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ColorNet Image Colorization Trained on Places Data (Raw Model)

Colorize a grayscale image

Released in 2016, this net automatically colorizes a grayscale image, exploiting a combination of local and global image features. Local features are extracted in a fully convolutional fashion, while the extraction of global features was developed leveraging the labels of the Places dataset during training.

Number of layers: 62 | Parameter count: 45,505,890 | Trained size: 182 MB

Training Set Information

Examples

Resource retrieval

Retrieve the resource object:

In[1]:=
ResourceObject["ColorNet Image Colorization Trained on Places Data \
(Raw Model)"]
Out[1]=

Get the pre-trained net:

In[2]:=
NetModel["ColorNet Image Colorization Trained on Places Data (Raw \
Model)"]
Out[2]=

Basic usage

This net takes a grayscale image as input and outputs the A and B channels in the LAB color space. Currently it needs an evaluation to merge its output with the luminance of the input:

In[3]:=
evaluationFunction[img_Image] := 
 Image[Prepend[
   NetModel[
     "ColorNet Image Colorization Trained on Places Data (Raw \
Model)"][img], ImageData[ColorSeparate[img, "L"]]], 
  Interleaving -> False, ColorSpace -> "LAB"]

Colorize a grayscale image:

In[4]:=
evaluationFunction[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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izt8Rp1IZVSJNYBcJddqLS6zK+Q0e4ruQsji9sXj8VAg4LJrPHpbyOn0AWaz
3WLxamVGmUWpVPEVQq1CYZTYYk6VzuF2uxIs5hASjUbjaXQiRyg2stAEEosL
GyJzUc8xpP+/wXLHQRAGAOhJXNUYDYIFIhSt2BYppLRSIaAEP4ORqDEujs4O
XsKTyh3ey3tmp2f0LU1D1pTQWb4Po/u1YXip6SMTJEXDX3WjPDTpd505MMcT
gKCzXOXV7fM+Pr+/x+u0ZQE0Auw5A522X29FxPCAAxdmuNZtN1AAEg/DNoFD
yLNS1Grrz217kRCLopRMaVxVUqxbN8rskG5kfr4Uu7LYZZK5IhNRvlERj+Mk
RTOWcIpbinzp4lBwiiiTSqg/QiRt2g==
"], {{0, 224}, {224, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
     "Byte", ColorSpace -> "Grayscale", Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->{204., Automatic},
ImageSizeRaw->{224, 224},
PlotRange->{{0, 224}, {0, 224}}]\)]
Out[4]=

Performance evaluation

Get a color image:

In[5]:=

Compare the colorization performed by the net with the ground truth:

In[6]:=
With[{grayscale = ColorConvert[img, "Grayscale"]},
 <|"Input" -> Image[grayscale, ImageSize -> 224], 
  "Prediction" -> 
   Image[evaluationFunction[grayscale], ImageSize -> 224], 
  "GroundTruth" -> Image[img, ImageSize -> 224]|>
 ]
Out[6]=

Export to MXNet

Export the net into a format that can be opened in MXNet:

In[7]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["ColorNet Image Colorization Trained on Places Data (Raw \
Model)"], "MXNet"]
Out[7]=

Export also creates a net.params file containing parameters:

In[8]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[8]=

Get the size of the parameter file:

In[9]:=
FileByteCount[paramPath]
Out[9]=

The size is similar to the byte count of the resource object:

In[10]:=
ResourceObject[
  "ColorNet Image Colorization Trained on Places Data (Raw \
Model)"]["ByteCount"]
Out[10]=

Represent the MXNet net as a graph:

In[11]:=
Import[jsonPath, {"MXNet", "NodeGraphPlot"}]
Out[11]=

Requirements

Wolfram Language 11.2 (September 2017) or above

Reference

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