The resulting annual competition is now known as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). To "democratize" ImageNet, Fei-Fei Li proposed to the PASCAL VOC team a collaboration, beginning in 2010, where research teams wouldĮvaluate 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. History of the ImageNet challenge Įrror rate history on ImageNet (showing best result per team and up to 10 entries per year) The average worker identified 50 images per minute. 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. ImageNet uses a variant of the broad WordNet schema to categorize objects, augmented with 120 categories of dog breeds to showcase fine-grained classification. Object-level annotations provide a bounding box around the (visible part of the) indicated object. 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". ImageNet crowdsources its annotation process. They presented their database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida. They used Amazon Mechanical Turk to help with the classification of images. Īs an assistant professor at Princeton, Li assembled a team of researchers to work on the ImageNet project. As a result of this meeting, Li went on to build ImageNet starting from the word database of WordNet and using many of its features. In 2007, Li met with Princeton professor Christiane Fellbaum, one of the creators of WordNet, to discuss the project. At a time when most AI research focused on models and algorithms, Li wanted to expand and improve the data available to train AI algorithms. History of the database ĪI researcher Fei-Fei Li began working on the idea for ImageNet in 2006. In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest. According to The Economist, "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole." This was made feasible due to the use of graphics processing units (GPUs) during training, an essential ingredient of the deep learning revolution. If you found any image copyrighted to yours, Please contact us, so we can remove it or mention its authors name.On 30 September 2012, a convolutional neural network (CNN) called AlexNet achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge, more than 10.8 percentage points lower than that of the runner up. Īll images remain property of their original owners.