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">

This is the Keras MNIST.npz datasets downloaded from Amazonaws img-datasets (amazonaws mnist.npz download link (11 MB)).
A 3D tensor of 8-bit integers, which is an array of 60,000 matrices of 28x28 integers representing possible handwritten digits from 0 to 9.
Feel free to use.

Dataset

This dataset was created by muddassir ali rana

Contents

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.

Dataset

This dataset was created by AnilKumarPallekonda

Released under CC0: Public Domain

Contents

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">

Dataset

This dataset was created by Rafael dos Santos

Contents

Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format. Since MNIST restricts us to 10 classes, we chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('kmnist', 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/kmnist-3.0.1.png" alt="Visualization" width="500px">

Dataset

This dataset was created by João Felipe

Contents

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cats_vs_dogs', 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/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F950187%2Fd8a0b40fa9a5ad45c65e703b28d4a504%2Fbackground.png?generation=1703873571061442&alt=media" alt="">

The process for collecting this dataset was documented in paper "https://doi.org/10.12913/22998624/122567">"Development of Extensive Polish Handwritten Characters Database for Text Recognition Research" by Mikhail Tokovarov, dr Monika Kaczorowska and dr Marek Miłosz. Link to download the original dataset: https://cs.pollub.pl/phcd/. The source fileset also contains a dataset of raw images of whole sentences written in Polish.

Context

PHCD (Polish Handwritten Characters Database) is a collection of handwritten texts in Polish. It was created by researchers at Lublin University of Technology for the purpose of offline handwritten text recognition. The database contains more than 530 000 images of handwritten characters. Each image is a 32x32 pixel grayscale image representing one of 89 classes (10 digits, 26 lowercase latin letters, 26 uppercase latin letters, 9 lowercase polish letters, 9 uppercase polish letters and 9 special characters), with around 6 000 examples per class.

How to use

This notebook contains a PyTorch example of how to load the dataset from .npz files and train a CNN model. You can also use the dataset with other frameworks, such as TensorFlow, Keras, etc.

For .npz files, use numpy.load method.

Contents

The dataset contains the following:

• dataset.npz - a file with two compressed numpy arrays:
  • "signs" - with all the images, sized 32 x 32 (grayscale)
  • "labels" - with all the labels (0-88) for examples from signs
• label_mapping.csv - a csv file with columns label and char, mapping from ids to characters from dataset
• images - folder with original 530 000 png images, sized 32 x 32, to use with other loading techniques

Acknowledgements

I want to express my gratitude to the following people: Dr. Edyta Łukasik for introducing me to this dataset and to authors of this dataset - Mikhail Tokovarov, dr. Monika Kaczorowska and dr. Marek Miłosz from Lublin University of Technology in Poland.

Inspiration

You can use this data the same way you used MNIST, KMNIST of Fashion MNIST: refine your image classification skills, use GPU & TPU to implement CNN architectures for models to perform such multiclass classifications.