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">

The MNIST-100 dataset is a variation of the original MNIST dataset, consisting of 100 handwritten numbers extracted from the MNIST dataset. Unlike the traditional MNIST dataset, which contains 60,000 training images of digits from 0 to 9, the Modified MNIST-10 dataset focuses on 100 numbers. Dataset Overview:

Dataset Name: MNIST-100 Total Number of Images: train: 60000 test: 1000 Classes: 100 (Numbers from 00 to 99) Image Size: 28x56 pixels (grayscale)

Data Collection: The MNIST-100 dataset… See the full description on the dataset page: https://huggingface.co/datasets/marcin119a/mnist100.

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.

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. The database is also widely used for training and testing in the field of machine learning. It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.

Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond

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 effort on preprocessing and formatting.

Four files are available on this site: train-images-idx3-ubyte.gz: training set images (9912422 bytes) train-labels-idx1-ubyte.gz: training set labels (28881 bytes) t10k-images-idx3-ubyte.gz: test set images (1648877 bytes) t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)

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.

The K-MNIST dataset is a large dataset of handwritten digits, each image is 28x28 pixels and consists of 10 classes (0-9).

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">

A dataset for magnetic particle imaging based on the MNIST dataset.

This dataset contains simulated MPI measurements along with ground truth phantoms selected from the https://yann.lecun.com/exdb/mnist/" target="_blank" rel="noopener">MNIST database of handwritten digits. A state-of-the-art model-based system matrix is used to simulate the MPI measurements of the MNIST phantoms. These measurements are equipped with noise perturbations captured by the preclinical MPI system (Bruker, Ettlingen, Germany). The dataset can be utilized in its provided form, while additional data is included to offer flexibility for creating customized versions.

MPI-MNIST features four different system matrices, each available in three spatial resolutions. The provided data is generated using a specified system matrix at highest spatial resolution. Reconstruction operations can be performed by using any of the provided system matrices at a lower resolution. This setup allows for simulating reconstructions from either an exact or an inexact forward operator. To cover further operator deviation setups, we provide additional noise data for the application of pixelwise noise to the reconstruction system matrix.

For supporting the development of learning-based methods, a large amount of further noise samples, captured by the Bruker scanner, is provided.

For a detailed description of the dataset, see arxiv.org/abs/2501.05583.

The Python-based GitHub repository available at https://github.com/meiraiske/MPI-MNIST" href="https://github.com/meiraiske/MPI-MNIST" target="_blank" rel="noopener">https://github.com/meiraiske/MPI-MNIST can be used for downloading the data from this website and preparing it for project use which includes an integration to PyTorch or PyTorch Lightning modules.

File Structure

All data, except for the phantoms, is provided in the MDF file format. This format is specifically tailored to store MPI data and contains metadata corresponding to the experimental setup. The ground truth phantoms are provided as HDF5 files since they do not require any metadata.

• SM: Contains twelve system matrices named SM_{physical model}_{resolution}.mdf. It covers four physical models given in three resolutions ('coarse', 'int' and 'fine'). The highest resolution ('fine') is used for data generation.
• large_noise: Contains large_NoiseMeas.mdf with 390060 noise measurements. Each noise measurement has been averaged over ten empty scanner measurements. This can be used e.g. for learning-based methods.

For dataset in ['train', 'test']:

• {dataset}_noise: Contains four noise matrices, where each noise measurement has been averaged over ten empty scanner measurements:
  1. NoiseMeas_phantom_{dataset}.mdf : Additive measurement noise for simulated measurements.
  2. NoiseMeas_phantom_bg_{dataset}.mdf : Unused noise reserved for background correction of 1.
  3. NoiseMeas_SM_{dataset}.mdf : System Matrix noise, that can be applied to each pixel of the reconstruction system matrix.
  4. NoiseMeas_SM_bg_{dataset}.mdf : Unused noise reserved for background correction of 3.
• {dataset}_gt: Contains {dataset}_gt.hdf5 with flattened and preprocessed ground truth MNIST phantoms given in coarse resolution (15x17=255 pixels) with pixel values in [0, 10].
• {dataset}_obs: Contains {dataset}_obs.mdf with noise free simulated measurements (observations) of {dataset}_gt.hdf5 using the system matrix stored in SM_fluid_opt_fine.mdf.
• {dataset}_obsnoisy: Contains {dataset}_obsnoisy.mdf with noise contained simulated measurements, resulting from {dataset}_obs.mdf and {dataset}_phantom_noise.mdf.

