In 2008 Principal Investigators Li Fei-Fei of Stanford Vision Lab and Kai Li of the Department of Computer Science at Princeton, and associates, advisors and friends, began building ImageNet, an image database and ontology, through a crowdsourcing process. In October 2013 the database contained 14,197,122 images, with 21,841 synsets indexed. Between 2013 when I wrote the entry, and September 2020 when I returned to, the number of images and synsets indexed remained unchanged, causing me to presume that this might be a static project.
The ImageNet database is organized according to the WordNet hierarchy.
"Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a 'synonym set' or 'synset'. There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy."
Among its many applications, ImageNet provides a standard by which the accuracy of image recognition software can be measured.