MNIST database

The MNIST database (Modified National Institute of Standards and Technology database^[1]) is a large database of handwritten digits that is commonly used for training various image processing systems.^[2][3] The database is also widely used for training and testing in the field of machine learning.^[4][5] It was created by "re-mixing" the samples from NIST's original datasets.^[6] 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.^[7] 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.^[7]

Sample images from MNIST test dataset

The MNIST database contains 60,000 training images and 10,000 testing images.^[8] 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.^[9] The original creators of the database keep a list of some of the methods tested on it.^[7] In their original paper, they use a support-vector machine to get an error rate of 0.8%.^[10]

Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the (final) successor to MNIST.^[11][12] 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.^[13][14] 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.^[15]

Special Database 1 and Special Database 3 consist of digits written by high school students and employees of the United States Census Bureau, respectively.^[7]

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.^[16]

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.^[17] The highest error rate listed^[7] on the original website of the database is 12 percent, which is achieved using a simple linear classifier with no preprocessing.^[10]

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.^[18]

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.^[7] Sometimes, these systems can be very successful; one such system achieved an error rate on the database of 0.39 percent.^[19]

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.^[20] In 2013, an approach based on regularization of neural networks using DropConnect has been claimed to achieve a 0.21 percent error rate.^[21] In 2016, the single convolutional neural network best performance was 0.25 percent error rate.^[22] 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.^[22][23] 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.^[24][25] In 2018, researchers from Department of System and Information Engineering, University of Virginia announced 0.18% error with simultaneous stacked three kind of neural networks (fully connected, recurrent and convolution neural networks).^[26]

Classifiers

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

Type	Classifier	Distortion	Preprocessing	Error rate (%)
Neural Network	Gradient Descent Tunneling	None	None	0^[27]
Linear classifier	Pairwise linear classifier	None	Deskewing	7.6^[10]
K-Nearest Neighbors	K-NN with rigid transformations	None	None	0.96^[28]
K-Nearest Neighbors	K-NN with non-linear deformation (P2DHMDM)	None	Shiftable edges	0.52^[29]
Boosted Stumps	Product of stumps on Haar features	None	Haar features	0.87^[30]
Non-linear classifier	40 PCA + quadratic classifier	None	None	3.3^[10]
Random Forest	Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)^[31]	None	Simple statistical pixel importance	2.8^[32]
Support-vector machine (SVM)	Virtual SVM, deg-9 poly, 2-pixel jittered	None	Deskewing	0.56^[33]
Neural network	2-layer 784-800-10	None	None	1.6^[34]
Neural network	2-layer 784-800-10	Elastic distortions	None	0.7^[34]
Deep neural network (DNN)	6-layer 784-2500-2000-1500-1000-500-10	Elastic distortions	None	0.35^[35]
Convolutional neural network (CNN)	6-layer 784-40-80-500-1000-2000-10	None	Expansion of the training data	0.31^[36]
Convolutional neural network	6-layer 784-50-100-500-1000-10-10	None	Expansion of the training data	0.27^[37]
Convolutional neural network (CNN)	13-layer 64-128(5x)-256(3x)-512-2048-256-256-10	None	None	0.25^[22]
Convolutional neural network	Committee of 35 CNNs, 1-20-P-40-P-150-10	Elastic distortions	Width normalizations	0.23^[17]
Convolutional neural network	Committee of 5 CNNs, 6-layer 784-50-100-500-1000-10-10	None	Expansion of the training data	0.21^[24]^[25]
Random Multimodel Deep Learning (RMDL)	10 NN-10 RNN - 10 CNN	None	None	0.18^[26]
Convolutional neural network	Committee of 20 CNNS with Squeeze-and-Excitation Networks^[38]	None	Data augmentation	0.17^[39]
Convolutional neural network	Ensemble of 3 CNNs with varying kernel sizes	None	Data augmentation consisting of rotation and translation	0.09^[40]

