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CIFAR-10 dataset

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The Benchmark has the following main features:
• It consists of 60000 32x32 color images belonging to 10 classes, with 6000 images per class.
• The 10 classes of the dataset are Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.
• There are 50000 training images and 10000 test images, divided into five training batches and one test batch, each with 10000 images.
• The test batch contains exactly 1000 randomly selected images from each class, while the training batches contain exactly 5000 images from each class.
• Each image is represented by 3072 features, the first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue.
• Labels are numbers in the range 0-9.
• There are no missing values.
• It is easy to download and use, instructions can be found at the link of the dataset.

Scientific Area:
Machine Learning

MatLab, Python

Target Group:

Cite as:
Krizhevsky, A., and Hinton, G., Learning multiple layers of features from tiny images, Citeseer, (2009): 7.

Author of the review:
Giulia Cademartori
University of Genoa


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Vincenzo DAmato DAmato

This is a basic dataset for images classification. You can find examples and cues to deal with these data in the following articles: 10.1109/ICSCCC.2018.8703316, 10.1109/CSCI.2016.0225, 10.14569/IJACSA.2019.0100638.