Datasets:
wider_face

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-nc-nd-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- extended\|other-wider
	task_categories:
	- object-detection
	task_ids:
	- face-detection
	paperswithcode_id: wider-face-1
	pretty_name: WIDER FACE
	dataset_info:
	features:
	- name: image
	dtype: image
	- name: faces
	sequence:
	- name: bbox
	sequence: float32
	length: 4
	- name: blur
	dtype:
	class_label:
	names:
	'0': clear
	'1': normal
	'2': heavy
	- name: expression
	dtype:
	class_label:
	names:
	'0': typical
	'1': exaggerate
	- name: illumination
	dtype:
	class_label:
	names:
	'0': normal
	'1': 'exaggerate '
	- name: occlusion
	dtype:
	class_label:
	names:
	'0': 'no'
	'1': partial
	'2': heavy
	- name: pose
	dtype:
	class_label:
	names:
	'0': typical
	'1': atypical
	- name: invalid
	dtype: bool
	splits:
	- name: train
	num_bytes: 12049881
	num_examples: 12880
	- name: test
	num_bytes: 3761103
	num_examples: 16097
	- name: validation
	num_bytes: 2998735
	num_examples: 3226
	download_size: 3676086479
	dataset_size: 18809719
	---

	# Dataset Card for WIDER FACE

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: http://shuoyang1213.me/WIDERFACE/index.html
	- Repository:
	- Paper: [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
	- Leaderboard: http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html
	- Point of Contact: shuoyang.1213@gmail.com

	### Dataset Summary

	WIDER FACE dataset is a face detection benchmark dataset, of which images are
	selected from the publicly available WIDER dataset. We choose 32,203 images and
	label 393,703 faces with a high degree of variability in scale, pose and
	occlusion as depicted in the sample images. WIDER FACE dataset is organized
	based on 61 event classes. For each event class, we randomly select 40%/10%/50%
	data as training, validation and testing sets. We adopt the same evaluation
	metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
	we do not release bounding box ground truth for the test images. Users are
	required to submit final prediction files, which we shall proceed to evaluate.

	### Supported Tasks and Leaderboards

	- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html).

	### Languages

	English

	## Dataset Structure

	### Data Instances

	A data point comprises an image and its face annotations.

	```
	{
	'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': {
	'bbox': [
	[178.0, 238.0, 55.0, 73.0],
	[248.0, 235.0, 59.0, 73.0],
	[363.0, 157.0, 59.0, 73.0],
	[468.0, 153.0, 53.0, 72.0],
	[629.0, 110.0, 56.0, 81.0],
	[745.0, 138.0, 55.0, 77.0]
	],
	'blur': [2, 2, 2, 2, 2, 2],
	'expression': [0, 0, 0, 0, 0, 0],
	'illumination': [0, 0, 0, 0, 0, 0],
	'occlusion': [1, 2, 1, 2, 1, 2],
	'pose': [0, 0, 0, 0, 0, 0],
	'invalid': [False, False, False, False, False, False]
	}
	}
	```

	### Data Fields

	- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`
	- `faces`: a dictionary of face attributes for the faces present on the image
	- `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
	- `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy`
	- `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1)
	- `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1)
	- `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2)
	- `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1)
	- `invalid`: whether the image is valid or invalid.

	### Data Splits

	The data is split into training, validation and testing set. WIDER FACE dataset is organized
	based on 61 event classes. For each event class, 40%/10%/50%
	data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images.

	## Dataset Creation

	### Curation Rationale

	The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters,
	making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
	with heavy occlusion, small scale, and atypical pose.

	### Source Data

	#### Initial Data Collection and Normalization

	WIDER FACE dataset is a subset of the WIDER dataset.
	The images in WIDER were collected in the following three steps: 1) Event categories
	were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images
	are retrieved using search engines like Google and Bing. For
	each category, 1000-3000 images were collected. 3) The
	data were cleaned by manually examining all the images
	and filtering out images without human face. Then, similar
	images in each event category were removed to ensure large
	diversity in face appearance. A total of 32203 images are
	eventually included in the WIDER FACE dataset.

	#### Who are the source language producers?

	The images are selected from publicly available WIDER dataset.

	### Annotations

	#### Annotation process

	The curators label the bounding boxes for all
	the recognizable faces in the WIDER FACE dataset. The
	bounding box is required to tightly contain the forehead,
	chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face
	which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating
	the face bounding boxes, they further annotate the following
	attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator
	and cross-checked by two different people.

	#### Who are the annotators?

	Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang.

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang

	### Licensing Information

	[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).

	### Citation Information

	```
	@inproceedings{yang2016wider,
	Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
	Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	Title = {WIDER FACE: A Face Detection Benchmark},
	Year = {2016}}
	```

	### Contributions

	Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.