cifar10

Dataset Card for CIFAR-10 Table of Contents Dataset Description Dataset Summary Supported Tasks and Leaderboards Languages Dataset Structure Data Instances Data Fields Data Splits Dataset Creation Curation Rationale Source Data Annotations Personal and Sensitive Information Considerations for Using the Data Social Impact of Dataset Discussion of Biases Other Known Limitations Additional Information Dataset Curators Licensing Information Citation Information Contributions Dataset Description **Homepage:** https://www.cs.toronto.edu/~kriz/cifar.html **Repository:** **Paper:** Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky **Leaderboard:** **Point of Contact:** Dataset 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. 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. Supported Tasks and Leaderboards `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available here. Languages English Dataset Structure Data Instances A sample from the training set is provided below: Data Fields img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` label: 0-9 with the following corresp