ImageNet

The ImageNet project is a large visual database designed for use in visual object recognition software research. As of 2016, over ten million 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.[1] The database of annotations of third-party image URL's is freely available directly from ImageNet; however, the actual images are not owned by ImageNet.[2] 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.

Dataset

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.[3]

ImageNet Challenge

Since 2010, the annual Imagenet Large Scale Visual Recognition Challenge (ILSVCR) is a competition where research teams submit programs that classify and detect objects and scenes. The ILSVCR 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.[3] The 2010s saw dramatic progress in image processing. Around 2011, a good ILSVCR 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.[4] By 2015, researchers reported that software exceeded human ability at the narrow ILSVCR tasks.[5] However, as one of the challenge’s organisers, 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.[6]

By 2014, over fifty institutions participated in the ILJVRC.[3] In 2015, Baidu scientists were banned for a year for using different accounts to greatly exceed the specified limit of two submissions per week.[7][8] Baidu later stated that it fired the team leader involved and that it would establish a scientific advisory panel.[9]

See also

Notes

  1. ↑ "ImageNet Summary and Statistics". ImageNet. Retrieved 22 June 2016.
  2. ↑ "ImageNet Overview". ImageNet. Retrieved 22 June 2016.
  3. 1 2 3 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.
  4. ↑ 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.
  5. ↑ Markoff, John (10 December 2015). "A Learning Advance in Artificial Intelligence Rivals Human Abilities". The New York Times. Retrieved 22 June 2016.
  6. ↑ Aron, Jacob (21 September 2015). "Forget the Turing test – there are better ways of judging AI". New Scientist. Retrieved 22 June 2016.
  7. ↑ 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.
  8. ↑ "Chinese search giant Baidu disqualified from AI test". BBC News. 14 June 2015. Retrieved 22 June 2016.
  9. ↑ "Baidu fires researcher involved in AI contest flap". PCWorld. 11 June 2015. Retrieved 22 June 2016.

External links

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