LFW Deep Funneled Dataset

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Visualization of the LFW Deep Funneled Dataset in the Deep Lake UI.

LFW Deep Funneled Dataset

What is LFW Deep Funneled Dataset?

LFW Deep Funneled Dataset is a face photo database developed to explore the problem of unlimited face recognition. LFW Deep Funneled Dataset was released for research purposes to make advancements in face verification, not to conduct a comprehensive review of commercial algorithms prior to release. In comparison with the images in the LFW dataset, the deep funneled images produce excellent results for most face verification algorithms.

Download LFW Deep Funneled Dataset in Python

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

Load LFW Deep Funneled Dataset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/lfw-deep-funneled')

LFW Deep Funneled Dataset Structure

LFW Deep Funneled Data Fields

images: tensor containing images of the people.
names: tensor containing the names of the people depicted in the images.

How to use LFW Deep Funneled Dataset with PyTorch and TensorFlow in Python

Train a model on LFW Deep Funneled 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= batch_size, shuffle = False)

Train a model on LFW Deep Funneled Dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

LFW Deep Funneled Dataset Creation

Data Collection and Normalization Information

In this database, there are over 13,000 face images collected from the Internet. Each face was signed with the name of the person depicted in the image. 1680 of the people pictured have two or more different photos in the dataset.

Additional Information about LFW Deep Funneled Dataset

LFW Deep Funneled Dataset Description

Homepage: http://vis-www.cs.umass.edu/lfw/
Paper: http://vis-www.cs.umass.edu/papers/nips2012_deep_congealing.pdf
Point of Contact: Gary Huang

LFW Deep Funneled Dataset Curators

Gary B. Huang, Marwan Mattar, Honglak Lee, and Erik Learned-Miller

LFW Deep Funneled 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!

LFW Deep Funneled Dataset Citation Information

				
					@InProceedings{Huang2012a,
author = {Gary B. Huang and Marwan Mattar and Honglak Lee and Erik Learned-Miller}, 
title = {Learning to Align from Scratch}, 
booktitle = {NIPS}, 
year = {2012}
}