In line with MNIST, each MDF/HDF5 file in {dataset}_gt, {dataset}_obs, {dataset}_obsnoisy for dataset in ['train', 'test'] contains 60000 samples for 'train' and 10000 samples for 'test'. The data can be manually reproduced in the intermediate resolution (45x51=2295 pixels) from the files in this dataset using the system matrices in intermediate ('int') resolution for reconstruction and upsampling the ground truth phantoms by 3 pixels per dimension. This case is also implemented in the Github repository .

The PDF file MPI-MNIST_Metadata.pdf contains a list of meta information for each of the MDF files of this dataset.

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 Enhanced Sign Language MNIST dataset is a comprehensive collection of grayscale images representing American Sign Language (ASL) gestures. This dataset serves as an enhancement to the original Sign Language MNIST dataset, providing a more diverse and extensive set of hand gesture samples for machine learning tasks.

Inspired by the need for more challenging benchmarks in image-based machine learning, this dataset is consistent with the original Sign Language MNIST dataset to acquire a self-generated dataset, resulting in a more robust and varied collection of hand gesture images. The original Sign Language MNIST dataset, available on Kaggle, provided a solid foundation with 27,455 training cases and 7,172 test cases, each representing a label (0-25) mapped to an alphabetic letter A-Z (excluding J and Z).

The Enhanced Sign Language MNIST dataset builds upon this foundation by incorporating additional images generated through a process involving various image manipulation techniques. These techniques include hand tracking using MediaPipe, cropping, grayscale conversion, and resizing, to create approximately 1400 samples of each alphabetic letter. The enhanced dataset contains 69,252 samples in total, with 55,402 samples for training and validation, and 13,850 samples for testing.

This dataset is invaluable for researchers and developers working on sign language recognition, hand gesture detection, and related computer vision tasks. It offers a challenging benchmark for evaluating the performance of machine learning models, particularly Convolutional Neural Networks (CNNs), in recognizing ASL gestures.

The dataset is divided into training and testing sets following the methodology outlined in Oladayo's research (2024), ensuring the consistency and reproducibility of experimental setups. The experimentation framework incorporated four distinct Convolutional Neural Network (CNN) models: CNN1, CNN2, CNN3, and CNN4. Additionally, four diverse data augmentation techniques were employed, denoted as DAM1, DAM2, DAM3, and DAM4. Notably, DAM1 represents the scenario where no data augmentation is applied.

CNN2 achieved a remarkable 99.89% validation accuracy on the enhanced test samples and 99.78% on the generated test samples. Training the model on a GPU/TPU took approximately 209 seconds (3.5 minutes), which is close to the results reported in the research report. This success underscores the effectiveness of sample generation in enhancing the model's performance, showcasing its superiority over traditional data augmentation methods.

With the Enhanced Sign Language MNIST dataset, researchers can explore new approaches to sign language recognition, develop more robust machine learning models, and ultimately contribute to the advancement of assistive technologies for the deaf and hard-of-hearing community.

If you use this code or the datasets in your research, please cite the following dissertation: Oladayo Luke. (2024). Enhancing Sign Language Recognition and Hand Gesture Detection using Convolutional Neural Networks and Data Augmentation Techniques. (Doctoral dissertation, Nova Southeastern University).

The MNIST dataset is a collection of images of handwritten digits, with size n = 70,000 and D = 784.

📊 MyTorch Refined MNIST Dataset

Curated by Aryan Singh Chandel (Shiro) at Rustamji Institute of Technology (RJIT). This dataset contains the refined version of the MNIST handwritten digit database, specifically pre-processed for compatibility with the MyTorch deep learning framework.

  🏗️ Dataset Structure

The data is stored in a compressed NumPy format (mnist_raw.npz) containing:

X_train / y_train: 60,000 samples for training. X_test / y_test: 10,000 samples for final… See the full description on the dataset page: https://huggingface.co/datasets/ShiroOnigami23/MyTorch-MNIST-Dataset.

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.

The Fashion MNIST dataset is a large dataset of fashion images, each image is a 28x28 grayscale image, and there are 60,000 training images, 10,000 validation images, and 10,000 test images.

The dataset used in the paper is a synthetic MNIST dataset generated by forming barycenters constructed with weights sampled uniformly from ∆3.

The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.

The MNIST dataset consists of 60,000 training samples and 10,000 test samples. Each sample is a 28×28 pixel grayscale handwritten digital image.

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.

Binarized version of the MNIST handwritten digits dataset