Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Extended MNIST - Python Package

Project description

EMNIST

Extended MNIST - Python Package

The EMNIST Dataset

The EMNIST Dataset is an extension to the original MNIST dataset to also include letters. For more details, see the EMNIST web page and the paper associated with its release:

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

The EMNIST Python Package

This package is a convenience wrapper around the EMNIST Dataset. The package provides functionality to automatically download and cache the dataset, and to load it as numpy arrays, minimizing the boilerplate necessary to make use of the dataset. (NOTE: The author of the Python package is not affiliated in any way with the authors of the dataset and the associated paper.)

Installation

To install the EMNIST Python package along with its dependencies, run the following command:

pip install emnist

The dataset itself is automatically downloaded and cached when needed. To preemptively download the data and avoid a delay later during the execution of your program, execute the following command after installation:

python -c "import emnist; emnist.ensure_cached_data()"

Alternately, if you have already downloaded the original IDX-formatted dataset from the EMNIST web page, copy or move it to ~/.cache/emnist/, where ~ is your home folder, and rename it from gzip.zip to emnist.zip. The package will use the existing file rather than downloading it again.

Usage

Usage of the EMNIST Python package is designed to be very simple.

To get a listing of the available subsets:

  >>> from emnist import list_datasets
  >>> list_datasets()
  ['balanced', 'byclass', 'bymerge', 'digits', 'letters', 'mnist']

(See the EMNIST web page for details on each of these subsets.)

To load the training samples for the 'digits' subset:

  >>> from emnist import extract_training_samples
  >>> images, labels = extract_training_samples('digits')
  >>> images.shape
  (240000, 28, 28)
  >>> labels.shape
  (240000,)

To load the test samples for the 'digits' subset:

  >>> from emnist import extract_test_samples
  >>> images, labels = extract_test_samples('digits')
  >>> images.shape
  (40000, 28, 28)
  >>> labels.shape
  (40000,)

Data is extracted directly from the downloaded compressed file to minimize disk usage, and is returned as standard numpy arrays.

Project details


Release history Release notifications

This version

0.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for emnist, version 0.0
Filename, size File type Python version Upload date Hashes
Filename, size emnist-0.0-py3-none-any.whl (7.3 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size emnist-0.0.tar.gz (6.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page