文獻引用:https://www.cs.toronto.edu/~kriz/cifar.html
The CIFAR-10 dataset
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. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.
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If you're going to use this dataset, please cite the tech report at the bottom of this page.
Version Size md5sum
CIFAR-10 python version 163 MB c58f30108f718f92721af3b95e74349a
CIFAR-10 Matlab version 175 MB 70270af85842c9e89bb428ec9976c926
CIFAR-10 binary version (suitable for C programs) 162 MB c32a1d4ab5d03f1284b67883e8d87530#