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">

Author: Yann LeCun, Corinna Cortes, Christopher J.C. Burges
Source: MNIST Website - Date unknown
Please cite:

The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples

It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. 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.

With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.

The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.

MNIST Dataset

Introduction

The MNIST (Modified National Institute of Standards and Technology) dataset is a widely used dataset for training and testing image processing systems. It consists of 70,000 images of handwritten digits (0-9), split into a training set of 60,000 images and a test set of 10,000 images. Each image is 28x28 pixels in size.

Description

The MNIST dataset includes:

• Training Images: 60,000 images of handwritten digits.
• Training Labels: Corresponding labels for the training images.
• Test Images: 10,000 images of handwritten digits.
• Test Labels: Corresponding labels for the test images.

Each image is grayscale and has been size-normalized and centered in a fixed-size image.

How to Use

First, load the dataset files:

import numpy as np

train_val_images = 'train_images.npy' # Train 80%, Validation 20%
train_val_labels = 'train_labels.npy' # Train 80%, Validation 20%
test_images = 'test_images.npy'
test_labels = 'test_labels.npy'

train_val_images = np.load(train_val_images)
train_val_labels = np.load(train_val_labels)

Split the dataset into training, validation, and test sets:

# 90% of the training data for training, 10% for validation
train_images = train_val_images[:int(train_val_images.shape[0] * 0.9)]
train_labels = train_val_labels[:int(train_val_labels.shape[0] * 0.9)]

val_images = train_val_images[int(train_val_images.shape[0] * 0.9):]
val_labels = train_val_labels[int(train_val_labels.shape[0] * 0.9):]

test_images = np.load(test_images)
test_labels = np.load(test_labels)

Now, you can use these splits for your machine learning model training and evaluation.

Acknowledgement

The MNIST dataset was created by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges. It is widely used for benchmarking image processing systems and is publicly available for academic and research purposes. Special thanks to the creators for making this dataset available to the research community.

Reduced MNIST is a streamlined version of the classic handwritten digits dataset designed for image classification, benchmarking, and rapid prototyping of computer vision and machine learning models.

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.

1) Data Introduction • The MNIST Dataset is a widely used benchmark for handwritten digit recognition, containing images of handwritten digits from 0 to 9.

2) Data Utilization (1) Characteristics of the MNIST Dataset: • The dataset consists of grayscale images representing digits, collected from a diverse population, making it ideal for evaluating machine learning algorithms on image classification tasks. • It provides a standardized and easily accessible resource for comparing the performance of various classification models.

(2) Applications of the MNIST Dataset: • Handwritten digit recognition model development: The MNIST dataset is commonly used for training and testing deep learning and machine learning models in tasks such as digit recognition, algorithm benchmarking, and educational demonstrations.

MNIST is a picture data set of handwritten numbers, which was organized by the National Institute of Standards and Technology (NIST) of the United States. A total of 250 handwritten digital pictures were collected, 50% of which were high school students and 50% were from the staff of the Census Bureau. The collection purpose of this data set is to realize the recognition of handwritten digits through algorithms. The data set contains 60000 images and labels, while the test set contains 10000 images and labels. The first 5000 training sets from the initial NIST program, The last 5000 test sets from the original NIST program. The first 5000 are more regular than the last 5000, because the first 5000 data come from the employees of the US Census Bureau, and the last 5000 data come from college students.

MNIST dataset originally hosted on https://deeplearning.net, re-hosted here because deeplearning.net is currently inaccessible.

Author: Han Xiao, Kashif Rasul, Roland Vollgraf
Source: Zalando Research
Please cite: Han Xiao and Kashif Rasul and Roland Vollgraf, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, arXiv, cs.LG/1708.07747

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. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Raw data available at: https://github.com/zalandoresearch/fashion-mnist

Target classes

Each training and test example is assigned to one of the following labels: Label Description
0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot

The MNIST dataset (Modified National Institute of Standards and Technology) is a widely used benchmark in machine learning and computer vision, consisting of 70,000 grayscale images of handwritten digits from 0 to 9, each sized 28×28 pixels. It is split into 60,000 training images and 10,000 test images, with labels indicating the correct digit for each image. MNIST is popular because it is simple, preprocessed, and standardized, making it ideal for training and testing algorithms, comparing models, and learning about digit recognition. Its consistent format and moderate size allow beginners and researchers alike to experiment with machine learning techniques efficiently.

🖼️ MNIST (Extracted from PyTorch Vision)

MNIST is a classic dataset of handwritten digits, widely used for image classification tasks in machine learning.

  ℹ️ Dataset Details





  📖 Dataset Description

The MNIST database of handwritten digits is a commonly used benchmark dataset in machine learning. It consists of 70,000 grayscale images of handwritten digits (0-9), each with a size of 28x28 pixels. The dataset is split into 60,000 training images and 10,000… See the full description on the dataset page: https://huggingface.co/datasets/p2pfl/MNIST.

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.

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. 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. With some classification methods (particuarly template-based methods, such as SVM and K-nearest neighbors),

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. 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. With some classification methods (particuarly template-based methods, such as SVM and K-nearest neighbors),

Dataset Card for "AudioMNIST"

The audioMNIST dataset has 50 English recordings per digit (0-9) of 60 speakers. There are 60 participants in total, with 12 being women and 48 being men, all featuring a diverse range of accents and country of origin. Their ages vary from 22 to 61 years old. This is a great dataset to explore a simple audio classification problem: either the digit or the gender.

  Bias, Risks, and Limitations

The genders represented in the dataset are… See the full description on the dataset page: https://huggingface.co/datasets/gilkeyio/AudioMNIST.

MNIST Dataset

This dataset contains MNIST handwritten digit images.

  Dataset Description

The classic MNIST dataset containing images of handwritten digits (0-9) for image classification tasks.

  Usage

Download the dataset

wget https://huggingface.co/datasets/Cleanlab/mnist/resolve/main/mnist.tar.gz tar -xzf mnist.tar.gz

  License

MIT License

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. 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.

License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

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.

This dataset contains images from the MNIST dataset encoded as quantum states.

MNIST dataset adapted to a text-based representation.

Modified images to be ~1/4 the original area. Done by taking a max pool.

This allows testing interpolation quality for Transformer-VAEs.

System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM

Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features.

Use .array_to_text( and .text_to_array( methods to test your generated data.

Data format: - text: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example: 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! % % C L a ^ ! ! 03 down ! ! ! - ` ` ` ` ` Y ` Q ! ! 04 down ! ! ! % ` ` ` R ^ ! ! ! ! ! 05 down ! ! ! ! $ G ` ! ! ! ! ! ! ! 06 down ! ! ! ! ! # ` Y < ! ! ! ! ! 07 down ! ! ! ! ! ! 5 ` ` F ! ! ! ! 08 down ! ! ! ! ! ! ! % ` ` 1 ! ! ! 09 down ! ! ! ! ! ! F ` ` ` ! ! ! ! 10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! ! 11 down ! ! L ` ` ` ` 5 ! ! ! ! ! ! 12 down ! ! ` ` V B ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! - label: Just a number with the texts matching label.