CIFAR Dataset

Data
Image data
Computer vision
Image classification
CNN
CIFAR
Author

Aidan O’Brien

Published

March 3, 2025

About this resource

CIFAR-10 (Canadian Institute for Advanced Research) is a widely used dataset consisting of 60,000 color images at a resolution of 32×32 pixels, spanning 10 distinct classes with 6,000 images per class. To be exact, the classes are airplanes, automobiles, cats, deer, dogs, horses, ships, and trucks. It is a labeled subset of the 80 Million Tiny Images dataset, originally created by researchers at MIT and NYU, with image annotations provided by paid student annotators. Despite its simplicity, CIFAR-10 covers a diverse set of real-world objects, making it a standard benchmark for evaluating and training computer vision models.

CIFAR-100 is a more fine-grained variant of CIFAR-10, containing the same images but categorized into 100 classes instead of 10. These 100 classes are grouped into 20 superclasses, with 600 images per class. This finer categorization makes CIFAR-100 a more challenging dataset, requiring models to distinguish between more nuanced variations of objects within the same broad categories. For example, in CIFAR-10, all dogs fall under a single “dog” category. In CIFAR-100, this is broken down into multiple specific dog breeds such as beagle, dalmatian, and golden retriever. Similarly, the “automobile” category in CIFAR-10 is split into more detailed vehicle types in CIFAR-100, such as pickup truck, race car, and tractor. This increased level of detail makes CIFAR-100 useful for training models that need to recognize finer-grained object differences.

Key features

  • Small size: Each image is only 32x32 pixels, and with only 60,000 images in total, the dataset is computationally manageable.
  • Diversity: The dataset cover a broad range of real-world objects, mainly consisting to animals and transportation vehicles. With this diveristy, CIFAR-10 ensures that models generalize well across different types of objects rather than specializing in a narrow domain.
  • Balance: Each class contains an equal number of images, preventing class imbalance issues.

Key applications

  • Image classification benchmarking: CIFAR has historically been a widely used benchmark dataset for evaluating the performance of image classification models, including convolutional neural networks (CNNs), vision transformers (ViTs), and other deep learning architectures.
  • Training, testing, and experimentation: CIFAR is commonly used in supervised learning tasks such as training CNNs for object recognition, evaluating feature extraction techniques (e.g., PCA), and testing optimization methods.
  • Transfer learning: Pre-trained models on CIFAR are often used as a starting point for fine-tuning CNNs when working with limited training data.
  • Data augmentation: CIFAR images can be augmented using transformations such as random rotations, flips, cropping, and color adjustments to artificially expand the dataset and improve model generalization. More advanced augmentation methods like MixUp and CutMix can further enhance classification accuracy.

Ethical concerns and acknowledgments

CIFAR’s parent dataset has been widely criticized for its content and collection method. 80 Million Tiny Images was created by scraping images off the internet without knowledge or consent of any of the owners of the photos. Also, in 2020, 80 Million Tiny Images was found to contain a range of racist, sexist, and other offensive labels, and for that reason was taken offline by MIT ans NYU in 2020, who also requested other researchers and users refrain from using copies of the dataset.

Questions?

If you have any lingering questions about this resource, feel free to post them on the ML+X Nexus Q&A on GitHub. We will update this resource as new information or applications arise.

See also