How to download a machine learning dataset for PyTorch and TensorFlow in Python

Without any need to download, a variety of popular machine learning datasets can be accessed and streamed with Hub with one line of code. This enables you to explore the datasets and train models without needing to download machine learning datasets regardless of their size. Access classical datasets like CIFAR-10, MNIST or Fashion-MNIST, as well as large datasets like Google Objectron, ImageNet, COCO, and many others in Python.

What is MNIST Dataset?

The MNIST (Modified National Institute of Standards and Technology database) dataset contains a training set of 60,000 images and a test set of 10,000 images of handwritten digits. The handwritten digit images have been size-normalized and centered in a fixed size of 28×28 pixels. The MNIST digits dataset is often used by data scientists who want to try machine learning techniques and pattern recognition methods on real-world data while spending minimal effort on preprocessing and formatting.

What is COCO Dataset?

The COCO (Common Objects in Context) dataset comprises 91 common object categories, 82 of which have more than 5,000 labeled examples. The dataset has 2.5 million labeled instances in 328k photos, created with the help of a large number of crowd workers using unique user interfaces for category detection, instance spotting, and instance segmentation. COCO dataset is a novel dataset with the purpose of improving the state-of-the-art in object detection by aligning the problem within the larger framework of scene understanding.

What is CIFAR 10 Dataset?

The CIFAR10 (Canadian Institute For Advanced Research) dataset consists of 10 classes with 6000 color images of 32×32 resolution for each class. It is divided into 50000 training and 10000 testing images. The test dataset contains exactly 1000 randomly collected images from each class. The training datasets may contain more images per class compared to others but they contain 5000 images per class on average.

What is CIFAR 100 Dataset?

The CIFAR100 (Canadian Institute For Advanced Research) dataset consists of 100 classes with 600 color images of 32×32 resolution for each class. It is divided into 500 training and 100 testing images per class. In CIFAR100, there are 20 superclasses sub-grouped into 100 classes. The dataset comes with two labels for each image such as a “fine” label (class) and a “coarse” label (superclass).

What is the MNIST dataset used for

MNIST is used as a “hello world” example by data scientists worldwide. Typically, MNIST dataset is used as a benchmark dataset, or as a proof-of-concept for training and testing purposes in the field of machine learning.

What is the MNIST dataset for Python?

The MNIST dataset (Modified National Institute of Standards and Technology database) is one of the most popular datasets in machine learning. MNIST is a dataset of 60,000 square 28×28 pixel images of handwritten single digits between 0 and 9. The images are in grayscale format.

How to download the MNIST dataset in Python?

You can load MNIST dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load MNIST dataset training subset or MNIST dataset testing subset in Python.

How to create an Image Dataset like MNIST dataset?

With Activeloop Hub, creating image datasets like the MNIST digits dataset is simple. Simple datasets like MNIST can be created automatically by allowing Hub parse the legacy files into Hub dataset format. More complex datasets can be created manually.

What is the size of each image in the MNIST dataset?

MNIST dataset image size is constant across all images of the dataset. Each MNIST dataset image is a fixed-size 28×28 pixel square image.

MNIST vs Fashion-MNIST. What is the difference between MNIST and Fashion-MNIST?

MNIST and Fashion-MNIST dataset are two separate datasets. However, the Fashion-MNIST dataset is meant to be an MNIST dataset alternative. Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. Both datasets are of the same size: 60 000 photos in the training set, as well as 10 000 pictures of clothing in the validation set of the dataset.

What does MNIST stand for?

MNIST is an acronym for the Modified National Institute of Standards and Technology dataset.

Why is MNIST so popular?

Created in 1998, MNIST dataset containing handwritten digits from 0-9 is small in size, simple in structure and easy to use. This makes it a good first dataset to try various pattern recognition methods on, with minimal effort required on data preprocessing..

Is MNIST suitable for deep learning?

Yes, MNIST data set is one of the most used datasets for deep learning. As of July 2022, MNIST only falls behind CIFAR-10, Imagenet, coco datasets in the amount of papers that cite them (or has used as a benchmark dataset.

LFW Dataset

Estimated reading: 4 minutes

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

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

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