14.2M hand-annotated images across 21K categories — the dataset that launched deep learning.
Browse commercial Computer Vision → Visit original source ↗ImageNet is the seminal large-scale image classification dataset maintained by Stanford Vision Lab. It contains 14.2M images hand-annotated across 21,841 WordNet synset categories. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) subset of 1.2M images / 1000 classes remains the canonical benchmark for image classification and drove the deep learning revolution via AlexNet in 2012.
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 ImageNet is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
ImageNet is distributed under Custom (non-commercial research). 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.