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WIDER Face Dataset

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Visualization of Wider face dataset in Deep Lake UI

WIDER Face dataset

What is WIDER Face Dataset?

The WIDER FACE dataset is a face detection benchmark dataset. The images in this dataset were selected from the publicly available WIDER dataset. The WIDER FACE dataset was organized based on 61 event classes. For each event class, data such as training, validation, and testing were randomly selected from the WIDER dataset. Similar to MALF and Caltech datasets, the WIDER FACE does not release the bounding box ground truth for the test images.

Download WIDER FACE Dataset in Python

Instead of downloading the WIDER FACE dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load WIDER Face Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/wider-face-train")

Load WIDER Face Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/wider-face-val")

Load WIDER Face Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/wider-face-test")

WIDER Face Dataset Structure

WIDER Face Data Fields
  • image: tensor containing the face image.
  • boxes: tensor representing bounding boxes.
  • poses: tensor to distinguish types of poses.
  • expressions: tensor to distinguish between ‘exaggerate_expression’ and ‘typical expression’.
  • illuminations: tensor to distinguish between ‘normal illumination’ and ‘extreme illumination’.
  • occlusions: tensor to distinguish ‘no occlusion’, ‘partial occlusion’, and ‘heavy occlusion’
  • validities: tensor checks if the image is valid or invalid.
  • blurs: tensor to distinguish ‘clear’, ‘normal blur’, and ‘heavy blur’ images.
WIDER Face Splits

How to use WIDER Face Dataset with PyTorch and TensorFlow in Python

Train a model on WIDER Face dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on WIDER Face dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()

Additional Information about WIDER Face Dataset

WIDER Face Dataset Description

WIDER Face Dataset Curators

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

WIDER Face Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

WIDER Face 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} }

WIDER Face Dataset FAQs

What is the WIDER Facedataset for Python?

The WIDER FACE dataset is commonly used as a Face Detection Benchmark. it contains 32,203 images and labels 393,703 faces with a high degree of variability in scale, poses, and occlusion. The database is split into training (40%), validation (10%), and testing (50%) sets. The images in the dataset are divided into three levels (easy, medium, and hard) according to the difficulties of image detection.

How to download the WIDER Face dataset in Python?

You can load the WIDER Face dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load the WIDER Face dataset training subset and testing subset in Python.

How can I use WIDER Face dataset in PyTorch or TensorFlow?

You can stream the WIDER Face dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the WIDER Face dataset with PyTorch in Python or train a model on the WIDER Face dataset with TensorFlow in Python.

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