Name: Metalhead.jl
Owner: Flux
Description: null
Created: 2018-02-15 21:37:03.0
Updated: 2018-05-22 09:51:21.0
Pushed: 2018-05-24 06:25:44.0
Homepage: null
Size: 160
Language: Julia
GitHub Committers
| User | Most Recent Commit | # Commits |
|---|
Other Committers
| User | Most Recent Commit | # Commits |
|---|
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).
n with dummy image data
a> x = rand(Float32, 224, 224, 3, 1)
224×3×1 Array{Float32,4}:
:, 1, 1] =
53337 0.252493 0.444695 0.767193 ? 0.107599 0.424298 0.218889 0.377959
47294 0.039822 0.829367 0.832303 0.582103 0.359319 0.259342 0.12293
a> vgg(x)
×1 Array{Float32,2}:
00851723
0079913
e the underlying model structure
a> vgg.layers
n(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)
n the model up to the last convolution/pooling layer
a> vgg.layers[1:21](x)
512×1 Array{Float32,4}:
:, 1, 1] =
57502 0.598338 0.594517 0.594425 0.594522 0.597183 0.59534
63341 0.600874 0.596379 0.596292 0.596385 0.598204 0.590494
Metalhead includes support for wokring 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.:
lHead.download(CIFAR10)
Once a dataset is load, it's training, validation, and test images are available using the
trainimgs, valimgs, and testimgs functions. E.g.
a> 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:
