ImageNet
Large labeled image database used to train and benchmark visual recognition models; its annual competition drove major advances in deep learning.
3 milestones
Deep residual networks make training at 100+ layers practical with identity shortcuts
Kaiming He and colleagues introduced residual learning, letting networks train at depths of up to 152 layers. Their ResNet won first place on five tracks at the ILSVRC and COCO 2015 competitions, including ImageNet classification, detection, localisation, and COCO detection and segmentation.
AlexNet and Deep Convolutional Neural Networks in Large-Scale Image Classification
In September 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton at the University of Toronto submitted a paper describing AlexNet, a deep convolutional neural network that achieved a top-5 error rate of 15.3% on the ImageNet Large Scale Visual Recognition Challenge, outperforming the next-best entry by more than 10 percentage points.
ImageNet: a large-scale hierarchical image database for object recognition
Researchers led by Li Fei-Fei introduced ImageNet, a dataset organised around more than 100,000 concept categories and aimed at providing roughly 1,000 human-annotated images per category, on which the ILSVRC challenge was later built.