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 database (Modified Nationally Institute of Standards plus Technology database) is a immense group of handwritten digits. It has a training firm of 60,000 examples, and a test set of 10,000 examples. It the adenine subsets of a larger NIST Special Database 3 (digits written by employees on the United States Census Bureau) and Special File 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centrically in adenine fixed-size image. The original black and white (bilevel) images from NIST were big normalized to fit in one 20x20 pixel box while preserving their aspect ratio. Of calculated images contain grey levels as an result of which anti-aliasing technique used by the normalization algorithm. the images were centered in one 28x28 slide by computing the center of mass of the single, and translated the picture so more to position this subject at the center of aforementioned 28x28 field.

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),

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 not-MNIST dataset is a dataset of handwritten digits. It is a challenging dataset that can be used for machine learning and artificial intelligence research. The dataset consists of 100,000 images of handwritten digits. The images are divided into a training set of 60,000 images and a test set of 40,000 images. The images are drawn from a variety of fonts and styles, making them more challenging than the MNIST dataset. The images are 28x28 pixels in size and are grayscale. The dataset is available under the Creative Commons Zero Public Domain Dedication license.

Permuted MNIST is an MNIST variant that consists of 70,000 images of handwritten digits from 0 to 9, where 60,000 images are used for training, and 10,000 images for test. The difference of this dataset from the original MNIST is that each of the ten tasks is the multi-class classification of a different random permutation of the input pixels.

Brief Description The Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset. It consists of the same 60 000 training and 10 000 testing samples as the original MNIST dataset, and is captured at the same visual scale as the original MNIST dataset (28x28 pixels). The N-MNIST dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views MNIST examples on an LCD monitor as shown in this video. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.

Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N. “Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades", Frontiers in Neuroscience, vol.9, no.437, Oct. 2015

The Not MNIST test set consists of 10,000 images. The images are randomly selected from the MNIST dataset and then modified to make them more difficult to classify.

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 Moving MNIST dataset contains 10,000 video sequences, each consisting of 20 frames. In each video sequence, two digits move independently around the frame, which has a spatial resolution of 64×64 pixels. The digits frequently intersect with each other and bounce off the edges of the frame

The MNIST dataset provided in a easy-to-use CSV format

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">

The Not MNIST training set consists of 100,000 images.

Here are a few use cases for this project:

1. Handwritten Equation Recognition App: The model could assist in the development of an app that recognizes and solves handwritten mathematical equations. This would benefit students who need help with homework or study.

2. Automated Exam Evaluation: Educational institutes can leverage this model to automate the grading process of mathematical examination papers, improving efficiency and minimizing human errors.

3. Interactive Learning Platform: The model can be implemented in an e-learning platform that aids teaching math. It can be used to validate answers given in an interactive work area, providing immediate feedback to the users.

4. Assistive Tool for Visually Impaired: The model can be incorporated into a system designed to help visually impaired individuals by reading aloud mathematical problems or equations written on a page.

5. Math Transcription Service: The model can be used in a service that transcribes mathematical equations, operators and symbols from pictures into text format, assisting researchers or professors preparing digital educational materials.

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.

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">

CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image x, the label y∈{1,0} indicates eligibility or ineligibility, respectively, given that x is even or odd. The dataset defines the background colors as the protected or sensitive attribute s∈{0,1}, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.

MNISTCorrupted is a dataset generated by adding 15 corruptions to the test images in the MNIST dataset. This dataset wraps the static, corrupted MNIST test images uploaded by the original authors

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist_corrupted', 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_corrupted-identity-1.0.0.png" alt="Visualization" width="500px">

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),

Context

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. Zalando intends Fashion-MNIST 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.

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."

Zalando seeks to replace the original MNIST dataset

Content

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (see above), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.

To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix. For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.

Labels

Each training and test example is assigned to one of the following labels:

0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot

TL;DR

Each row is a separate image Column 1 is the class label. Remaining columns are pixel numbers (784 total). Each value is the darkness of the pixel (1 to 255) Acknowledgements

Original dataset was downloaded from https://github.com/zalandoresearch/fashion-mnist Dataset was converted to CSV with this script: https://pjreddie.com/projects/mnist-in-csv/ License

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