EfficientNet family of models
This is the API reference for the EfficientNet family of models supported by Metalhead.jl.
The higher-level model constructors
Metalhead.EfficientNet
— TypeEfficientNet(config::Symbol; pretrain::Bool = false, inchannels::Integer = 3,
nclasses::Integer = 1000)
Create an EfficientNet model (reference).
Arguments
config
: size of the model. Can be one of[:b0, :b1, :b2, :b3, :b4, :b5, :b6, :b7, :b8]
.pretrain
: set totrue
to load the pre-trained weights for ImageNetinchannels
: number of input channels.nclasses
: number of output classes.
EfficientNet does not currently support pretrained weights.
See also Metalhead.efficientnet
.
Metalhead.EfficientNetv2
— TypeEfficientNetv2(config::Symbol; pretrain::Bool = false, inchannels::Integer = 3,
nclasses::Integer = 1000)
Create an EfficientNetv2 model (reference).
Arguments
config
: size of the network (one of[:small, :medium, :large, :xlarge]
)pretrain
: whether to load the pre-trained weights for ImageNetinchannels
: number of input channelsnclasses
: number of output classes
EfficientNetv2
does not currently support pretrained weights.
See also efficientnet
.
The mid-level functions
Metalhead.efficientnet
— Functionefficientnet(config::Symbol; norm_layer = BatchNorm, stochastic_depth_prob = 0.2,
dropout_prob = nothing, inchannels::Integer = 3, nclasses::Integer = 1000)
Create an EfficientNet model. (reference).
Arguments
config
: size of the model. Can be one of[:b0, :b1, :b2, :b3, :b4, :b5, :b6, :b7, :b8]
.norm_layer
: normalization layer to use.stochastic_depth_prob
: probability of stochastic depth. Set tonothing
to disable stochastic depth.dropout_prob
: probability of dropout in the classifier head. Set tonothing
to disable dropout.inchannels
: number of input channels.nclasses
: number of output classes.
Metalhead.efficientnetv2
— Functionefficientnetv2(config::Symbol; norm_layer = BatchNorm, stochastic_depth_prob = 0.2,
dropout_prob = nothing, inchannels::Integer = 3, nclasses::Integer = 1000)
Create an EfficientNetv2 model. (reference).
Arguments
config
: size of the network (one of[:small, :medium, :large, :xlarge]
)norm_layer
: normalization layer to use.stochastic_depth_prob
: probability of stochastic depth. Set tonothing
to disable stochastic depth.dropout_prob
: probability of dropout in the classifier head. Set tonothing
to disable dropout.inchannels
: number of input channels.nclasses
: number of output classes.