR 2013 paper, so that researchers can repeat      our experiments or test our facial point detector on other datasets. The code and datasets are for research purposes only. If you      use our code or datasets, please cite the paper. The material provided on this web page is subject to change.    

    Training set:

    It contains 5,590 LFW images and 7,876 other images downloaded from the web. The training set and validation set    are defined in trainImageList.txt and testImageList.txt, respectively. Each line of these text files starts with the image name,    followed by the boundary positions of the face bounding box retured by our face detector, then followed by the positions of the five    facial points.
    Testing set:

    It contains the 1,521 BioID images, 781 LFPW training images, and 249 LFPW test images used in our testing, together    with the text files recording the boundary positions of the face bounding box retured by our face detector for each dataset. A few    images that our face detector failed are not listed in the text files. LFPW images are renamed for the convenience of processing.

    Comparison with other methods

    Comparison results:

    The numerical results corresponding to Figure 6, 7 in Section 5.2 in our paper are provided.    Please see the readme.txt in the downloaded package.


    Reference

    [1] Y. Sun, X. Wang, and X. Tang. Deep Convolutional Network Cascade for Facial Point Detection. In Proceedings
          of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.    [PDF]


    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:10 去赚积分?
    • 915浏览
    • 14下载
    • 3点赞
    • 收藏
    • 分享