--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K, 'label': 5 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["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[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `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. ## Dataset Creation ### 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 built by grouping the images form the high school students. The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set. ### Source Data #### Initial Data Collection and Normalization The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then 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. #### Who are the source language producers? Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable. ### Annotations #### Annotation process The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them. #### Who are the annotators? Same as the source data creators. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Chris Burges, Corinna Cortes and Yann LeCun ### Licensing Information MIT Licence ### Citation Information ``` @article{lecun2010mnist, title={MNIST handwritten digit database}, author={LeCun, Yann and Cortes, Corinna and Burges, CJ}, journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist}, volume={2}, year={2010} } ``` ### Contributions Thanks to [@sgugger](https://github.com/sgugger) for adding this dataset.