ImageNet

technology · earth

The labelled image dataset, begun in 2007 under Fei-Fei Li and released from 2009, that supplied the missing ingredient for deep learning in vision and set the pattern for everything that followed. Its size was the argument: millions of images, hand-labelled across thousands of categories, organised on the WordNet noun hierarchy, built largely through crowdsourced annotation. The annual ImageNet Large Scale Visual Recognition Challenge that ran on a subset of it turned image classification into a public, repeatable, externally scored contest, which is what made the 2012 result legible as a result rather than as a claim. ImageNet is also the origin of a habit the field has not escaped: progress came to be defined as movement on a fixed, public, downloadable test set — a design that rewards optimisation against the set itself and that later benchmarks inherited wholesale.

A technology is not a place. Drawing it on a map would assert something about the world that no stored fact supports.