See also

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

References

1. "THE MNIST DATABASE of handwritten digits". Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond.
2. "Support vector machines speed pattern recognition - Vision Systems Design". Vision Systems Design. September 2004. Retrieved 17 August 2013.
3. Gangaputra, Sachin. "Handwritten digit database". Retrieved 17 August 2013.
4. Qiao, Yu (2007). "THE MNIST DATABASE of handwritten digits". Retrieved 18 August 2013.
5. Platt, John C. (1999). (PDF). Advances in Neural Information Processing Systems: 557–563. Archived from the original (PDF) on 4 March 2016. Retrieved 18 August 2013.
6. Grother, Patrick J. "NIST Special Database 19 - Handprinted Forms and Characters Database" (PDF). National Institute of Standards and Technology.
7. LeCun, Yann; Cortez, Corinna; Burges, Christopher C.J. "The MNIST Handwritten Digit Database". Yann LeCun's Website yann.lecun.com. Retrieved 30 April 2020.
8. Kussul, Ernst; Baidyk, Tatiana (2004). "Improved method of handwritten digit recognition tested on MNIST database". Image and Vision Computing. 22 (12): 971–981. doi:10.1016/j.imavis.2004.03.008.
9. Zhang, Bin; Srihari, Sargur N. (2004). "Fast k-Nearest Neighbor Classification Using Cluster-Based Trees" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 26 (4): 525–528. doi:10.1109/TPAMI.2004.1265868. PMID 15382657. S2CID 6883417. Retrieved 20 April 2020.
10. LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-Based Learning Applied to Document Recognition" (PDF). Proceedings of the IEEE. 86 (11): 2278–2324. doi:10.1109/5.726791. S2CID 14542261. Retrieved 18 August 2013.
11. NIST (4 April 2017). "The EMNIST Dataset". NIST. Retrieved 11 April 2022.
12. NIST (27 August 2010). "NIST Special Database 19". NIST. Retrieved 11 April 2022.
13. Cohen, G.; Afshar, S.; Tapson, J.; van Schaik, A. (2017). "EMNIST: an extension of MNIST to handwritten letters". arXiv:1702.05373 [cs.CV].
14. Cohen, G.; Afshar, S.; Tapson, J.; van Schaik, A. (2017). "EMNIST: an extension of MNIST to handwritten letters". arXiv:1702.05373v1 [cs.CV].
15. Bottou, Léon; Cortes, Corinna; Denker, John S.; Drucker, Harris; Guyon, Isabelle; Jackel, L. D.; LeCun, Y.; Muller, U. A.; Sackinger, E.; Simard, P.; Vapnik, V. (1994). "Comparison of classifier methods: A case study in handwritten digit recognition". Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5). Vol. 2. Jerusalem, Israel. pp. 77–82. doi:10.1109/ICPR.1994.576879. ISBN 0-8186-6270-0.{{cite book}}: CS1 maint: location missing publisher (link)
16. Yadav, Chhavi; Bottou, Leon (2019). "Cold Case: The Lost MNIST Digits". Advances in Neural Information Processing Systems. 32. arXiv:1905.10498. Article has a detailed history and a reconstruction of the discarded testing set.
17. Cires¸an, Dan; Ueli Meier; Jürgen Schmidhuber (2012). "Multi-column deep neural networks for image classification" (PDF). 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3642–3649. arXiv:1202.2745. CiteSeerX 10.1.1.300.3283. doi:10.1109/CVPR.2012.6248110. ISBN 978-1-4673-1228-8. S2CID 2161592.
18. Kussul, Ernst; Tatiana Baidyk (2004). (PDF). Image and Vision Computing. 22 (12): 971–981. doi:10.1016/j.imavis.2004.03.008. Archived from the original (PDF) on 21 September 2013. Retrieved 20 September 2013.
19. Ranzato, Marc'Aurelio; Christopher Poultney; Sumit Chopra; Yann LeCun (2006). "Efficient Learning of Sparse Representations with an Energy-Based Model" (PDF). Advances in Neural Information Processing Systems. 19: 1137–1144. Retrieved 20 September 2013.
20. Ciresan, Dan Claudiu; Ueli Meier; Luca Maria Gambardella; Jürgen Schmidhuber (2011). (PDF). 2011 International Conference on Document Analysis and Recognition (ICDAR). pp. 1135–1139. CiteSeerX 10.1.1.465.2138. doi:10.1109/ICDAR.2011.229. ISBN 978-1-4577-1350-7. S2CID 10122297. Archived from the original (PDF) on 22 February 2016. Retrieved 20 September 2013.
21. Wan, Li; Matthew Zeiler; Sixin Zhang; Yann LeCun; Rob Fergus (2013). Regularization of Neural Network using DropConnect. International Conference on Machine Learning(ICML).
