Dark Face - Dataset Ninja

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.

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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Homepage

Research Paper 1 (main)

Research 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.

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.

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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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 50399

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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