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.

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.

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.

EMNIST Dataset

Q: 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.

Estimated reading: 6 minutes

Visualization of the EMNIST Dataset training subset for category letters in the Deep Lake UI

EMNIST dataset

What is EMNIST Dataset?

The Extended-MNIST (EMNIST) dataset consists of 28×28 pixel images of handwritten character digits derived from the NIST Special Database 19. The dataset has a structure that directly matches the MNIST dataset and provides more challenging classification tasks involving letters and digits.

Download EMNIST Dataset in Python

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

Load EMNIST Dataset Training Subset for the category ByClass in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-byclass-train")

Load EMNIST Dataset Testing Subset for the category ByClass in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-byclass-test")

Load EMNIST Dataset Training Subset for the category ByMerge in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-bymerge-train")

Load EMNIST Dataset Testing Subset for the category ByMerge in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-bymerge-test")

Load EMNIST Dataset Training Subset for the category Balanced in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-balanced-train")

Load EMNIST Dataset Testing Subset for the category Balanced in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-balanced-test")

Load EMNIST Dataset Training Subset for the category Letters in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-letters-train")

Load EMNIST Dataset Testing Subset for the category Letters in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-letters-test")

Load EMNIST Dataset Training Subset for the category Digits in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-digits-train")

Load EMNIST Dataset Testing Subset for the category Digits in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-digits-test")

Load EMNIST Dataset Training Subset for the category MNIST in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-mnist-train")

Load EMNIST Dataset Testing Subset for the category MNIST in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-mnist-test")

EMNIST Dataset Structure

EMNIST Data Fields

images: tensor containing 28×28 images.
labels: tensor representing the class label.

EMNIST Data Splits

The EMNIST data training subset for class ByClass is composed of 697,932 images.
The EMNIST data testing subset for class ByClass is composed of 116,323 images.
The EMNIST data training subset for class Merge is composed of 697,932 images.
The EMNIST data testing subset for class Merge is composed of 116,323 images.
The EMNIST data training subset for class Balanced is composed of 112,800 images.
The EMNIST data testing subset for class Balanced is composed of 112,800 images.
The EMNIST data training subset for class Digits is composed of 240,000 images.
The EMNIST data testing subset for class Digits is composed of 40,000 images.
The EMNIST data training subset for class Letters is composed of 88,800 images.
The EMNIST data testing subset for class Letters is composed of 56,800 images.
The EMNIST data training subset for class MNIST is composed of 60,000 images.
The EMNIST data testing subset for class MNIST is composed of 10,000 images.

How to use EMNIST Dataset with PyTorch and TensorFlow in Python

Train a model on EMNIST 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 EMNIST dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

EMNIST Dataset Creation

Data Collection

The EMNIST Dataset consists of 28×28 pixel images of handwritten character digits collected from the NIST Special Database 19.

Additional Information about EMNIST Dataset

EMNIST Dataset Description

Homepage: https://www.nist.gov/itl/products-and-services/emnist-dataset
Paper: Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373
Point of Contact: emnist@nist.gov

EMNIST Dataset Curators

Cohen, G., Afshar, S., Tapson, J., & van Schaik, A.

EMNIST Dataset Citation Information

				
					Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373

EMNIST Dataset FAQs

What is the MNIST dataset for Python?

The Extended-MNIST (EMNIST) dataset has the same structure as the MNIST dataset and it consists of 28×28 pixel images of handwritten characters which were derived from the NIST Special Database 19. This dataset was created to provide more challenging classification tasks involving letters and digits in comparison to the MNIST dataset.

How to download the EMNIST dataset in Python?

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

How can I use EMNIST dataset in PyTorch or TensorFlow?

You can stream the EMNIST 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 EMNIST dataset with PyTorch or train a model on the EMNIST dataset with TensorFlow.

Should I work with EMNIST dataset in CSV?

No. CSV is not optimized for working with image data, especially for machine learning workflows. Instead of downloading the EMNIST dataset in CSV format, you can easily load, version-control, query, and manipulate EMNIST for machine learning purposes using Activeloop Deep Lake.

How to create an Image Dataset like EMNIST dataset?

With Activeloop Deep Lake, creating image datasets like the EMNIST dataset is easy. Simple datasets like EMNIST can be created automatically by allowing Deep Lake to parse the legacy files into Deep Lake dataset format. More complex datasets can be created manually.

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

EMNIST and Fashion-MNIST datasets are two separate datasets. Both datasets are inspired by, and meant to be alternatives to the original MNIST dataset, and thus contain 28×28 pixel images.

Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. EMNIST comprises of handwritten digits and letters from the NIST-Special Database 19.

EMNIST vs MNIST . What is the difference between EMNIST and MNIST ?

EMNIST and MNIST datasets are two separate datasets. Both the datasets contain 28×28 pixel handwritten images. The MNIST dataset comprises of only handwritten digits and it is quite easy to achieve high accuracies on it. The EMNIST dataset was created to provide more challenging classification tasks which involves both letters and digits in the dataset.

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

Each image in the EMNIST dataset is a 28×28 grayscale image.