diff --git a/vision/cifar10/resnet18.jl b/vision/cifar10/resnet18.jl
new file mode 100644
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+++ b/vision/cifar10/resnet18.jl
@@ -0,0 +1,163 @@
+#Should work with
+#Julia 1.4
+#Atom v0.12.10
+#CUDAapi v4.0.0
+#CUDAdrv v6.2.2
+#CUDAnative v3.0.2
+#CuArrays v2.0.1 #master (https://github.com/JuliaGPU/CuArrays.jl)
+#DataFrames v0.20.2
+#Flux v0.10.3 #master (https://github.com/FluxML/Flux.jl)
+#ImageMagick v1.1.3
+#Images v0.22.0
+#Juno v0.8.1
+#MLDatasets v0.4.1
+#Metalhead v0.5.0
+#NNlib v0.6.6
+#RDatasets v0.6.1
+#StatsBase v0.32.2
+#Zygote v0.4.12
+#
+#
+#
+#Still has issues with speed and memory consumption.
+#ConvTranspose slows the neural network down significantly.
+#A more optimal way to feed images to the neural network probably can be found.
+#resizing images in preprocessing should be faster but might lead to greater
+#memory consumption. Resizing in preprocessing could be done with ConvTranspose,
+#imresize, or by some other tool.
+
+
+ENV["JULIA_CUDA_VERBOSE"] = true
+ENV["JULIA_CUDA_MEMORY_POOL"] = "split"
+ENV["JULIA_CUDA_MEMORY_LIMIT"] = 8000_000_000
+
+using Random
+using Statistics
+using CuArrays
+using Zygote
+using Flux, Flux.Optimise
+using Metalhead, Images
+using Metalhead: trainimgs
+using Images.ImageCore
+using Flux: onehotbatch, onecold, logitcrossentropy, Momentum, @epochs
+using Base.Iterators: partition
+using Dates
+
+CuArrays.allowscalar(false)
+
+batch_size = 1
+
+getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
+
+function make_minibatch(imgs,labels,batch_size)
+  data_set = [(cat(imgs[i]..., dims = 4), labels[:,i])
+          for i in partition(1:length(imgs), batch_size)]
+  return data_set
+end
+
+X = trainimgs(CIFAR10)
+
+train_idxs = 1:49000
+
+train_imgs = [getarray(X[i].img) for i in train_idxs]
+train_labels = Float32.(onehotbatch([X[i].ground_truth.class for i in train_idxs],1:10))
+train_set = make_minibatch(train_imgs,train_labels,batch_size)
+train_data = train_set |>
+  x -> map(y->gpu.(y),x)
+
+verify_idxs = 49001:50000
+verify_imgs = cat([getarray(X[i].img) for i in verify_idxs]..., dims = 4)
+verify_labels = Float32.(onehotbatch([X[i].ground_truth.class for i in verify_idxs],1:10))
+verify_set = [(verify_imgs,verify_labels)]
+verify_data = verify_set |>
+  x -> map(y->gpu.(y),x)
+
+tX = valimgs(CIFAR10)
+test_idxs = 1:10000
+test_imgs = [getarray(tX[i].img) for i in test_idxs]
+test_labels = Float32.(onehotbatch([tX[i].ground_truth.class for i in test_idxs], 1:10))
+test_set = make_minibatch(test_imgs,test_labels,batch_size)
+test_data = test_set |>
+  x -> map(y->gpu.(y),x)
+
+
+
+identity_layer(n) = Chain(Conv((3,3), n=>n, pad = (1,1), stride = (1,1)),
+                                  BatchNorm(n,relu),
+                                  Conv((3,3), n=>n, pad = (1,1), stride = (1,1)),
+                                  BatchNorm(n,relu))
+
+convolution_layer(n) = Chain(Conv((3,3), n=> 2*n, pad = (1,1), stride = (2,2)),
+                             BatchNorm(2*n,relu),
+                             Conv((3,3), 2*n=>2*n, pad = (1,1), stride = (1,1)),
+                             BatchNorm(2*n,relu))
+
+simple_convolution(n) = Chain(Conv((1,1), n=>n, pad = (1,1), stride = (2,2)),
+                              BatchNorm(n,relu))
+
+
+m_filter(n) = Chain(
+  Conv((3,3), n=>2*n, pad = (1,1), stride = (2,2)),
+  BatchNorm(2*n,relu),
+) |> gpu
+
+struct Combinator
+    conv::Chain
+end |> gpu
+Combinator(n) = Combinator(m_filter(n))# |> gpu
+
+
+function (op::Combinator)(x, y)
+  z = op.conv(y)
+  return x + z
+end
+
+
+n = 7
+
+m = Chain(
+  ConvTranspose((n, n), 3 => 3, stride = n),
+  Conv((7,7), 3=>64, pad = (3,3), stride = (2,2)),
+  BatchNorm(64,relu),
+  MaxPool((3,3), pad = (1,1), stride = (2,2)),
+  SkipConnection(identity_layer(64), (variable_1, variable_2) -> variable_1 + variable_2),
+  SkipConnection(identity_layer(64), (variable_1, variable_2) -> variable_1 + variable_2),
+  SkipConnection(convolution_layer(64), Combinator(64)),
+  SkipConnection(identity_layer(128), (variable_1, variable_2) -> variable_1 + variable_2),
+  SkipConnection(convolution_layer(128), Combinator(128)),
+  SkipConnection(identity_layer(256), (variable_1, variable_2) -> variable_1 + variable_2),
+  SkipConnection(convolution_layer(256), Combinator(256)),
+  SkipConnection(identity_layer(512), (variable_1, variable_2) -> variable_1 + variable_2),
+  MeanPool((7,7)),
+  x -> reshape(x, :, size(x,4)),
+  Dense(512*1, 10),
+  softmax,
+) |> gpu
+
+
+
+function accuracy(data_set)
+  batch_size = size(data_set[1][1])[end]
+  l = length(data_set)*batch_size
+  s = 0f0
+  for (x,y) in data_set
+    s += sum((onecold(m(x|>gpu) |> cpu) .== onecold(y|>cpu)))
+  end
+  return s/l
+end
+
+
+loss(x, y) = sum(logitcrossentropy(m(x), y))
+opt = Momentum(0.01)
+
+number_of_epochs = 1
+
+@epochs number_of_epochs train!(loss, params(m), train_data, opt, cb = Flux.throttle(() -> println("training... $(Dates.Time(Dates.now()))") , 10))
+
+
+verify_acc = accuracy(verify_data)
+@info "Verify accuracy : $(verify_acc)"
+
+
+test_acc = accuracy(test_data)
+@info "Test accuracy : $(test_acc)"