Wolfram Computation Meets Knowledge

Colorful Image Colorization Trained on ImageNet Competition Data

Colorize a grayscale image

Released in 2016, this model recasts image colorization into a classification problem by dividing the AB color space into 313 bins. The final color for each pixel is picked by performing an ad hoc modified mean of its probability distribution over the bins. The resulting method produces vibrant and realistic colorizations.

Number of layers: 58 | Parameter count: 32,241,005 | Trained size: 129 MB

Training Set Information

Examples

Resource retrieval

Retrieve the resource object:

In[1]:=
ResourceObject["Colorful Image Colorization Trained on ImageNet \
Competition Data"]
Out[1]=

Get the pre-trained net:

In[2]:=
NetModel["Colorful Image Colorization Trained on ImageNet Competition \
Data"]
Out[2]=

Evaluation function

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

In[3]:=
netevaluation[img_Image] := With[{lum = ColorSeparate[img, "L"]},
  Image[Prepend[
    ArrayResample[
     NetModel[
       "Colorful Image Colorization Trained on ImageNet Competition \
Data"][lum], Prepend[Reverse@ImageDimensions@img, 2]], 
    ImageData[lum]], Interleaving -> False, ColorSpace -> "LAB"]
  ]

Basic usage

Colorize a grayscale image using the evaluation function:

In[4]:=
netevaluation[\!\(\*
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"], {{0, 224}, {224, 
      0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
     "Byte", ColorSpace -> "Grayscale", Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->{126., Automatic},
ImageSizeRaw->{224, 224},
PlotRange->{{0, 224}, {0, 224}}]\)]
Out[4]=

Performance evaluation

Get a color image:

In[5]:=
CloudGet["https://www.wolframcloud.com/objects/7f084c72-30ff-4071-b078-630ef15bfed7"] (* Evaluate this cell to copy the example input from a cloud object *)

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[netevaluation[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["Colorful Image Colorization Trained on ImageNet \
Competition Data"], "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[
  "Colorful Image Colorization Trained on ImageNet Competition \
Data"]["ByteCount"]
Out[10]=

Represent the MXNet net as a graph:

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

Requirements

Wolfram Language 11.3 (March 2018) or above

Reference