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Adience Dataset

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Visualization of Adience dataset in the Deep Lake UI

Adience Dataset

What is Adience Dataset?

The Adience Dataset was created to aid the study of age and gender recognition and can be also used as a benchmark dataset for face photos. The dataset is as close as it can get to real-world face imaging conditions. The dataset contains 26,580 photos with 8 age groups(labels) taken by 2,284 subjects on iPhone 5 (or later) and the images set for Flicker albums. The dataset has a diverse collection of faces having variations in poses, appearance, noise, lighting, and more.

Downloading Adience Dataset in Python

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

Load Adience Fold Faces Dataset Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/adience')

Adience Dataset Structure

Adience Data Fields
Data Fields
  • images: tensor containing the image
  • ages: tensor containing ages (label) of a corresponding image
  • gender: tensor containing the gender of each image
  • x: part of the bounding box of the face in the original Flickr image
  • y: part of the bounding box of the face in the original Flickr image
  • dx: part of the bounding box of the face in the original Flickr image
  • dy: part of the bounding box of the face in the original Flickr image
  • tilt_ang: pose of the face in the original Flickr image
  • fiducial_yaw_angle: pose of the face in the original Flickr image
  • fiducial_score: score of the landmark detector
Adience Data Splits

How to use Adience Dataset with PyTorch and TensorFlow in Python

Train a model on Adience 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 Adience dataset with TensorFlow in Python 
				
					dataloader = ds.tensorflow()

Additional Information about Adience Dataset

Adience Dataset Description

Adience Dataset Curators
E. Eidinger, R. Enbar, and T. Hassner
Adience 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!
Adience Dataset Citation Information 
				
					@article{eidinger2014age,
  title={Age and gender estimation of unfiltered faces},
  author={Eidinger, Eran and Enbar, Roee and Hassner, Tal},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={9},
  number={12},
  pages={2170--2179},
  year={2014},
  publisher={IEEE}
}

Adience Dataset FAQs

What is the Adience dataset for Python?

The Adience Dataset was created to aid the study of age and gender recognition and can be also used as a benchmark dataset for face photos. The dataset is as close as it can get to real-world face imaging conditions.

How can I use the Adience dataset in PyTorch or TensorFlow?
You can stream the Adience 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 Adience dataset with PyTorch in Python or train a model on the Adience dataset with TensorFlow in Python.
 
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