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{{Short description|Image dataset}}
{{Use dmy dates|date=September 2019}}
The '''ImageNet''' project is a large visual [[database]] designed for use in [[Outline of object recognition|visual object recognition software]] research. More than 14 million<ref name="New Scientist">{{cite news|title=New computer vision challenge wants to teach robots to see in 3D|url=https://www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/|access-date=3 February 2018|work=New Scientist|date=7 April 2017}}</ref><ref name="nytimes 2012">{{cite news|last1=Markoff|first1=John|title=For Web Images, Creating New Technology to Seek and Find|url=https://www.nytimes.com/2012/11/20/science/for-web-images-creating-new-technology-to-seek-and-find.html|access-date=3 February 2018|work=The New York Times|date=19 November 2012}}</ref> images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided.<ref>{{Cite web |date=2020-09-07 |title=ImageNet |url=http://image-net.org/about-stats.php |archive-url=https://web.archive.org/web/20200907212153/http://image-net.org/about-stats.php |archive-date=2020-09-07 |access-date=2022-10-11
==Significance for deep learning==
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As an assistant professor at Princeton, Li assembled a team of researchers to work on the ImageNet project. They used [[Amazon Mechanical Turk]] to help with the classification of images.<ref name="Gershgorn"/>
They presented their database for the first time as a poster at the 2009 [[Conference on Computer Vision and Pattern Recognition]] (CVPR) in Florida.<ref name="Gershgorn">{{cite web |url=https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/ |title=The data that transformed AI research—and possibly the world |last=Gershgorn |first=Dave |date=26 July 2017 |website=Quartz |publisher=Atlantic Media Co.|quote=Having read about WordNet's approach, Li met with professor Christiane Fellbaum, a researcher influential in the continued work on WordNet, during a 2006 visit to Princeton. |access-date=26 July 2017 }}</ref><ref>{{Citation |last1=Deng |first1=Jia |last2=Dong |first2=Wei |last3=Socher |first3=Richard |last4=Li |first4=Li-Jia |last5=Li |first5=Kai |last6=Fei-Fei |first6=Li |contribution=ImageNet: A Large-Scale Hierarchical Image Database |year=2009 |title=2009 conference on Computer Vision and Pattern Recognition |contribution-url=http://www.image-net.org/papers/imagenet_cvpr09.pdf }}</ref><ref>{{Citation|last=Li|first=Fei-Fei|title=How we're teaching computers to understand pictures|date=23 March 2015 |url=https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures?language=en|access-date=16 December 2018}}</ref>
==Dataset==
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== Subsets of the Dataset ==
There are various subsets of the ImageNet dataset used in various context. One of the most highly used subset of ImageNet is the "ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012-2017 image classification and localization dataset". This is also referred to in the research literature as ImageNet-1K or ILSVRC2017, reflecting the original ILSVRC challenge that involved 1,000 classes. ImageNet-1K contains 1,281,167 training images, 50,000 validation images and 100,000 test images.<ref>{{Cite web |title=ImageNet |url=https://www.image-net.org/download.php |access-date=2022-10-19 |website=www.image-net.org}}</ref> The full original dataset is referred to as ImageNet-21K. ImageNet-21k contains 14,197,122 images divided into 21,841 classes. Some papers round this up and name it ImageNet-22k.<ref>{{cite
==History of the ImageNet challenge==
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