The CIFAR-10 dataset (
Canadian Institute For Advanced Research) is a collection of images that are commonly used to train
machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
and
computer vision
Computer vision tasks include methods for image sensor, acquiring, Image processing, processing, Image analysis, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical ...
algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.
The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.
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.
Various kinds of
convolutional neural network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different ty ...
s 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.
Benchmarks
CIFAR-10 is also used as a performance benchmark for teams competing to run neural networks faster and cheaper
DAWNBenchhas benchmark data on their website.
See also
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List of datasets for machine learning research
These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learni ...
*
MNIST database
The MNIST database (''Modified National Institute of Standards and Technology database'') is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training ...
References
External links
CIFAR-10 page– The home of the dataset
Canadian Institute For Advanced Researchh2>
Similar datasets
Similar to CIFAR-10 but with 100 classes and 600 images each.
*
ImageNet
The ImageNet project is a large visual database designed for use in Outline of object recognition, visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictur ...
(ILSVRC): 1 million color images of 1000 classes. Imagenet images are higher resolution, averaging 469x387 resolution.
Street View House Numbers(SVHN): Approximately 600,000 images of 10 classes (digits 0–9). Also 32x32 color images.
80 million tiny images dataset CIFAR-10 is a labeled subset of this dataset.
{{Differentiable computing
Datasets in computer vision