Other models

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

Create a AlexNet. (reference).

Arguments

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

See also alexnet.

VGG(imsize::Dims{2}; config, inchannels, batchnorm = false, nclasses, fcsize, dropout_prob)

Construct a VGG model with the specified input image size. Typically, the image size is (224, 224).

Keyword Arguments:

• config : VGG convolutional block configuration. It is defined as a vector of tuples (output_channels, num_convolutions) for each block
• inchannels: number of input channels
• batchnorm: set to true to use batch normalization after each convolution
• nclasses: number of output classes
• fcsize: intermediate fully connected layer size (see Metalhead.vgg_classifier_layers)
• dropout_prob: dropout level between fully connected layers

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

Create a SqueezeNet (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 squeezenet.

UNet(imsize::Dims{2} = (256, 256), inchannels::Integer = 3, outplanes::Integer = 3,
     encoder_backbone = Metalhead.backbone(DenseNet(121)); pretrain::Bool = false)

Creates a UNet model with an encoder built of specified backbone. By default it uses DenseNet backbone, but any ResNet-like Metalhead model can be used for the encoder. (reference).

Arguments

• imsize: size of input image
• inchannels: number of channels in input image
• outplanes: number of output feature planes.
• encoder_backbone: The backbone layers of specified model to be used as encoder. For example, Metalhead.backbone(Metalhead.ResNet(18)) can be passed to instantiate a UNet with layers of resnet18 as encoder.
• pretrain: Whether to load the pre-trained weights for ImageNet

See also Metalhead.unet.

alexnet(; dropout_prob = 0.5, inchannels::Integer = 3, nclasses::Integer = 1000)

Create an AlexNet model (reference).

Arguments

• dropout_prob: dropout probability for the classifier
• inchannels: The number of input channels.
• nclasses: the number of output classes

vgg(imsize; config, inchannels, batchnorm = false, nclasses, fcsize, dropout_prob)

Create a VGG model (reference).

Arguments

• imsize: input image width and height as a tuple
• config: the configuration for the convolution layers (see Metalhead.vgg_convolutional_layers)
• inchannels: number of input channels
• batchnorm: set to true to use batch normalization after each convolution
• nclasses: number of output classes
• fcsize: intermediate fully connected layer size (see Metalhead.vgg_classifier_layers)
• dropout_prob: dropout level between fully connected layers

squeezenet(; dropout_prob = 0.5, inchannels::Integer = 3, nclasses::Integer = 1000)

Create a SqueezeNet model. (reference).

Arguments

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

unet(encoder_backbone, imgdims, outplanes::Integer, final::Any = unet_final_block,
     fdownscale::Integer = 0)

Creates a UNet model with specified convolutional backbone. Backbone of any Metalhead ResNet-like model can be used as encoder (reference).

Arguments

- `encoder_backbone`: The backbone layers of specified model to be used as encoder.
	For example, `Metalhead.backbone(Metalhead.ResNet(18))` can be passed 
	to instantiate a UNet with layers of resnet18 as encoder.
- `inputsize`: size of input image
- `outplanes`: number of output feature planes
- `final`: final block as described in original paper
- `fdownscale`: downscale factor