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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.
Yann LeCun is a director of AI research at Facebook and a professor at NYU. He works on machine learning, computer vision, mobile robotics, and computational neuroscience. He also leads the MNIST project, a large-scale benchmark for handwritten digit recognition.
- Dataset Structure
- Dataset Creation
- Considerations For Using The Data
- Additional Information
Data Instances
A data point comprises an image and its label:
Data Fields
1. image: A PIL.Image.Image object containing the 28x28 image. Note that when accessing the image column: dataset["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset["image"] should always be preferred over dataset["image"] 2. label: an integer between 0 and 9 representing the digit.
Data Splits
The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.
Curation Rationale
The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was buil...
Personal and Sensitive Information
[More Information Needed]
Dataset Curators
Chris Burges, Corinna Cortes and Yann LeCun
Licensing Information
MIT Licence
Contributions
Thanks to @sguggerfor adding this dataset.
The MNIST database of handwritten digits is one of the most popular image recognition datasets. It contains 60k examples for training and 10k examples for testing. This page intends to provide a mirror site for downloading MNIST database hosted on http://yann.lecun.com/exdb/mnist/.
Yann LeCun. Chief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York University. Verified email at cs.nyu.edu - Homepage. AI machine learning computer vision...
MNIST is a subset of NIST data with 60,000 training and 10,000 test images of digits. It is available as a HDF5 file that can be read by PyMVPA, a Python library for machine learning and pattern recognition.
Jun 1, 2023 · Description: The MNIST database of handwritten digits. Additional Documentation : Explore on Papers With Code north_east. Homepage : http://yann.lecun.com/exdb/mnist/ Source code : tfds.image_classification.MNIST. Versions: 3.0.1 (default): No release notes. Download size: 11.06 MiB. Dataset size: 21.00 MiB. Auto-cached ( documentation ): Yes.
