MobileNet family of models

MobileNetv1(width_mult::Real = 1; pretrain::Bool = false,
            inchannels::Integer = 3, nclasses::Integer = 1000)

Create a MobileNetv1 model with the baseline configuration (reference).

Arguments

• width_mult: Controls the number of output feature maps in each block (with 1 being the default in the paper; this is usually a value between 0.1 and 1.4)
• pretrain: Whether to load the pre-trained weights for ImageNet
• inchannels: The number of input channels.
• nclasses: The number of output classes

See also Metalhead.mobilenetv1.

MobileNetv2(width_mult = 1.0; inchannels::Integer = 3, pretrain::Bool = false,
            nclasses::Integer = 1000)

Create a MobileNetv2 model with the specified configuration. (reference).

Arguments

• width_mult: Controls the number of output feature maps in each block (with 1 being the default in the paper; this is usually a value between 0.1 and 1.4)
• pretrain: Whether to load the pre-trained weights for ImageNet
• inchannels: The number of input channels.
• nclasses: The number of output classes

See also Metalhead.mobilenetv2.

MobileNetv3(config::Symbol; width_mult::Real = 1, pretrain::Bool = false,
            inchannels::Integer = 3, nclasses::Integer = 1000)

Create a MobileNetv3 model with the specified configuration. (reference). Set pretrain = true to load the model with pre-trained weights for ImageNet.

Arguments

• config: :small or :large for the size of the model (see paper).
• width_mult: Controls the number of output feature maps in each block (with 1 being the default in the paper; this is usually a value between 0.1 and 1.4)
• pretrain: whether to load the pre-trained weights for ImageNet
• inchannels: number of input channels
• nclasses: the number of output classes

See also Metalhead.mobilenetv3.

MNASNet(config::Symbol; width_mult::Real = 1, pretrain::Bool = false,
        inchannels::Integer = 3, nclasses::Integer = 1000)

Creates a MNASNet model with the specified configuration. (reference)

Arguments

• config: configuration of the model. One of B1, A1 or small. B1 is without squeeze-and-excite layers, A1 is with squeeze-and-excite layers, and small is a smaller version of A1.
• width_mult: Controls the number of output feature maps in each block (with 1 being the default in the paper; this is usually a value between 0.1 and 1.4)
• pretrain: Whether to load the pre-trained weights for ImageNet
• inchannels: The number of input channels.
• nclasses: The number of output classes

See also Metalhead.mnasnet.