mnists 0.1.0

MNISTs: All MNIST-like datasets in one package

MNISTs: All MNIST-like datasets in one package

MNISTs provides an easy way to use MNIST and other MNIST-like datasets (e.g. FashionMNIST, KMNIST) in your numpy code.

MNISTs replicates the functionality of torchvision.datasets.mnist without the need to download dozens of dependencies. MNISTs has only one dependency - numpy.

Usage

Each dataset stores train/test images as numpy arrays of shape (n_samples, img_height, img_width) and train/test labels as numpy arrays of shape (n_samples,).

MNIST example:

>>> from mnists import MNIST
>>> mnist = MNIST()
>>> type(mnist.train_images)
<class 'numpy.ndarray'>
>>> mnist.train_images.dtype
dtype('uint8')
>>> mnist.train_images.min()
0
>>> mnist.train_images.max()
255
>>> mnist.train_images.shape
(60000, 28, 28)
>>> mnist.train_labels.shape
(60000,)
>>> mnist.test_images.shape
(10000, 28, 28)
>>> mnist.test_labels.shape
(10000,)
>>> mnist.classes[:3]
['0 - zero', '1 - one', '2 - two']

FashionMNIST example:

from mnists import FashionMNIST
import matplotlib.pyplot as plt

fmnist = FashionMNIST()
plt.imshow(fmnist.train_images[0], cmap='gray')
plt.title(fmnist.classes[fmnist.train_labels[0]])
plt.axis('off')
plt.show()

KMNIST example:

from mnists import KMNIST
import matplotlib.pyplot as plt

kmnist = KMNIST()
plt.imshow(
    kmnist.test_images[:256]
        .reshape(16, 16, 28, 28)
        .swapaxes(1, 2)
        .reshape(16 * 28, -1),
    cmap='gray')
plt.axis('off')
plt.show()

Installation

Install mnists from PyPi:

pip install mnists

or from source:

pip install -U git+https://github.com/pczarnik/mnists

The only requirements for MNISTs are numpy>=1.22 and python>=3.9.

Acknowledgments

The main inspirations for MNISTs were mnist and torchvision.datasets.mnist.