ImageNet
In plain wordsA huge collection of more than 14 million labeled photos, sorted into object categories, whose yearly recognition contest from 2010 to 2017 sparked the deep learning boom.
A datasetdataset, n.: An organized collection of examples gathered for a purpose — such as photos with their captions, or questions with answers — used to teach or test an AI system. of more than 14 million images labeled with nearly 22,000 object categories taken from the noun hierarchy of the WordNet lexical database, built by Fei-Fei LiFei-Fei Li, prop. n.: A Chinese-American computer scientist whose ImageNet project, millions of hand-labeled photos, gave deep learning the proving ground that launched its boom; she now champions 'human-centered' AI.'s group at Princeton and Stanford from 2007 using images gathered from the web and labeled by crowdworkerscrowdsourcing, n.: Getting many people over the internet to each do a small piece of a big job, such as labeling pictures for a few cents apiece, with anyone free to join in. on Amazon Mechanical Turk. Its subset for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC, 2010–2017) — about 1.28 million training images in 1,000 classes, often called ImageNet-1k — became computer vision's central benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time.. In 2012 AlexNetAlexNet, prop. n.: The neural network that won a major image-recognition contest by a huge margin in 2012, convincing researchers and companies that deep learning trained on graphics chips was the way forward. cut the winning top-5 classification error from about 26 percent to 15.3 percent, triggering the deep learning revolutiondeep learning revolution, n.: The rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems.; by 2015 residual networks reached 3.6 percent, below an estimated human error of about 5 percent, and the challenge ended in 2017. Pretraining on ImageNet was the standard starting point for vision models until web-scale image–text data took over.
We initialized the detector from an ImageNet-pretrained backbone.
Origin
Conceived by Fei-Fei Li in 2006–2007 and first presented as a poster at CVPR 2009 by Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li. Its premise, that progress was limited by data rather than algorithms, was unfashionable at the time.
Usage note
Audits found offensive and biased labels in its 'person' categories; in 2019–2020 the maintainers removed many of them, and in 2021 they released a version of the challenge images with faces blurred for privacy.
Related
- AlexNetAlexNet, prop. n.: The neural network that won a major image-recognition contest by a huge margin in 2012, convincing researchers and companies that deep learning trained on graphics chips was the way forward.
- deep learning revolutiondeep learning revolution, n.: The rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems.
- Fei-Fei LiFei-Fei Li, prop. n.: A Chinese-American computer scientist whose ImageNet project, millions of hand-labeled photos, gave deep learning the proving ground that launched its boom; she now champions 'human-centered' AI.
- benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time.
- image classificationimage classification, n.: Teaching a computer to say what a picture shows by choosing a label, such as 'cat', 'pneumonia' or 'stop sign', from a fixed list of possibilities.
- MNISTMNIST, prop. n.: A famous collection of 70,000 small images of handwritten digits used to test image-recognition programs. Getting a model to read MNIST digits is the 'hello world' of machine learning.
- crowdsourcingcrowdsourcing, n.: Getting many people over the internet to each do a small piece of a big job, such as labeling pictures for a few cents apiece, with anyone free to join in.
Sources
- Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K. & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. CVPR 2009.
- Russakovsky, O. et al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115(3), 211–252.
Mentioned in
benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time., computer visioncomputer vision, n.: The part of AI that lets computers make sense of images and video: recognizing faces, reading signs, spotting tumors on scans or judging how far away a car is., datasetdataset, n.: An organized collection of examples gathered for a purpose — such as photos with their captions, or questions with answers — used to teach or test an AI system., image classificationimage classification, n.: Teaching a computer to say what a picture shows by choosing a label, such as 'cat', 'pneumonia' or 'stop sign', from a fixed list of possibilities., AlexNetAlexNet, prop. n.: The neural network that won a major image-recognition contest by a huge margin in 2012, convincing researchers and companies that deep learning trained on graphics chips was the way forward., backbonebackbone, n.: The main, usually pretrained part of a vision model that turns an image into useful internal features; smaller task-specific parts are attached to it, like limbs to a spine., CLIPCLIP, prop. n.: An OpenAI model that learned to match pictures with their captions from hundreds of millions of web examples, so it can judge how well any piece of text describes any image., COCOCOCO, prop. n.: A widely used collection of everyday photos in which people outlined and named the objects, described each picture in sentences and marked body joints; it is used to train and compare vision systems., crowdsourcingcrowdsourcing, n.: Getting many people over the internet to each do a small piece of a big job, such as labeling pictures for a few cents apiece, with anyone free to join in., CVPRCVPR, prop. n.: The leading yearly conference on computer vision — teaching computers to understand images and video — and one of the most cited publication venues in any field of science., deep learning revolutiondeep learning revolution, n.: The rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems., feature extractionfeature extraction, n.: Turning raw data such as pixels or sound into a short list of telling numbers — computed by hand-designed rules or by an AI system trained earlier — that a new model can learn from or compare., Fei-Fei LiFei-Fei Li, prop. n.: A Chinese-American computer scientist whose ImageNet project, millions of hand-labeled photos, gave deep learning the proving ground that launched its boom; she now champions 'human-centered' AI., godfathers of AIgodfathers of AI, phr.: A media nickname for three scientists — Geoffrey Hinton, Yoshua Bengio and Yann LeCun — whose decades of work on neural networks underpins modern AI. They shared computing's top prize in 2019., Kaiming HeKaiming He, prop. n.: A Chinese computer scientist whose ResNet design, with shortcut connections that let networks grow hundreds of layers deep, is among the most cited research in all of science., Microsoft ResearchMicrosoft Research, prop. n.: Microsoft's research division, with laboratories on several continents, whose scientists have produced influential work on speech, vision and language AI., MNISTMNIST, prop. n.: A famous collection of 70,000 small images of handwritten digits used to test image-recognition programs. Getting a model to read MNIST digits is the 'hello world' of machine learning., R-CNNR-CNN, prop. n.: A family of object detectors that first pick out image regions likely to contain objects, then use a neural network to label each region and tighten its box., ResNetResNet, prop. n.: A family of image-recognition networks from 2015 whose shortcut connections allowed far deeper networks than before, over 100 layers, and became a standard building block across AI., WordNetWordNet, prop. n.: A hand-made map of English vocabulary in which words sharing a meaning are bundled together and the bundles are linked — recording, say, that a dog is a kind of animal and a wheel is part of a car., Alex KrizhevskyAlex Krizhevsky, prop. n.: A Canadian computer scientist who built AlexNet, the image-recognition network that was trained on gaming graphics chips and whose 2012 contest win launched the deep learning boom., Amazon Mechanical TurkAmazon Mechanical Turk, prop. n.: A website Amazon opened in 2005 that acts as a hiring hall for tiny online jobs, such as labeling images for AI, which registered workers pick up and finish for a few cents each., CaffeCaffe, prop. n.: An early free deep-learning toolkit from the University of California, Berkeley, popular for image recognition in the mid-2010s, in which networks were described in simple text files rather than code., Kate CrawfordKate Crawford, prop. n.: An Australian scholar who studies the social, political and environmental costs of AI, co-founded the AI Now Institute, and wrote 'Atlas of AI'.