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README.md

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+---
+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.

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