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.

The Street View House Numbers (SVHN) Dataset

Estimated reading: 5 minutes

Visualization of SVHN Dataset in the Deep Lake UI

The Street View House Numbers (SVHN) Dataset

What is the SVHN Dataset?

The Street View House Number (SVHN) dataset has 60,0000 32 x 32 RGB images of printed digits (from 0 to 9) clipped from photographs of house number plates. The trimmed photos are centered on the digit of interest while surrounding digits and other distractions are retained. Photos of house numbers in various countries from Google Street View, as well as input from Amazon Mechanical Turk (AMT) framework (used to identify and transcribe the single digits) was used to create the SVHN dataset.

In addition to the training and testing sets, SVHN also provides an additional set that is less challenging and may be used to assist in the training process. In all, the SVHN dataset provides an unbiased real-world picture dataset for machine learning and object recognition with little data preparation and formatting needed.

Download SVHN Dataset in Python

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

Load SVHN Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/svhn-train')

Load SVHN Dataset Extra Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/svhn-extra')

Load SVHN Dataset Test Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/svhn-test')

SVHN Dataset Structure

SVHN Data Fields

image: a tensor containing 32×32 images
boxes: a tensor to draw character-level bounding boxes around the digits.
labels: an integer between 0 to 9 representing digits.

SVHN Data Splits

SVHN training split comprises 73257 digits
SVHN testing split comprises 26032 digits.
SVHN extra split comprises 531131 digits, these are comparatively less difficult samples, to use as extra training data.

How to use SVHN Dataset with PyTorch and TensorFlow in Python

Train a model on SVHN 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 SVHN dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about SVHN Dataset

Homepage: http://ufldl.stanford.edu/housenumbers/
Paper: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. (PDF)

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!

Citation Information

				
					title={SVHN: Reading Digits in Natural Images with Unsupervised Feature Learning}, 
author={Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng }, Workshop={NIPS Workshop on Deep Learning and Unsupervised Feature Learning }
year={2011}

SVHN Dataset FAQs

What is the SVHN dataset for Python?

The SVHN dataset is used for developing machine learning and object recognition algorithms with minimal requirements for data preprocessing and formatting. It contains real-world images has an order of magnitude more labeled data than the MNIST dataset. SVHN was created from house numbers in Google Street View images.

What is the SVHN dataset used for?

The Street View House Numbers (SVHN) dataset is used for creating machine learning and object recognition algorithms. It is a commonly used benchmark dataset as it needs minimal data preprocessing and formatting. MNIST‘s photos, for example, are cropped, but this dataset has an order of magnitude more labeled data (nearly 600,000 digit images) and is geared towards a far more difficult problem (recognizing digits and numbers in natural scene images).

How was the SVHN dataset generated?

The SVHN dataset was created by combining a large amount of Google Street View images by utilizing a combination of scripts and the Amazon Mechanical Turk, which helped localize, as well as transcribe the single digits. A large set of urban area house numbers from different countries was used in the sample.

How to download the SVHN dataset in Python?

With the open-source package Activeloop Deep Lake, you can load the SVHN dataset with one line of code using Python. See detailed instructions on loading the SVHN dataset training subset and the SVHN dataset testing subset in Python.

How can I use SVHN dataset in PyTorch or TensorFlow?

The open-source package Activeloop Deep Lake allows you to stream the SVHN dataset while training a model in TensorFlow or PyTorch with one line of code. See detailed instructions on how to train a model with PyTorch in Python or train a model on the SVHN dataset with TensorFlow in Python.