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Dark Face Dataset

610011203
Tagsurveillance
Taskobject detection
Release YearMade in 2019
Licenseunknown

Introduction #

Released 2019-03-15 ·Wenhan Yang, Ye Yuan, Wenqi Renet al.

Dark Face: Face Detection in Low Light Condition is an object detection dataset containing 6100 images with 50399 labeled instances of a single class - face. It offers real-world low-light images taken at night in various locations like teaching buildings, streets, bridges, and parks. These images are labeled with bounding boxes highlighting human faces, serving as crucial train and test sets for accurate face detection in low light environments. Designed for UG²+ Challenge Track 2

UG2+ Challenge Track 2 aims to evaluate and advance the robustness of object detection algorithms in specific poorvisibility situations, including challenging weather and lighting conditions.

In Sub-challenge 2.2, authors use the self-curated Dark Face dataset. It is composed of 10,000 images (6,000 for training and validation, and 4,000 for testing) taken in under-exposure condition where human faces are annotated by human with bounding boxes; and 9,000 images taken with the same equipment in the similar environment without human annotations.

dark-face_preview_1

Sub-challenge 2.2: Dark Face has a high degree of variability in scale, pose, occlusion, appearance and illumination. The face regions in the red boxes are zoomed-in for better viewing.

Additionally, authors provide a unique set of 789 paired lowlight/normal-light images captured in controllable real lighting conditions (but unnecessarily containing faces), which can be optionally used as parts of the training data. The training and evaluation set includes 43,849 annotated faces and the heldout test set includes 37,711 annotated faces.

Please Note, that test split consist of 100 images

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Dataset LinkHomepageDataset LinkResearch Paper 1 (main)Dataset LinkResearch Paper 2

Summary #

Dark Face: Face Detection in Low Light Condition is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 6100 images with 50399 labeled objects belonging to 1 single class (face).

Images in the Dark Face dataset have bounding box annotations. There are 100 (2% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (6000 images) and test (100 images). The dataset was released in 2019 by the Peking University.

Dataset Poster

Explore #

Dark Face dataset has 6100 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
OpenSample annotation mask from Dark FaceSample image from Dark Face
👀
Have a look at 6100 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
face
rectangle
6000
50399
8.4
0.41%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
face
rectangle
50399
0.05%
4.93%
0%
1px
0.14%
226px
31.39%
16px
2.23%
1px
0.09%
189px
17.5%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 50399 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 50399
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
face
rectangle
1128.png
720 x 1080
10px
1.39%
7px
0.65%
0.01%
2
face
rectangle
1128.png
720 x 1080
9px
1.25%
9px
0.83%
0.01%
3
face
rectangle
1128.png
720 x 1080
7px
0.97%
7px
0.65%
0.01%
4
face
rectangle
1128.png
720 x 1080
8px
1.11%
4px
0.37%
0%
5
face
rectangle
1128.png
720 x 1080
6px
0.83%
5px
0.46%
0%
6
face
rectangle
1128.png
720 x 1080
6px
0.83%
5px
0.46%
0%
7
face
rectangle
2217.png
720 x 1080
68px
9.44%
79px
7.31%
0.69%
8
face
rectangle
2217.png
720 x 1080
17px
2.36%
17px
1.57%
0.04%
9
face
rectangle
2217.png
720 x 1080
15px
2.08%
16px
1.48%
0.03%
10
face
rectangle
2217.png
720 x 1080
6px
0.83%
6px
0.56%
0%

License #

License is unknown for the Dark Face: Face Detection in Low Light Condition dataset.

Source

Citation #

If you make use of the Dark Face data, please cite the following reference:

@ARTICLE{poor_visibility_benchmark,
  author={Yang, Wenhan and Yuan, Ye and Ren, Wenqi and Liu, Jiaying and Scheirer, Walter J. and Wang, Zhangyang and Zhang, and et al.},
  journal={IEEE Transactions on Image Processing}, 
  title={Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study}, 
  year={2020},
  volume={29},
  number={},
  pages={5737-5752},
  doi={10.1109/TIP.2020.2981922}
}

@inproceedings{Chen2018Retinex,
  title={Deep Retinex Decomposition for Low-Light Enhancement},
  author={Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu},
  booktitle={British Machine Vision Conference},
  year={2018},
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-dark-face-dataset,
  title = { Visualization Tools for Dark Face Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/dark-face } },
  url = { https://datasetninja.com/dark-face },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Please visit dataset homepage to download the data.

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