The Imagenet Large Scale Visual Recognition Challenge (ILSVRC
) is an annual computer vision competition. Each year, teams compete on two tasks. The first is to detect objects within an image coming from 200 classes, which is called object localization. The second is to classify images, each labeled with one of 1000 categories, which is called image classification.
In 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton won the competition by a sizable margin using a convolutional network (ConvNet) named AlexNet. This became a watershed moment for deep learning.
Two years later, Karen Simonyan and Andrew Zisserman won 1st and 2nd place in the two tasks described above. Their model was also a ConvNet named VGG-19. VGG is the acronym for their lab at Oxford (Visual Geometry Group) and 19 is the number of layers in the model with trainable parameters.
What attracted me to this model was its simplicity - the model shares most of the same basic architecture and algorithms as LeNet5, one of the first ConvNets from the 90s. The main difference is the addition of several more layers (from 5 to 19), which seems to validate the idea that deeper networks are able to learn better representations (this trend continues with the introduction of Residual Networks, which won IVCLR the following year with a whopping 152 layers).