diff --git a/src/data/Data.jl b/src/data/Data.jl
index d5b5f38d..ddf0624b 100644
--- a/src/data/Data.jl
+++ b/src/data/Data.jl
@@ -13,6 +13,9 @@ end
 include("mnist.jl")
 export MNIST
 
+include("fashion-mnist.jl")
+export FashionMNIST
+
 include("cmudict.jl")
 using .CMUDict
 
diff --git a/src/data/fashion-mnist.jl b/src/data/fashion-mnist.jl
new file mode 100644
index 00000000..4e697672
--- /dev/null
+++ b/src/data/fashion-mnist.jl
@@ -0,0 +1,115 @@
+module FashionMNIST
+
+using CodecZlib, Colors
+
+const Gray = Colors.Gray{Colors.N0f8}
+
+const dir = joinpath(@__DIR__, "../../deps/fashion-mnist")
+
+function gzopen(f, file)
+  open(file) do io
+    f(GzipDecompressorStream(io))
+  end
+end
+
+function load()
+  mkpath(dir)
+  cd(dir) do
+    for file in ["train-images-idx3-ubyte",
+                 "train-labels-idx1-ubyte",
+                 "t10k-images-idx3-ubyte",
+                 "t10k-labels-idx1-ubyte"]
+      isfile(file) && continue
+      @info "Downloading Fashion-MNIST dataset"
+      download("http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/$file.gz", "$file.gz")
+      open(file, "w") do io
+        write(io, gzopen(read, "$file.gz"))
+      end
+    end
+  end
+end
+
+const IMAGEOFFSET = 16
+const LABELOFFSET = 8
+
+const NROWS = 28
+const NCOLS = 28
+
+const TRAINIMAGES = joinpath(dir, "train-images-idx3-ubyte")
+const TRAINLABELS = joinpath(dir, "train-labels-idx1-ubyte")
+const TESTIMAGES = joinpath(dir, "t10k-images-idx3-ubyte")
+const TESTLABELS = joinpath(dir, "t10k-labels-idx1-ubyte")
+
+function imageheader(io::IO)
+  magic_number = bswap(read(io, UInt32))
+  total_items = bswap(read(io, UInt32))
+  nrows = bswap(read(io, UInt32))
+  ncols = bswap(read(io, UInt32))
+  return magic_number, Int(total_items), Int(nrows), Int(ncols)
+end
+
+function labelheader(io::IO)
+  magic_number = bswap(read(io, UInt32))
+  total_items = bswap(read(io, UInt32))
+  return magic_number, Int(total_items)
+end
+
+function rawimage(io::IO)
+  img = Array{Gray}(undef, NCOLS, NROWS)
+  for i in 1:NCOLS, j in 1:NROWS
+    img[i, j] = reinterpret(Colors.N0f8, read(io, UInt8))
+  end
+  return img
+end
+
+function rawimage(io::IO, index::Integer)
+  seek(io, IMAGEOFFSET + NROWS * NCOLS * (index - 1))
+  return rawimage(io)
+end
+
+rawlabel(io::IO) = Int(read(io, UInt8))
+
+function rawlabel(io::IO, index::Integer)
+  seek(io, LABELOFFSET + (index - 1))
+  return rawlabel(io)
+end
+
+getfeatures(io::IO, index::Integer) = vec(getimage(io, index))
+
+"""
+    images()
+    images(:test)
+
+Load the MNIST images.
+
+Each image is a 28×28 array of `Gray` colour values (see Colors.jl).
+
+Returns the 60,000 training images by default; pass `:test` to retreive the
+10,000 test images.
+"""
+function images(set = :train)
+  load()
+  io = IOBuffer(read(set == :train ? TRAINIMAGES : TESTIMAGES))
+  _, N, nrows, ncols = imageheader(io)
+  [rawimage(io) for _ in 1:N]
+end
+
+"""
+    labels()
+    labels(:test)
+
+Load the labels corresponding to each of the images returned from `images()`.
+Each label is a number from 0-9.
+
+Returns the 60,000 training labels by default; pass `:test` to retreive the
+10,000 test labels.
+"""
+function labels(set = :train)
+  load()
+  io = IOBuffer(read(set == :train ? TRAINLABELS : TESTLABELS))
+  _, N = labelheader(io)
+  [rawlabel(io) for _ = 1:N]
+end
+
+
+end