The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.

Note: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use tf.transpose within ds.map to convert the images to a human-friendlier format.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('emnist', 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/emnist-byclass-3.1.0.png" alt="Visualization" width="500px">

The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 (https://www.nist.gov/srd/nist-special-database-19) and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset (http://yann.lecun.com/exdb/mnist/). Further information on the dataset contents and conversion process can be found in the paper available at https://arxiv.org/abs/1702.05373v2

The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits.

The database is made available in original MNIST format and Matlab format.

This dataset is derived from the Leaf repository (https://github.com/TalwalkarLab/leaf) pre-processing of the Extended MNIST dataset, grouping examples by writer. Details about Leaf were published in "LEAF: A Benchmark for Federated Settings" https://arxiv.org/abs/1812.01097Note: This dataset does not include some additional preprocessing that MNIST includes, such as size-normalization and centering. In the Federated EMNIST data, the value of 1.0 corresponds to the background, and 0.0 corresponds to the color of the digits themselves; this is the inverse of some MNIST representations, e.g. in tensorflow_datasets, where 0 corresponds to the background color, and 255 represents the color of the digit.Data set sizes:only_digits=True: 3,383 users, 10 label classestrain: 341,873 examplestest: 40,832 examplesonly_digits=False: 3,400 users, 62 label classestrain: 671,585 examplestest: 77,483 examplesRather than holding out specific users, each user's examples are split across train and test so that all users have at least one example in train and one example in test. Writers that had less than 2 examples are excluded from the data set.The tf.data.Datasets returned by tff.simulation.datasets.ClientData.create_tf_dataset_for_client will yield collections.OrderedDict objects at each iteration, with the following keys and values, in lexicographic order by key:'label': a tf.Tensor with dtype=tf.int32 and shape [1], the class label of the corresponding pixels. Labels [0-9] correspond to the digits classes, labels [10-35] correspond to the uppercase classes (e.g., label 11 is 'B'), and labels [36-61] correspond to the lowercase classes (e.g., label 37 is 'b').'pixels': a tf.Tensor with dtype=tf.float32 and shape [28, 28], containing the pixels of the handwritten digit, with values in the range [0.0, 1.0].Argsonly_digits(Optional) whether to only include examples that are from the digits [0-9] classes. If False, includes lower and upper case characters, for a total of 62 class labels.cache_dir(Optional) directory to cache the downloaded file. If None, caches in Keras' default cache directory.ReturnsTuple of (train, test) where the tuple elements are tff.simulation.datasets.ClientData objects.

Emnist

## Overview

Emnist is a dataset for object detection tasks - it contains Let annotations for 7,183 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Олексій Чорний

Contents

EMNIST_yachay

## Overview

EMNIST_yachay is a dataset for classification tasks - it contains Vowels annotations for 400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).