VGG implementation

Source: R/models-vgg.R

VGG models implementations based on Very Deep Convolutional Networks For Large-Scale Image Recognition

model_vgg11(pretrained = FALSE, progress = TRUE, ...)

model_vgg11_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg13(pretrained = FALSE, progress = TRUE, ...)

model_vgg13_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg16(pretrained = FALSE, progress = TRUE, ...)

model_vgg16_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg19(pretrained = FALSE, progress = TRUE, ...)

model_vgg19_bn(pretrained = FALSE, progress = TRUE, ...)

Arguments

pretrained

(bool): If TRUE, returns a model pre-trained on ImageNet

progress

(bool): If TRUE, displays a progress bar of the download to stderr

...

other parameters passed to the VGG model implementation.

Functions

  • model_vgg11(): VGG 11-layer model (configuration "A")

  • model_vgg11_bn(): VGG 11-layer model (configuration "A") with batch normalization

  • model_vgg13(): VGG 13-layer model (configuration "B")

  • model_vgg13_bn(): VGG 13-layer model (configuration "B") with batch normalization

  • model_vgg16(): VGG 13-layer model (configuration "D")

  • model_vgg16_bn(): VGG 13-layer model (configuration "D") with batch normalization

  • model_vgg19(): VGG 19-layer model (configuration "E")

  • model_vgg19_bn(): VGG 19-layer model (configuration "E") with batch normalization