AFW Dataset

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Visualization of the AFW dataset in the Deep Lake UI

AFW Dataset

What is AFW Dataset?

The AFW (Annotated Faces in the Wild) dataset, is made up of 205 images with 468 faces in total. Every face in the dataset is labeled with at most six landmarks with visibility labels, as well as a bounding box. AFW is often used for landmark estimation in real-world, cluttered images, face detection, and pose estimation.

Download AFW Dataset in Python

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

Load AFW Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/AFW")

AFW Dataset Structure

AFW Data Fields

image: tensor containing the image.
keypoints: tensor to identify various key points from the face

AFW Data Splits

The AFW dataset training set is composed of 337.

How to use AFW Dataset with PyTorch and TensorFlow in Python

Train a model on AFW 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 AFW dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about AFW Dataset

AFW Dataset Description

Homepage: https://ibug.doc.ic.ac.uk/resources
Paper: Introduced by Xiangxin Zhu et al. in Face detection, pose estimation, and landmark localization in the wild
Point of Contact: N/A

AFW Dataset Curators

Xiangxin Zhu; Deva Ramanan

AFW Dataset 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!

AFW Dataset Citation Information

				
					@inproceedings{,
  title = {Face detection, pose estimation, and landmark localization in the wild},
  author = {Xiangxin Zhu; Deva Ramanan},
  booktitle = {2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2012} 
}

AFW Dataset FAQs

What is the AFW dataset for Python?

The Annotated Faces in the Wild (AFW) dataset comprises 205 pictures with 468 faces in total. AFW is a popular standardized benchmark for automatic facial landmark localization and detection. The dataset helps with developing models that detect a set of predefined facial fiducial points.

What is the AFW dataset used for?

The AFW dataset is commonly used for training and testing models for face detection, pose estimation, and landmark estimation in real-world, cluttered images. It is a popular benchmark for automatic facial landmark localization and detection.

How can I use AFW dataset in PyTorch or TensorFlow?

You can stream the AFW 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 AFW dataset with PyTorch in Python or train a model on the AFW dataset with TensorFlow in Python.