CIFAR-10

The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.^[1][2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.^[3] The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.^[4]

Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works.

CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the dataset was created, students were paid to label all of the images.^[5]

Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10.

Research papers claiming state-of-the-art results on CIFAR-10

This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR-10 dataset. Not all papers are standardized on the same pre-processing techniques, like image flipping or image shifting. For that reason, it is possible that one paper's claim of state-of-the-art could have a higher error rate than an older state-of-the-art claim but still be valid.

Paper title	Error rate (%)	Publication date
Convolutional Deep Belief Networks on CIFAR-10[6]	21.1	August, 2010
Maxout Networks[7]	9.38	February 13, 2013
Wide Residual Networks[8]	4.0	May 23, 2016
Neural Architecture Search with Reinforcement Learning[9]	3.65	November 4, 2016
Fractional Max-Pooling[10]	3.47	December 18, 2014
Densely Connected Convolutional Networks[11]	3.46	August 24, 2016
Shake-Shake regularization[12]	2.86	May 21, 2017
Coupled Ensembles of Neural Networks[13]	2.68	September 18, 2017
ShakeDrop regularization[14]	2.67	Feb 7, 2018
Improved Regularization of Convolutional Neural Networks with Cutout[15]	2.56	Aug 15, 2017
Regularized Evolution for Image Classifier Architecture Search[16]	2.13	Feb 6, 2018
Rethinking Recurrent Neural Networks and other Improvements for Image Classification[17]	1.64	July 31, 2020
AutoAugment: Learning Augmentation Policies from Data[18]	1.48	May 24, 2018
A Survey on Neural Architecture Search[19]	1.33	May 4, 2019
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism[20]	1.00	Nov 16, 2018
Reduction of Class Activation Uncertainty with Background Information[21]	0.95	May 5, 2023
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale[22]	0.5	2021

Benchmarks

CIFAR-10 is also used as a performance benchmark for teams competing to run neural networks faster and cheaper. DAWNBench has benchmark data on their website.

See also

• List of datasets for machine learning research
• MNIST database