The CIFAR-10 dataset (
Canadian Institute For Advanced Research
The Canadian Institute for Advanced Research (CIFAR) is a Canadian-based global research organization that brings together teams of top researchers from around the world to address important and complex questions. It was founded in 1982 and is s ...
) is a collection of images that are commonly used to train
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
and
computer vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the hum ...
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. When the dataset was created, students were paid to label all of the images.
Various kinds of
convolutional neural network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
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.
See also
*
List of datasets for machine learning research
These datasets are applied for machine learning 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 learning ...
*
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 a ...
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 (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