Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.

Benchmarks

MNIST dataset

Users: 1 - Average Rating: 4.00


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

Language/Environments:
C, C++, MatLab, Octave, Python, R

Target Group:
Basic


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


Reviews

You have to login to leave a comment. If you are not registered click here

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: https://doi.org/10.3390/app9153169, 10.2991/ijcis.2017.10.1.38, https://doi.org/10.1016/j.procs.2020.03.309.