Hybrid CNN architectures

ConvMixer(config::Symbol; pretrain::Bool = false, inchannels::Integer = 3,
          nclasses::Integer = 1000)

Creates a ConvMixer model. (reference)

Arguments

• config: the size of the model, either :base, :small or :large
• pretrain: whether to load the pre-trained weights for ImageNet
• inchannels: number of input channels
• nclasses: number of classes in the output

ConvMixer does not currently support pretrained weights.

See also Metalhead.convmixer.

ConvNeXt(config::Symbol; pretrain::Bool = true, inchannels::Integer = 3,
         nclasses::Integer = 1000)

Creates a ConvNeXt model. (reference)

Arguments

• config: The size of the model, one of tiny, small, base, large or xlarge.
• pretrain: set to true to load pre-trained weights for ImageNet
• inchannels: number of input channels
• nclasses: number of output classes

See also Metalhead.convnext.

convmixer(planes::Integer, depth::Integer; kernel_size::Dims{2} = (9, 9),
          patch_size::Dims{2} = (7, 7), activation = gelu,
          inchannels::Integer = 3, nclasses::Integer = 1000)

Creates a ConvMixer model. (reference)

Arguments

• planes: number of planes in the output of each block
• depth: number of layers
• kernel_size: kernel size of the convolutional layers
• patch_size: size of the patches
• activation: activation function used after the convolutional layers
• inchannels: number of input channels
• nclasses: number of classes in the output

convnext(config::Symbol; stochastic_depth_prob = 0.0, layerscale_init = 1.0f-6,
         inchannels::Integer = 3, nclasses::Integer = 1000)

Creates a ConvNeXt model. (reference)

Arguments

• config: The size of the model, one of tiny, small, base, large or xlarge.
• stochastic_depth_prob: Stochastic depth probability.
• layerscale_init: Initial value for LayerScale (reference)
• inchannels: number of input channels.
• nclasses: number of output classes