MNIST database

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Short description: Database of handwritten digits
MNIST sample images
Sample images from MNIST test dataset

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]

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 1998 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.[7]

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.[15] 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.[16]

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.[17]

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.[18] In 2013, an approach based on regularization of neural networks using DropConnect has been claimed to achieve a 0.21 percent error rate.[19] In 2016, the single convolutional neural network best performance was 0.25 percent error rate.[20] 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.[20][21] 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.[22][23] Some images in the testing dataset are barely readable and may prevent reaching test error rates of 0%.[24] 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).[25]

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 (%)
Linear classifier Pairwise linear classifier None Deskewing 7.6[10]
K-Nearest Neighbors K-NN with non-linear deformation (P2DHMDM) None Shiftable edges 0.52[26]
Boosted Stumps Product of stumps on Haar features None Haar features 0.87[27]
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)[28] None Simple statistical pixel importance 2.8[29]
Support-vector machine (SVM) Virtual SVM, deg-9 poly, 2-pixel jittered None Deskewing 0.56[30]
Deep neural network (DNN) 2-layer 784-800-10 None None 1.6[31]
Deep neural network 2-layer 784-800-10 Elastic distortions None 0.7[31]
Deep neural network 6-layer 784-2500-2000-1500-1000-500-10 Elastic distortions None 0.35[32]
Convolutional neural network (CNN) 6-layer 784-40-80-500-1000-2000-10 None Expansion of the training data 0.31[33]
Convolutional neural network 6-layer 784-50-100-500-1000-10-10 None Expansion of the training data 0.27[34]
Convolutional neural network (CNN) 13-layer 64-128(5x)-256(3x)-512-2048-256-256-10 None None 0.25[20]
Convolutional neural network Committee of 35 CNNs, 1-20-P-40-P-150-10 Elastic distortions Width normalizations 0.23[15]
Convolutional neural network Committee of 5 CNNs, 6-layer 784-50-100-500-1000-10-10 None Expansion of the training data 0.21[22][23]
Random Multimodel Deep Learning (RMDL) 10 NN-10 RNN - 10 CNN None None 0.18[25]
Convolutional neural network Committee of 20 CNNS with Squeeze-and-Excitation Networks[35] None Data augmentation 0.17[36]

