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EMNIST Dataset
Load EMNIST dataset in Python fast with one line of code. Stream EMNIST dataset while training models in PyTorch & TensorFlow.
Visualization of the EMNIST Dataset training subset for category letters on the Activeloop Platform

What is EMNIST Dataset?

The Extended-MNIST (EMNIST) dataset consists of 28x28 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 open-source package Hub with just one line of code.

Load EMNIST Dataset Training Subset for the category ByClass in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-byclass-train")
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Load EMNIST Dataset Testing Subset for the category ByClass in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-byclass-test")
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Load EMNIST Dataset Training Subset for the category ByMerge in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-bymerge-train")
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Load EMNIST Dataset Testing Subset for the category ByMerge in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-bymerge-test")
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Load EMNIST Dataset Training Subset for the category Balanced in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-balanced-train")
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Load EMNIST Dataset Testing Subset for the category Balanced in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-balanced-test")
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Load EMNIST Dataset Training Subset for the category Letters in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-letters-train")
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Load EMNIST Dataset Testing Subset for the category Letters in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-letters-test")
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Load EMNIST Dataset Training Subset for the category Digits in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-digits-train")
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Load EMNIST Dataset Testing Subset for the category Digits in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-digits-test")
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Load EMNIST Dataset Training Subset for the category MNIST in Python

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import hub
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ds = hub.load("hub://activeloop/emnist-mnist-train")
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Load EMNIST Dataset Testing Subset for the category MNIST in Python

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

EMNIST Data Fields

  • images: tensor containing 28x28 images.
  • labels: tensor representing the class label.

EMNIST Data Splits

How to use EMNIST Dataset with PyTorch and TensorFlow in Python

Train a model on the EMNIST dataset with PyTorch

Let's use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:
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dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
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Train a model on the EMNIST dataset with TensorFlow

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dataloader = ds.tensorflow()
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EMNIST Dataset Creation

Data Collection
The EMNIST Dataset consists of 28x28 pixel images of handwritten character digits collected from the NIST Special Database 19.

Additional Information about KMNIST Dataset

EMNIST Dataset Description

EMNIST Dataset Curators

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

EMNIST Dataset Citation Information

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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
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EMNIST Dataset FAQs

What is the EMNIST dataset for Python?

The Extended-MNIST (EMNIST) dataset has the same structure as the MNIST dataset and it consists of 28x28 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 Hub. 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 the 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 Hub 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 Hub.

How to create an Image Dataset like EMNIST dataset?

With Activeloop Hub, creating image datasets like the EMNIST dataset is easy. Simple datasets like EMNIST can be created automatically by allowing Hub to parse the legacy files into Hub 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 28x28 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 28x28 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.
Hub community member Manas Gupta has contributed to this dataset documentation. You're awesome, Manas!
Last modified 27d ago