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The ImageNet project is a large visual database designed for use in visual object recognition software research. Over 14 million[1][2] URLs of images have been hand-annotated by ImageNet to indicate what objects are pictured; in at least one million of the images, bounding boxes are also provided.[3] ImageNet contains over 20 thousand categories;[2] a typical category, such as "balloon" or "strawberry", contains several hundred images.[4] The database of annotations of third-party image URLs is freely available directly from ImageNet; however, the actual images are not owned by ImageNet.[5] Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The ImageNet Challenge uses a "trimmed" list of one thousand non-overlapping classes.[6]

A dramatic 2012 breakthrough in solving the ImageNet Challenge is widely considered to be the beginning of the deep learning revolution of the 2010s: "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole."[4][7]


The database was presented for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida by researchers from the Computer Science department at Princeton University.[8][9]

Error rate history on ImageNet (showing best result per team and up to 10 entries per year)


ImageNet crowdsources its annotation process. Image-level annotations indicate the presence or absence of an object class in an image, such as "there are tigers in this image" or "there are no tigers in this image". Object-level annotations provide a bounding box around the (visible part of the) indicated object. ImageNet uses a variant of the broad WordNet schema to categorize objects, augmented with 120 categories of dog breeds to showcase fine-grained classification.[6] One downside of WordNet use is the categories may be more "elevated" than would be optimal for ImageNet: "Most people are more interested in Lady Gaga or the iPod Mini than in this rare kind of diplodocus." In 2012 ImageNet was the world's largest academic user of Mechanical Turk. The average worker identified 50 images per minute.[2]

ImageNet Challenge[edit]

Since 2010, the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is a competition where research teams evaluate their algorithms on the given data set, and compete to achieve higher accuracy on several visual recognition tasks. The ILSVRC aims to "follow in the footsteps" of the smaller-scale PASCAL VOC challenge, established in 2005, which contained only about 20,000 images and twenty object classes.[6] The ILSVRC uses a "trimmed" list of only 1000 image categories or "classes", including 90 of the 120 dog breeds classified by the full ImageNet schema.[6] The 2010s saw dramatic progress in image processing. Around 2011, a good ILSVRC classification error rate was 25%. In 2012, a deep convolutional neural net achieved 16%; in the next couple of years, error rates fell to a few percent.[10] While the 2012 breakthrough "combined pieces that were all there before", the dramatic quantitative improvement marked the start of an industry-wide artificial intelligence boom.[4] By 2015, researchers reported that software exceeded human ability at the narrow ILSVRC tasks.[11] However, as one of the challenge's organizers, Olga Russakovsky, pointed out in 2015, the programs only have to identify images as belonging to one of a thousand categories; humans can recognize a larger number of categories, and also (unlike the programs) can judge the context of an image.[12]

By 2014, over fifty institutions participated in the ILSVRC.[6] In 2015, Baidu scientists were banned for a year for using different accounts to greatly exceed the specified limit of two submissions per week.[13][14] Baidu later stated that it fired the team leader involved and that it would establish a scientific advisory panel.[15]

In 2017, 29 of 38 competing teams got less than 5% wrong.[16] In 2017 ImageNet stated it would roll out a new, much more difficult, challenge in 2018 that involves classifying 3D objects using natural language. Because creating 3D data is more costly than annotating a pre-existing 2D image, the dataset is expected to be smaller. The applications of progress in this area would range from robotic navigation to augmented reality.[17]

Non-competition results[edit]

Around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and COCO. According to Google, NASNet's performance exceeded all previously published ImageNet performance.[18]

See also[edit]


  1. ^ "New computer vision challenge wants to teach robots to see in 3D". New Scientist. 7 April 2017. Retrieved 3 February 2018.
  2. ^ a b c Markoff, John (19 November 2012). "For Web Images, Creating New Technology to Seek and Find". The New York Times. Retrieved 3 February 2018.
  3. ^ "ImageNet Summary and Statistics". ImageNet. Retrieved 22 June 2016.
  4. ^ a b c "From not working to neural networking". The Economist. 25 June 2016. Retrieved 3 February 2018.
  5. ^ "ImageNet Overview". ImageNet. Retrieved 22 June 2016.
  6. ^ a b c d e Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.
  7. ^ "Machines 'beat humans' for a growing number of tasks". Financial Times. 30 November 2017. Retrieved 3 February 2018.
  8. ^ Gershgorn, Dave (2017-07-26). "The data that transformed AI research—and possibly the world". Quartz. Atlantic Media Co. Retrieved 2017-07-26.
  9. ^ Deng, Jia; Dong, Wei; Socher, Richard; Li, Li-Jia; Li, Kai; Fei-Fei, Li (2009), "ImageNet: A Large-Scale Hierarchical Image Database" (PDF), 2009 conference on Computer Vision and Pattern Recognition
  10. ^ Robbins, Martin (6 May 2016). "Does an AI need to make love to Rembrandt's girlfriend to make art?". The Guardian. Retrieved 22 June 2016.
  11. ^ Markoff, John (10 December 2015). "A Learning Advance in Artificial Intelligence Rivals Human Abilities". The New York Times. Retrieved 22 June 2016.
  12. ^ Aron, Jacob (21 September 2015). "Forget the Turing test – there are better ways of judging AI". New Scientist. Retrieved 22 June 2016.
  13. ^ Markoff, John (3 June 2015). "Computer Scientists Are Astir After Baidu Team Is Barred From A.I. Competition". The New York Times. Retrieved 22 June 2016.
  14. ^ "Chinese search giant Baidu disqualified from AI test". BBC News. 14 June 2015. Retrieved 22 June 2016.
  15. ^ "Baidu fires researcher involved in AI contest flap". PCWorld. 11 June 2015. Retrieved 22 June 2016.
  16. ^ Gershgorn, Dave (10 September 2017). "The Quartz guide to artificial intelligence: What is it, why is it important, and should we be afraid?". Quartz. Retrieved 3 February 2018.
  17. ^ "New computer vision challenge wants to teach robots to see in 3D". New Scientist. 7 April 2017. Retrieved 3 February 2018.
  18. ^ "Google AI creates its own 'child' bot". The Independent. 5 December 2017. Retrieved 5 February 2018.

External links[edit]