Datasets:
Tasks:
Object Detection
Sub-tasks:
face-detection
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
| 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. |