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LFW Dataset

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Visualization of the LFW Dataset in the Deep Lake UI

LFW Dataset

What is LFW Dataset?

LFW (Labeled Faces in the Wild) dataset is a face photo database developed to explore the problem of unrestricted face recognition. LFW was released for research purposes to make advancements in face verification, not to conduct a comprehensive review of commercial algorithms prior to release. The database is an initial attempt to provide a set of categorized faces covering a range of circumstances that people commonly encounter in their daily lives. The database displays “normal” diversity in poses, lighting, focus, accuracy, facial expressions, age, gender, ethnicity, accessories, makeup, occlusions, background, and photographic quality. Despite this discrepancy, the images in the database are presented in a simple and consistent format for maximum usability.

Download LFW Dataset in Python

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

Load LFW Dataset in Python 

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

LFW Dataset Structure

LFW 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 Dataset with PyTorch and TensorFlow in Python

Train a model on LFW Dataset with PyTorch in Python 
				
					dataloader = ds.pytorch(num_workers = 0, batch_size= 4, shuffle = False)
Train a model on LFW Dataset with TensorFlow in Python 
				
					dataloader = ds.tensorflow()

LFW Dataset Creation

Curation Rationale
Labeled Faces in the Wild has been released with the main goal of solving the unseen pair matching problem of face recognition, precisely aligning detection alignment recognition pipeline, and accurate and easy comparison of face recognition algorithms.
Data Collection and Normalization Information
This database, there are over 13,000 face images collected from the Internet in this database. Each face was signed with the name of the person depicted. 1680 of the people pictured have two or more different photos in the dataset.

Additional Information about LFW Dataset

LFW Dataset Description

LFW Dataset Curators
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller
LFW 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 Dataset Citation Information 
				
					@TechReport{LFWTech, 
author = {Gary B. Huang and Manu Ramesh and Tamara Berg and Erik Learned-Miller}, 
title = {Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments}, 
institution = {University of Massachusetts, Amherst}, 
year = 2007,
 number = {07-49}, month = {October}
}

LFW Dataset FAQs

What is the LFW dataset for Python?

The Labeled Faces in the Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition. The database attempts to provide a set of categorized faces covering a range of circumstances that people commonly encounter in their daily lives.

What is the LFW dataset used for?
Labeled Faces in the Wild is used as a public benchmark for face verification, also known as pair matching. The dataset was released for research purposes to make advancements in face verification.
How to download the LFW dataset in Python?

You can load the LFW dataset with one line of code using the open-source package Activeloop Deep Lake. See detailed instructions on how to load the LFW dataset in Python.

How can I use the LFW dataset in PyTorch or TensorFlow?

You can stream the LFW 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 LFW dataset with PyTorch in Python and how to train a model on the LFW dataset with TensorFlow in Python.

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