Pkg.add("Metalhead")This package provides computer vision models that run on top of the Flux machine learning library.
Each model (like VGG19) is a Flux layer, so you can do anything you would normally do with a model; like moving it to the GPU, training or freezing components, and extending it to carry out other tasks (such as neural style transfer).
# Run with dummy image data
julia> x = rand(Float32, 224, 224, 3, 1)
224×224×3×1 Array{Float32,4}:
[:, :, 1, 1] =
0.353337 0.252493 0.444695 0.767193 … 0.107599 0.424298 0.218889 0.377959
0.247294 0.039822 0.829367 0.832303 0.582103 0.359319 0.259342 0.12293
⋮
julia> vgg(x)
1000×1 Array{Float32,2}:
0.000851723
0.00079913
⋮
# See the underlying model structure
julia> vgg.layers
Chain(Conv2D((3, 3), 3=>64, NNlib.relu), Conv2D((3, 3), 64=>64, NNlib.relu), Metalhead.#3, Conv2D((3, 3), 64=>128, NNlib.relu), Conv2D((3, 3), 128=>128, NNlib.relu), Metalhead.#4, Conv2D((3, 3), 128=>256, NNlib.relu), Conv2D((3, 3), 256=>256, NNlib.relu), Conv2D((3, 3), 256=>256, NNlib.relu), Conv2D((3, 3), 256=>256, NNlib.relu), Metalhead.#5, Conv2D((3, 3), 256=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Metalhead.#6, Conv2D((3, 3), 512=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Conv2D((3, 3), 512=>512, NNlib.relu), Metalhead.#7, Metalhead.#8, Dense(25088, 4096, NNlib.relu), Flux.Dropout{Float32}(0.5f0, false), Dense(4096, 4096, NNlib.relu), Flux.Dropout{Float32}(0.5f0, false), Dense(4096, 1000), NNlib.softmax)
# Run the model up to the last convolution/pooling layer
julia> vgg.layers[1:21](x)
7×7×512×1 Array{Float32,4}:
[:, :, 1, 1] =
0.657502 0.598338 0.594517 0.594425 0.594522 0.597183 0.59534
0.663341 0.600874 0.596379 0.596292 0.596385 0.598204 0.590494
⋮Metalhead includes support for working with several common object recognition datasets.
The datasets() function will attempt to auto-detect any common dataset placed in
the datasets/. The Metalhead.download function can be used to download these datasets
(where such automatic download is possible - for other data sets, see datasets/README.md),
e.g.:
Metalhead.download(CIFAR10)
Once a dataset is loaded, it's training, validation, and test images are available using the
trainimgs, valimgs, and testimgs functions. E.g.
julia> valimgs(dataset(ImageNet))[rand(1:50000, 10)]
will fetch 10 random validation images from the ImageNet data set.
If you are using OS X, it is recommended that you use iTerm2 and install the
TerminalExtensions.jl package. This will allow you to see inference results
as well as the corresponding images directly in your terminal:
