A huge set of over 14 million labeled photos, and the yearly contest built around it.
ImageNet is a giant photo dataset released in 2009 by a team led by Fei-Fei Li. It has since grown to more than 14 million photos, each labeled by people with what's in it: "cat", "bicycle" and so on. Labeling that many pictures took the work of a huge number of people recruited online.
From 2010, a yearly contest asked AI systems to sort these photos into 1,000 categories. In 2012 a University of Toronto team using a deep neural network won with a top-5 error of 15.3%, far ahead of second place at 26.2%. That result is often called the start of the deep learning era.
Think of it like this
Think of ImageNet as a gigantic deck of picture flashcards: photo on the front, name on the back. The contest was like a class quiz to see who could name the most cards correctly.
For example
The labels are detailed: even dogs are split into separate breeds.
Records about this
Source-checked moments from AI history.
The turning point at which, with a wide margin over second place, deep CNNs became the mainstream methodology of computer vision
A standard annual competition in large-scale image classification began. It later became the stage for deep learning's breakthrough
Released a large-scale image dataset based on the WordNet hierarchy, creating the recognition that data is the bottleneck