22. SimpleNet (2016). "Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures". arXiv:1608.06037. Retrieved 3 December 2020.
23. SimpNet (2018). "Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet". Github. arXiv:1802.06205. Retrieved 3 December 2020.
24. Romanuke, Vadim. "Parallel Computing Center (Khmelnytskyi, Ukraine) represents an ensemble of 5 convolutional neural networks which performs on MNIST at 0.21 percent error rate". Retrieved 24 November 2016.
25. Romanuke, Vadim (2016). "Training data expansion and boosting of convolutional neural networks for reducing the MNIST dataset error rate". Research Bulletin of NTUU "Kyiv Polytechnic Institute". 6 (6): 29–34. doi:10.20535/1810-0546.2016.6.84115.
26. Kowsari, Kamran; Heidarysafa, Mojtaba; Brown, Donald E.; Meimandi, Kiana Jafari; Barnes, Laura E. (2018-05-03). "RMDL: Random Multimodel Deep Learning for Classification". Proceedings of the 2nd International Conference on Information System and Data Mining. pp. 19–28. arXiv:1805.01890. doi:10.1145/3206098.3206111. ISBN 9781450363549. S2CID 19208611.
27. Deng, Bo (2023-12-26). "Error-free Training for Artificial Neural Network". arXiv:2312.16060 [cs.LG].
28. Lindblad, Joakim; Nataša Sladoje (January 2014). "Linear time distances between fuzzy sets with applications to pattern matching and classification". IEEE Transactions on Image Processing. 23 (1): 126–136. Bibcode:2014ITIP...23..126L. doi:10.1109/TIP.2013.2286904. PMID 24158476. S2CID 1908950.
29. Keysers, Daniel; Thomas Deselaers; Christian Gollan; Hermann Ney (August 2007). "Deformation models for image recognition". IEEE Transactions on Pattern Analysis and Machine Intelligence. 29 (8): 1422–1435. CiteSeerX 10.1.1.106.3963. doi:10.1109/TPAMI.2007.1153. PMID 17568145. S2CID 2528485.
30. Kégl, Balázs; Róbert Busa-Fekete (2009). "Boosting products of base classifiers" (PDF). Proceedings of the 26th Annual International Conference on Machine Learning. pp. 497–504. doi:10.1145/1553374.1553439. ISBN 9781605585161. S2CID 8460779. Retrieved 27 August 2013.
31. "RandomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)". 21 January 2020.
32. "Mehrad Mahmoudian / MNIST with RandomForest".
33. Decoste, Dennis; Schölkopf, Bernhard (2002). "Training Invariant Support Vector Machines". Machine Learning. 46 (1–3): 161–190. doi:10.1023/A:1012454411458. ISSN 0885-6125. OCLC 703649027.
34. Patrice Y. Simard; Dave Steinkraus; John C. Platt (2003). "Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis". Proceedings of the Seventh International Conference on Document Analysis and Recognition. Vol. 1. Institute of Electrical and Electronics Engineers. p. 958. doi:10.1109/ICDAR.2003.1227801. ISBN 978-0-7695-1960-9. S2CID 4659176.
35. Ciresan, Claudiu Dan; Ueli Meier; Luca Maria Gambardella; Juergen Schmidhuber (December 2010). "Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition". Neural Computation. 22 (12): 3207–20. arXiv:1003.0358. doi:10.1162/NECO_a_00052. PMID 20858131. S2CID 1918673.
36. Romanuke, Vadim. "The single convolutional neural network best performance in 18 epochs on the expanded training data at Parallel Computing Center, Khmelnytskyi, Ukraine". Retrieved 16 November 2016.
37. Romanuke, Vadim. "Parallel Computing Center (Khmelnytskyi, Ukraine) gives a single convolutional neural network performing on MNIST at 0.27 percent error rate". Retrieved 24 November 2016.
38. Hu, Jie; Shen, Li; Albanie, Samuel; Sun, Gang; Wu, Enhua (2019). "Squeeze-and-Excitation Networks". IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (8): 2011–2023. arXiv:1709.01507. doi:10.1109/TPAMI.2019.2913372. PMID 31034408. S2CID 140309863.
39. "GitHub - Matuzas77/MNIST-0.17: MNIST classifier with average 0.17% error". GitHub. 25 February 2020.
40. An, Sanghyeon; Lee, Minjun; Park, Sanglee; Yang, Heerin; So, Jungmin (2020-10-04). "An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition". arXiv:2008.10400 [cs.CV].

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