update readme

README.md

@@ -9,29 +9,25 @@ Pkg.clone("https://github.com/FluxML/Metalhead.jl")
 
 This package provides computer vision models that run on top of the [Flux](http://fluxml.github.io/) machine learning library.
 
-```julia
-julia> using Metalhead
+![IJulia Screenshot](https://i.imgur.com/spBsaz7.png)
 
-julia> vgg = VGG19()
-VGG19()
+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).
 
-julia> x = rand(Float32, 224, 224, 3, 1) # Dummy image data
+```julia
+# 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> vgg19(x)
+julia> vgg(x)
 1000×1 Array{Float32,2}:
  0.000851723
  0.00079913
   ⋮
-```
 
-Because models like `VGG19` are Flux layers, you can do anything you would normally do with a model, including moving it to the GPU, training or freezing components, and extending it to carry out other tasks (such as neural style transfer).
-
-```julia
 # 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)