graviti logo产品公开数据集关于我们
Demo演示登录
647
0
17
FDDB
概要
讨论
代码
活动
33c4889e-8ce2-11eb-b816-506b4b419b4c
505a83b·
Jun 28, 2021 12:40 AM
·1Commits

Overview

The Face Detection Data Set and Benchmark (FDDB) is a data set of face regions designed for studying the problem of unconstrained face detection. This data set contains the annotations for 5171 faces in a set of 2845 images taken from the Faces in the Wild data set.

Data Annotation

Face Detection Data Set and Benchmark
University of Massachusetts - Amherst

Face annotations

Uncompressing the "FDDB-folds.tgz" file creates a directory "FDDB-folds", which contains files with names: FDDB-fold-xx.txt and FDDB-fold-xx-ellipseList.txt, where xx = {01, 02, ..., 10} represents the fold-index. Each line in the FDDB-fold-xx.txt file specifies a path to an image in the above-mentioned data set. For instance, the entry 2002/07/19/big/img_130 corresponds to originalPics/2002/07/19/big/img_130.jpg.

The corresponding annotations are included in the file "FDDB-fold-xx-ellipseList.txt" in the following format:

<image name i>
<number of faces in this image =im>
<face i1>
<face i2>
...
<face im>

Here, each face is denoted by: <major_axis_radius minor_axis_radius angle center_x center_y 1>.

Detection output

To be recognized by the evaluation code, the detection output is expected in the following format:

<image name i>
<number of faces in this image =im>
<face i1>
<face i2>
...
<face im>

where the representation of a face depends on the specifics of the shape of the hypothesized image region. The evaluation code supports the following shapes:

  • Rectangular regions Each face region is represented as: <left_x top_y width height detection_score>

  • Elliptical regions Each face region is represented as: <major_axis_radius minor_axis_radius angle center_x center_y detection_score>.

Also, the order of images in the output file is expected to be the same as the order in the file annotatedList.txt.

Citation

Please cite as: Vidit Jain and Erik Learned-Miller. FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst. 2010.

BibTeX entry:

@TechReport{fddbTech,
  author = {Vidit Jain and Erik Learned-Miller},
  title =  {FDDB: A Benchmark for Face Detection in Unconstrained Settings},
  institution =  {University of Massachusetts, Amherst},
  year = {2010},
  number = {UM-CS-2010-009}
  }
🎉感谢Hello Dataset的贡献
数据集信息
应用场景暂无
标注类型暂无
任务类型暂无
LicenseUnknown
更新时间2020-12-31 17:25:21
数据概要
数据格式暂无
数据数量2.85K
已标注数量0
文件大小553KB
版权归属方
Vision Lab
标注方
未知
了解更多和支持
立即开始构建AI
免费开始联系我们