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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. 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. 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 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.
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.
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. 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. By 2015, researchers reported that software exceeded human ability at the narrow ILSVRC tasks. 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.
By 2014, over fifty institutions participated in the ILSVRC. In 2015, Baidu scientists were banned for a year for using different accounts to greatly exceed the specified limit of two submissions per week. Baidu later stated that it fired the team leader involved and that it would establish a scientific advisory panel.
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