60,000 tiny 32×32 images across 100 balanced classes — a standard classification benchmark.
Browse commercial Computer Vision → Visit original source ↗CIFAR-100 is a small-image classification benchmark from the University of Toronto. 60,000 32×32 color images across 100 fine-grained classes, grouped into 20 superclasses, with exactly 600 images per class (500 train + 100 test). Commonly used for teaching, fast iteration, and regularization research.
LQS is our 7-dimension quality score, computed from the dataset's published statistics. See methodology →
Composite score computed from the 7 dimensions below: completeness, uniqueness, validation health, size adequacy, format compliance, label density, and class balance.
Common tasks and benchmarks where CIFAR-100 is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
CIFAR-100 is distributed under MIT-style (unrestricted research use). This is a third-party public dataset; LabelSets indexes and scores it but does not host or redistribute the data. Always verify current license terms with the maintainer before commercial use.
LabelSets sellers offer paid computer vision datasets with what public datasets often can't give you:
Other entries in the Computer Vision catalog.