CIFAR10100

CIFAR10100 is a composite benchmark used in machine learning research that combines the widely used CIFAR-10 and CIFAR-100 datasets to enable joint evaluation of image classification models. The CIFAR family consists of small, color images intended for benchmarking learning algorithms, with two commonly used datasets.

CIFAR-10 consists of 60,000 32x32 color images in 10 classes, with 50,000 training and 10,000 test images. CIFAR-100 also comprises 60,000 images but labels them into 100 fine categories organized into 20 coarse superclasses, again split into 50,000 training and 10,000 test images. In a CIFAR10100 setup, researchers often treat the two tasks as related but separate classification problems, allowing evaluation of shared representations, transfer learning, or multi-task learning approaches. Some experiments present the tasks sequentially for continual learning, while others train a single model on both tasks with task-specific output heads.

Typical modeling approaches in CIFAR10100 include using a shared feature extractor coupled with separate classification heads for the 10-class and the 100-class tasks, regularization to mitigate interference between tasks, and evaluation of cross-task transfer. Evaluation generally reports top-1 accuracy on the CIFAR-10 and CIFAR-100 test sets, with additional analysis of per-task performance and sometimes class-wise results.

Availability and preprocessing follow the standard CIFAR conventions. Both datasets are publicly accessible through the CIFAR project and standard machine learning libraries, commonly normalized per channel and augmented with techniques such as random cropping and horizontal flipping. Limitations include the small image size and potential biases inherent in the original datasets, which may limit generalization to higher-resolution real-world imagery. See also CIFAR-10, CIFAR-100, and multi-task learning.