The MNIST database of handwritten digits.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/mnist-3.0.1.png" alt="Visualization" width="500px">

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/fashion_mnist-3.0.1.png" alt="Visualization" width="500px">

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

Moving variant of MNIST database of handwritten digits. This is the data used by the authors for reporting model performance. See tfds.video.moving_mnist.image_as_moving_sequence for generating training/validation data from the MNIST dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('moving_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.

Context

The MNIST dataset is one of the best known image classification problems out there, and a veritable classic of the field of machine learning. This dataset is more challenging version of the same root problem: classifying letters from images. This is a multiclass classification dataset of glyphs of English letters A - J.

This dataset is used extensively in the Udacity Deep Learning course, and is available in the Tensorflow Github repo (under Examples). I'm not aware of any license governing the use of this data, so I'm posting it here so that the community can use it with Kaggle kernels.

Content

notMNIST _large.zip is a large but dirty version of the dataset with 529,119 images, and notMNIST_small.zip is a small hand-cleaned version of the dataset, with 18726 images. The dataset was assembled by Yaroslav Bulatov, and can be obtained on his blog. According to this blog entry there is about a 6.5% label error rate on the large uncleaned dataset, and a 0.5% label error rate on the small hand-cleaned dataset.

The two files each containing 28x28 grayscale images of letters A - J, organized into directories by letter. notMNIST_large.zip contains 529,119 images and notMNIST_small.zip contains 18726 images.

Acknowledgements

Thanks to Yaroslav Bulatov for putting together the dataset.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cifar10', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cifar10-3.0.2.png" alt="Visualization" width="500px">

MedMNIST Pneumonia Dataset

The PneumoniaMNIST is based on a prior dataset of 5,856 pediatric chest X-Ray images. The task is binary-class classification of pneumonia against normal. The source training set is split with a ratio of 9:1 into training and validation set, and use its source validation set as the test set. The source images are gray-scale, and their sizes are (384–2,916) × (127–2,713). The images are center-cropped with a window size of length of the short edge and resized into 1 × 28 × 28.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('pneumonia_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/pneumonia_mnist-1.0.0.png" alt="Visualization" width="500px">