Inception family of models

GoogLeNet(; pretrain::Bool = false, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an Inception-v1 model (commonly referred to as GoogLeNet) (reference).

Arguments

• pretrain: set to true to load the model with pre-trained weights for ImageNet
• nclasses: the number of output classes
• batchnorm: set to true to use batch normalization after each convolution
• bias: set to true to use bias in the convolution layers

See also Metalhead.googlenet.

Inceptionv3(; pretrain::Bool = false, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an Inception-v3 model (reference).

Arguments

• pretrain: set to true to load the pre-trained weights for ImageNet
• inchannels: number of input channels
• nclasses: the number of output classes

See also Metalhead.inceptionv3.

Inceptionv4(; pretrain::Bool = false, inchannels::Integer = 3,
            nclasses::Integer = 1000)

Creates an Inceptionv4 model. (reference)

Arguments

• pretrain: set to true to load the pre-trained weights for ImageNet
• inchannels: number of input channels.
• nclasses: the number of output classes.

See also Metalhead.inceptionv4.

InceptionResNetv2(; pretrain::Bool = false, inchannels::Integer = 3, 
                  nclasses::Integer = 1000)

Creates an InceptionResNetv2 model. (reference)

Arguments

• pretrain: set to true to load the pre-trained weights for ImageNet
• inchannels: number of input channels.
• nclasses: the number of output classes.

See also Metalhead.inceptionresnetv2.

Xception(; pretrain::Bool = false, inchannels::Integer = 3, nclasses::Integer = 1000)

Creates an Xception model. (reference)

Arguments

• pretrain: set to true to load the pre-trained weights for ImageNet.
• inchannels: number of input channels.
• nclasses: the number of output classes.

See also Metalhead.xception.

googlenet(; dropout_prob = 0.4, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an Inception-v1 model (commonly referred to as GoogLeNet) (reference).

Arguments

• dropout_prob: the dropout probability in the classifier head. Set to nothing to disable dropout.
• inchannels: the number of input channels
• nclasses: the number of output classes
• batchnorm: set to true to include batch normalization after each convolution
• bias: set to true to use bias in the convolution layers

inceptionv3(; dropout_prob = 0.2, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an Inception-v3 model (reference).

Arguments

• dropout_prob: the dropout probability in the classifier head. Set to nothing to disable dropout.
• inchannels: number of input feature maps
• nclasses: the number of output classes

inceptionv4(; dropout_prob = nothing, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an Inceptionv4 model. (reference)

Arguments

• dropout_prob: probability of dropout in classifier head. Set to nothing to disable dropout.
• inchannels: number of input channels.
• nclasses: the number of output classes.

inceptionresnetv2(; inchannels::Integer = 3, dropout_prob = nothing, nclasses::Integer = 1000)

Creates an InceptionResNetv2 model. (reference)

Arguments

• dropout_prob: probability of dropout in classifier head. Set to nothing to disable dropout.
• inchannels: number of input channels.
• nclasses: the number of output classes.

xception(; dropout_prob = nothing, inchannels::Integer = 3, nclasses::Integer = 1000)

Creates an Xception model. (reference)

Arguments

• dropout_prob: probability of dropout in classifier head. Set to nothing to disable dropout.
• inchannels: number of input channels.
• nclasses: the number of output classes.