Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class and 50000 training images and 10000 test images divided into five training batches and one test batch, each with 10000 images.
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The dataset is widely used for machine learning research and is a common benchmarking tool for computer vision systems.
According to
Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images.
CIFAR-10 and CIFAR-100 datasets
toronto.edu
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]
CIFAR-10 - Wikipedia
wikipedia.org
The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. ...
CIFAR-10 Dataset | Papers With Code
paperswithcode.com
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
cifar10 | TensorFlow Datasets
tensorflow.org
This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser, with nothing but Javascript. The state of the art on this dataset is ...
ConvNetJS CIFAR-10 demo
stanford.edu
On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis ...
CIFAR-10 on Benchmarks.AI
benchmarks.ai
