Computer Vision Models (Vision API)

Native Lux Models

# Boltz.Vision.VGG — Function.

VGG(imsize; config, inchannels, batchnorm = false, nclasses, fcsize, dropout)

Create a VGG model [1].

Arguments

• imsize: input image width and height as a tuple

• config: the configuration for the convolution 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

• dropout: dropout level between fully connected layers

References

[1] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

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VGG(depth::Int; batchnorm=false, kwargs...)

Create a VGG model [1] with ImageNet Configuration.

Arguments

• depth::Int: the depth of the VGG model. Choices: {11, 13, 16, 19}.

Keyword Arguments

• batchnorm = false: set to true to use batch normalization after each convolution.

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

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# Boltz.Vision.VisionTransformer — Function.

VisionTransformer(name::Symbol; kwargs...)

Creates a Vision Transformer model with the specified configuration.

Arguments

• name::Symbol: name of the Vision Transformer model to create. The following models are available:

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

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Imported from Metalhead.jl

Tip

You need to load Flux and Metalhead before using these models.

# Boltz.Vision.AlexNet — Function.

AlexNet(; kwargs...)

Create an AlexNet model [1]

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.

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# Boltz.Vision.ConvMixer — Function.

ConvMixer(name::Symbol; kwargs...)

Create a ConvMixer model [1].

Arguments

• name::Symbol: The name of the ConvMixer model. Must be one of :base, :small, or :large.

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Zhu, Zhuoyuan, et al. "ConvMixer: A Convolutional Neural Network with Faster Depth-wise Convolutions for Computer Vision." arXiv preprint arXiv:1911.11907 (2019).

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# Boltz.Vision.DenseNet — Function.

DenseNet(depth::Int; kwargs...)

Create a DenseNet model [1].

Arguments

• depth::Int: The depth of the DenseNet model. Must be one of 121, 161, 169, or 201.

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

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# Boltz.Vision.GoogLeNet — Function.

GoogLeNet(; kwargs...)

Create a GoogLeNet model [1].

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

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# Boltz.Vision.MobileNet — Function.

MobileNet(name::Symbol; kwargs...)

Create a MobileNet model [1, 2, 3].

Arguments

• name::Symbol: The name of the MobileNet model. Must be one of :v1, :v2, :v3_small, or :v3_large.

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017). [2] Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [3] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam. "Searching for MobileNetV3." arXiv preprint arXiv:1905.02244. 2019.

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# Boltz.Vision.ResNet — Function.

ResNet(depth::Int; kwargs...)

Create a ResNet model [1].

Arguments

• depth::Int: The depth of the ResNet model. Must be one of 18, 34, 50, 101, or 152.

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

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# Boltz.Vision.ResNeXt — Function.

ResNeXt(depth::Int; kwargs...)

Create a ResNeXt model [1].

Arguments

• depth::Int: The depth of the ResNeXt model. Must be one of 50, 101, or 152.

Keyword Arguments

• pretrained::Bool=false: If true, returns a pretrained model.

• rng::Union{Nothing, AbstractRNG}=nothing: Random number generator.

• seed::Int=0: Random seed.

• initialized::Val{Bool}=Val(true): If Val(true), returns (model, parameters, states), otherwise just model.

References

[1] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He, Ross Gorshick, and Piotr Dollár. "Aggregated residual transformations for deep neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

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Pretrained Models

Tip

Pass pretrained=true to the model constructor to load the pretrained weights.

MODEL	TOP 1 ACCURACY (%)	TOP 5 ACCURACY (%)
AlexNet()	54.48	77.72
VGG(11)	67.35	87.91
VGG(13)	68.40	88.48
VGG(16)	70.24	89.80
VGG(19)	71.09	90.27
VGG(11; batchnorm=true)	69.09	88.94
VGG(13; batchnorm=true)	69.66	89.49
VGG(16; batchnorm=true)	72.11	91.02
VGG(19; batchnorm=true)	72.95	91.32

Preprocessing

All the pretrained models require that the images be normalized with the parameters mean = [0.485f0, 0.456f0, 0.406f0] and std = [0.229f0, 0.224f0, 0.225f0].