See also

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. http://yann.lecun.com/exdb/mnist/. 
  2. ↑ "Support vector machines speed pattern recognition - Vision Systems Design". Vision Systems Design. http://www.vision-systems.com/articles/print/volume-9/issue-9/technology-trends/software/support-vector-machines-speed-pattern-recognition.html. 
  3. ↑ Gangaputra, Sachin. "Handwritten digit database". http://cis.jhu.edu/~sachin/digit/digit.html. 
  4. ↑ Qiao, Yu (2007). "THE MNIST DATABASE of handwritten digits". http://www.gavo.t.u-tokyo.ac.jp/~qiao/database.html. 
  5. ↑ Platt, John C. (1999). "Using analytic QP and sparseness to speed training of support vector machines". Advances in Neural Information Processing Systems: 557–563. http://ar.newsmth.net/att/148aa490aed5b5/smo-nips.pdf. Retrieved 18 August 2013. 
  6. ↑ Grother, Patrick J.. "NIST Special Database 19 - Handprinted Forms and Characters Database". https://www.nist.gov/system/files/documents/srd/nistsd19.pdf. 
  7. ↑ 7.0 7.1 7.2 7.3 7.4 7.5 LeCun, Yann; Cortez, Corinna; Burges, Christopher C.J.. "The MNIST Handwritten Digit Database". http://yann.lecun.com/exdb/mnist/. 
  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". IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (4): 525–528. doi:10.1109/TPAMI.2004.1265868. PMID 15382657. http://mleg.cse.sc.edu/edu/csce822/uploads/Main.ReadingList/KNN_fastbyClustering.pdf. Retrieved 20 April 2020. 
  10. ↑ 10.0 10.1 10.2 10.3 LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-Based Learning Applied to Document Recognition". Proceedings of the IEEE 86 (11): 2278–2324. doi:10.1109/5.726791. http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf. Retrieved 18 August 2013. 
  11. ↑ NIST. "The EMNIST Dataset". https://www.nist.gov/itl/products-and-services/emnist-dataset. 
  12. ↑ NIST. "NIST Special Database 19". https://www.nist.gov/srd/nist-special-database-19. 
  13. ↑ Cohen, G.; Afshar, S.; Tapson, J.; van Schaik, A. (2017). "EMNIST: an extension of MNIST to handwritten letters.". https://arxiv.org/abs/1702.05373. 
  14. ↑ Cohen, G.; Afshar, S.; Tapson, J.; van Schaik, A. (2017). "EMNIST: an extension of MNIST to handwritten letters.". https://arxiv.org/abs/1702.05373v1. 
  15. ↑ 15.0 15.1 Cires¸an, Dan; Ueli Meier; Jürgen Schmidhuber (2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3642–3649. doi:10.1109/CVPR.2012.6248110. ISBN 978-1-4673-1228-8. http://repository.supsi.ch/5145/1/IDSIA-04-12.pdf. 
  16. ↑ Kussul, Ernst; Tatiana Baidyk (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. https://vlabdownload.googlecode.com/files/Image_VisionComputing.pdf. Retrieved 20 September 2013. 
  17. ↑ Ranzato, Marc'Aurelio; Christopher Poultney; Sumit Chopra; Yann LeCun (2006). "Efficient Learning of Sparse Representations with an Energy-Based Model". Advances in Neural Information Processing Systems 19: 1137–1144. http://yann.lecun.com/exdb/publis/pdf/ranzato-06.pdf. Retrieved 20 September 2013. 
  18. ↑ Ciresan, Dan Claudiu; Ueli Meier; Luca Maria Gambardella; Jürgen Schmidhuber (2011). "Convolutional neural network committees for handwritten character classification". 2011 International Conference on Document Analysis and Recognition (ICDAR). pp. 1135–1139. doi:10.1109/ICDAR.2011.229. ISBN 978-1-4577-1350-7. http://www.icdar2011.org/fileup/PDF/4520b135.pdf. Retrieved 20 September 2013. 
  19. ↑ Wan, Li; Matthew Zeiler; Sixin Zhang; Yann LeCun; Rob Fergus (2013). "Regularization of Neural Network using DropConnect". International Conference on Machine Learning(ICML). 
  20. ↑ 20.0 20.1 20.2 SimpleNet (2016). "Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures". https://github.com/Coderx7/SimpleNet. 
  21. ↑ SimpNet (2018). "Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet". https://github.com/Coderx7/SimpNet. 
  22. ↑ 22.0 22.1 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.". https://drive.google.com/file/d/0B1WkCFOvGHDddElkdkl6bzRLRE0/view?usp=sharing. 
  23. ↑ 23.0 23.1 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. 
  24. ↑ MNIST classifier, GitHub. "Classify MNIST digits using Convolutional Neural Networks". https://github.com/j05t/mnist. 
  25. ↑ 25.0 25.1 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 2018 International Conference on Information System and Data Mining. doi:10.1145/3206098.3206111. 
  26. ↑ 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. doi:10.1109/TPAMI.2007.1153. PMID 17568145. 
  27. ↑ Kégl, Balázs; Róbert Busa-Fekete (2009). "Boosting products of base classifiers". Proceedings of the 26th Annual International Conference on Machine Learning: 497–504. https://users.lal.in2p3.fr/kegl/research/PDFs/keglBusafeRekete09.pdf. Retrieved 27 August 2013. 
  28. ↑ "RandomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)". 21 January 2020. https://cran.r-project.org/web/packages/randomForestSRC/. 
  29. ↑ "Mehrad Mahmoudian / MNIST with RandomForest". https://gitlab.com/mehrad/mnist-with-randomforest. 
  30. ↑ 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. 
  31. ↑ 31.0 31.1 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. 1. Institute of Electrical and Electronics Engineers. pp. 958. doi:10.1109/ICDAR.2003.1227801. ISBN 978-0-7695-1960-9. http://research.microsoft.com/apps/pubs/?id=68920. 
  32. ↑ 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. doi:10.1162/NECO_a_00052. PMID 20858131. 
  33. ↑ Romanuke, Vadim. "The single convolutional neural network best performance in 18 epochs on the expanded training data at Parallel Computing Center, Khmelnytskyi, Ukraine". https://drive.google.com/file/d/0B1WkCFOvGHDdWlZvWUlLd0V3ZFU/view?usp=sharing. 
  34. ↑ Romanuke, Vadim. "Parallel Computing Center (Khmelnytskyi, Ukraine) gives a single convolutional neural network performing on MNIST at 0.27 percent error rate". https://drive.google.com/file/d/0B1WkCFOvGHDdOC0yR0tfbmpidjg/view?usp=sharing. 
  35. ↑ 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. doi:10.1109/TPAMI.2019.2913372. PMID 31034408. 
  36. ↑ "GitHub - Matuzas77/MNIST-0.17: MNIST classifier with average 0.17% error". 25 February 2020. https://github.com/Matuzas77/MNIST-0.17.git. 

Further reading

External links




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