Drop-in MNIST replacement with 70,000 fashion item images across 10 classes.
Browse commercial Computer Vision → Visit original source ↗Fashion-MNIST from Zalando Research is a drop-in replacement for MNIST that's harder but keeps the same format. 70,000 28×28 grayscale images of fashion items (T-shirt, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag, ankle boot), perfectly balanced at 7,000 images per class. Designed to address MNIST saturation — most modern classifiers hit >99% on MNIST, leaving no room to differentiate approaches.
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 Fashion-MNIST is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
Fashion-MNIST is distributed under MIT. 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.
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Other entries in the Computer Vision catalog.