EfficientNet.jl

EfficientNet implementation in Julia.

Install

]add https://github.com/pxl-th/EfficientNet.jl.git

Construct model

model = EffNet("efficientnet-b0"; n_classes=10, in_channels=3)

ImageNet pretrained model

model = EfficientNet.from_pretrained("efficientnet-b3")

By default, weights are stored in ~/.cache/EfficientNet.jl/ directory. If there are no weights, it will attempt to download them. Additionally, you can specify cache directory as a second parameter to from_pretrained function.

Available pretrained models are B0-B7. They are loaded from EfficientNet-PyTorch and converted to Julia's format.

Example inference on image

using Images
using EfficientNet

device = gpu
model = EfficientNet.from_pretrained("efficientnet-b3")
model = model |> testmode! |> device

image = "./spaceshuttle.png"
x = Images.load(image) |> channelview .|> Float32
x = Flux.unsqueeze(permutedims(x, (3, 2, 1)), 4)

o = x |> device |> model |> softmax |> cpu
o = sortperm(o[:, 1])
@info "Top 5 classes: $(o[end:-1:end - 5] .- 1)"

Feature extraction

To extract list of features, pass Val(:stages) as the second parameter:

features = model(x, Val(:stages))

It will contain features, extracted from different resolution levels. This can be used in something like UNet architecture as an encoder. To get those resolution levels, use model.stages and model.stages_channels to get ids of specific layers and output channels.