MNIST database

Sample images from MNIST test dataset

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.

The MNIST database contains 60,000 training images and 10,000 testing images. Half of the training set and half of the test set were taken from NIST's training dataset, while the other half of the training set and the other half of the test set were taken from NIST's testing dataset. The original creators of the database keep a list of some of the methods tested on it. In their original paper, they use a support-vector machine to get an error rate of 0.8%.

Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the (final) successor to MNIST. MNIST included images only of handwritten digits. EMNIST includes all the images from NIST Special Database 19, which is a large database of handwritten uppercase and lower case letters as well as digits. The images in EMNIST were converted into the same 28x28 pixel format, by the same process, as were the MNIST images. Accordingly, tools which work with the older, smaller, MNIST dataset will likely work unmodified with EMNIST.

History

The set of images in the MNIST database was created in 1994 as a combination of two of NIST's databases: Special Database 1; and Special Database 3.

Special Database 1 and Special Database 3 consist of digits written by high school students and employees of the United States Census Bureau, respectively.

The original dataset was a set of 128x128 binary images, processed into 28x28 grayscale images. The training set and the testing set both originally had 60k samples, but 50k of the testing set samples were discarded.

Performance

Some researchers have achieved "near-human performance" on the MNIST database, using a committee of neural networks; in the same paper, the authors achieve performance double that of humans on other recognition tasks. The highest error rate listed on the original website of the database is 12 percent, which is achieved using a simple linear classifier with no preprocessing.

In 2004, a best-case error rate of 0.42 percent was achieved on the database by researchers using a new classifier called the LIRA, which is a neural classifier with three neuron layers based on Rosenblatt's perceptron principles.

Some researchers have tested artificial intelligence systems using the database put under random distortions. The systems in these cases are usually neural networks and the distortions used tend to be either affine distortions or elastic distortions. Sometimes, these systems can be very successful; one such system achieved an error rate on the database of 0.39 percent.

In 2011, an error rate of 0.27 percent, improving on the previous best result, was reported by researchers using a similar system of neural networks. In 2013, an approach based on regularization of neural networks using DropConnect has been claimed to achieve a 0.21 percent error rate. In 2016, the single convolutional neural network best performance was 0.25 percent error rate. As of August 2018, the best performance of a single convolutional neural network trained on MNIST training data using no data augmentation is 0.25 percent error rate. Also, the Parallel Computing Center (Khmelnytskyi, Ukraine) obtained an ensemble of only 5 convolutional neural networks which performs on MNIST at 0.21 percent error rate.

Classifiers

This is a table of some of the machine learning methods used on the dataset and their error rates, by type of classifier:

Typ	Classifier	Distortion	Preprocessing	Error rate (%)
Neural Network	Gradient Descent Tunneling	None	None	0
Linear classifier	Pairwise linear classifier	None	Deskewing	7.6
K-Nearest Neighbors	K-NN with rigid transformations	None	None	0.96
K-Nearest Neighbors	K-NN with non-linear deformation (P2DHMDM)	None	Shiftable edges	0.52
Boosted Stumps	Product of stumps on Haar features	None	Haar features	0.87
Non-linear classifier	40 PCA + quadratic classifier	None	None	3.3
Random Forest	Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)	None	Simple statistical pixel importance	2.8
Support-vector machine (SVM)	Virtual SVM, deg-9 poly, 2-pixel jittered	None	Deskewing	0.56
Neural network	2-layer 784-800-10	None	None	1.6
Neural network	2-layer 784-800-10	Elastic distortions	None	0.7
Deep neural network (DNN)	6-layer 784-2500-2000-1500-1000-500-10	Elastic distortions	None	0.35
Convolutional neural network (CNN)	6-layer 784-40-80-500-1000-2000-10	None	Expansion of the training data	0.31
Convolutional neural network	6-layer 784-50-100-500-1000-10-10	None	Expansion of the training data	0.27
Convolutional neural network (CNN)	13-layer 64-128(5x)-256(3x)-512-2048-256-256-10	None	None	0.25
Convolutional neural network	Committee of 35 CNNs, 1-20-P-40-P-150-10	Elastic distortions	Width normalizations	0.23
Convolutional neural network	Committee of 5 CNNs, 6-layer 784-50-100-500-1000-10-10	None	Expansion of the training data	0.21
Convolutional neural network	Committee of 20 CNNS with Squeeze-and-Excitation Networks	None	Data augmentation	0.17
Convolutional neural network	Ensemble of 3 CNNs with varying kernel sizes	None	Data augmentation consisting of rotation and translation	0.09

See also

• List of datasets for machine learning research
• Caltech 101
• LabelMe
• OCR

Further reading

• Ciresan, Dan; Meier, Ueli; Schmidhuber, Jürgen (June 2012). "Multi-column deep neural networks for image classification" (PDF). 2012 IEEE Conference on Computer Vision and Pattern Recognition. New York, NY: Institute of Electrical and Electronics Engineers. pp. 3642–3649. arXiv:1202.2745. CiteSeerX 10.1.1.300.3283. doi:10.1109/CVPR.2012.6248110. ISBN 9781467312264. OCLC 812295155. S2CID 2161592. Retrieved 2013-12-09.

External links

• Official website
• Visualization of the MNIST database – groups of images of MNIST handwritten digits on GitHub