70,000 handwritten digits — the canonical intro-ML benchmark.
Browse commercial Computer Vision → Visit original source ↗MNIST is the most widely-used digit classification benchmark in machine learning. Curated by Yann LeCun from a subset of NIST Special Database 3 and 1, it contains 60,000 training + 10,000 test images of 28×28 handwritten digits (0–9), perfectly balanced across classes. It's the de facto 'hello world' for image classification and every ML library ships with loaders for it.
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 MNIST is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
MNIST is distributed under Public Domain. 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.