# vgg11_bn

Implementation of VGG proposed in Very Deep Convolutional Networks For Large-Scale Image Recognition

VGG.vgg11()
VGG.vgg13()
VGG.vgg16()
VGG.vgg19()
VGG.vgg11_bn()
VGG.vgg13_bn()
VGG.vgg16_bn()
VGG.vgg19_bn()


Please be aware that the [bn]{.title-ref} models uses BatchNorm but they are very old and people back then don't know the bias is superfluous in a conv followed by a batchnorm.

Examples:

# change activation
VGG.vgg11(activation = nn.SELU)
# change number of classes (default is 1000 )
VGG.vgg11(n_classes=100)
# pass a different block
from nn.models.classification.senet import SENetBasicBlock
VGG.vgg11(block=SENetBasicBlock)
# store the features tensor after every block