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MNIST dataset

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The Benchmark has the following main features:
• It consists of 70,000 28x28 binary images of handwritten digits from 0 to 9, written by different writers.
• The digits have been size-normalized and centered in a fixed-size image.
• The training set consist of 60,000 examples and the test set of 10,000 examples.
• Each image is represented by 28x28 pixels, which have values from 0 to 255, 0 means background (white), 255 means foreground (black).
• Labels are numbers in the range 0-9.
• There’re no missing values.
• It is easy to download and use, instructions can be found at the link of the dataset.
• It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Scientific Area:
Machine Learning

C, C++, MatLab, Octave, Python, R

Target Group:

Cite as:
LeCun, Y. and Bottou, L. and Bengio, Y. and Haffner, P., Gradient-based learning applied to document recognition, Proceedings of the IEEE 86.11 (1998): 2278-2324.

Author of the review:
Giulia Cademartori
University of Genoa


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Davide Ilardi

This is a basic dataset to test your algorithms in image classification. See the following papers for examples on how to address these data:, 10.2991/ijcis.2017.10.1.38,