While there are images all over the Internet, there are very few good ways of searching for them.
The way it works now is that you type in a word, and if you’re lucky and a person somewhere online has uploaded an image labeled with your search word, then you’ll see that image in your search results.
But what if the search engine didn’t have to rely on people labeling images correctly? What if the computer could just recognize images itself, the way people do?
To develop a system that can do that, Stanford computer scientist Fei-Fei Li created the world’s largest visual database, ImageNet, which holds 14 million labeled images.
A number of researchers have used the database to test their images as it developed over the last few years. This summer, when two Google researchers tested it they found it worked twice as well as other “neural networks,” or systems that try to mimic human brain functions.
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Direct to ImageNet
The database can be keyword search. Open to all users.
At the present time that database provides access to 14,197,122 images.