Metadata-Version: 2.1
Name: emnist
Version: 0.0
Summary: Extended MNIST - Python Package
Home-page: https://github.com/hosford42/EMNIST
Author: Aaron Hosford
Author-email: hosford42@gmail.com
License: UNKNOWN
Project-URL: The EMNIST Dataset, https://www.nist.gov/itl/iad/image-group/emnist-dataset
Project-URL: The EMNIST Paper, https://arxiv.org/abs/1702.05373v1
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](https://www.nist.gov/itl/iad/image-group/emnist-dataset) and the 
        [paper](http://arxiv.org/abs/1702.05373) 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:
        
        ```python
          >>> from emnist import list_datasets
          >>> list_datasets()
          ['balanced', 'byclass', 'bymerge', 'digits', 'letters', 'mnist']
        ```
        
        (See the [EMNIST web page](https://www.nist.gov/itl/iad/image-group/emnist-dataset) for details on each of 
        these subsets.)
        
        To load the training samples for the 'digits' subset:
        
        ```python
          >>> 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:
        
        ```python
          >>> 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.
        
Keywords: MNIST EMNIST image recognition data dataset numpy idx neural networkmachine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.0
Description-Content-Type: text/markdown
Provides-Extra: inspect
