VGGNet - Wikipedia

The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family includes various configurations with different depths, denoted by the letter "VGG" followed by the number of weight layers. The most common ones are VGG-16 (13 convolutional layers + 3 fully connected layers, 138M parameters) and VGG-19 (16 + 3, 144M parameters).[1] The VGG family were widely applied in various computer vision areas.[2] An ensemble model of VGGNets achieved state-of-the-art results in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014.[1][3] It was used as a baseline comparison in the ResNet paper for image classification,[4] as the network in the Fast Region-based CNN for object detection, and as a base network in neural style transfer.[5] The series was historically important as an early influential model designed by composing generic modules, whereas AlexNet (2012) was designed "from scratch". I

VGGNet - Wikipedia Jump to content From Wikipedia, the free encyclopedia Series of convolutional neural networks for image classification VGGNet Developer Visual Geometry Group Release September 4, 2014 ; 11 years ago ( 2014-09-04 ) Written in Caffe Type Convolutional neural network Deep neural network License CC BY 4.0 Website www .robots .ox .ac .uk /~vgg /research /very _deep / VGG module architecture compared to AlexNet architecture The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford . The VGG family incl

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