MNIST Benchmark Database

Context:
- It can (often) be used as a Supervised Classification Benchmark Database for (handwritten character recognition).
- It also serves as a standard for evaluating the performance of machine learning algorithms for digit classification tasks.
- ...
Example(s):
- https://paperswithcode.com/dataset/mnist
- ...
Counter-Example(s):
- ImageNet Database,
- MS COCO Dataset.
See: Handwritten Digit Recognition, Machine Learning, Benchmark Dataset.

References

(Deng, 2012) ⇒ Li Deng. (2012). “The MNIST Database of Handwritten Digit Images for Machine Learning Research [best of the Web].” IEEE Signal Processing Magazine 29, no. 6
- QUOTE: ... this issue ... presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research …

(LeCun et al., 1998a) ⇒ Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. (1998). “Gradient-based Learning Applied to Document Recognition.” Proceedings of the IEEE 86, no. 11

(LeCun et al., 1998b) ⇒ Yann LeCun, Corinna Cortes, and Christopher J.C. Burges. (2010). “MNIST Handwritten Digit Database.” AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2
- QUOTE: The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
  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. …
  … The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.