Wolfram Computation Meets Knowledge

ColorNet Image Colorization Trained on ImageNet Competition Data

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 ImageNet Competition dataset during training.

Number of layers: 62 | Parameter count: 44,457,314 | Trained size: 178 MB

Training Set Information

Examples

Resource retrieval

Retrieve the resource object:

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

Get the pre-trained net:

In[2]:=
NetModel["ColorNet 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] := 
 Image[Prepend[
   ArrayResample[
    NetModel[
      "ColorNet Image Colorization Trained on ImageNet Competition \
Data"][img], Prepend[Reverse@ImageDimensions@img, 2]], 
   ImageData[ColorSeparate[img, "L"]]], Interleaving -> False, 
  ColorSpace -> "LAB"]

Basic usage

Colorize a grayscale image using the evaluation function:

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

Performance evaluation

Get a color image:

In[5]:=
CloudGet["https://www.wolframcloud.com/objects/5b361ecd-ea10-4fa4-863f-6f50f6fd31ee"] (* 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]=

Net information

Inspect the number of parameters of all arrays in the net:

In[7]:=
NetInformation[
 NetModel["ColorNet Image Colorization Trained on ImageNet \
Competition Data"], "ArraysElementCounts"]
Out[7]=

Obtain the total number of parameters:

In[8]:=
NetInformation[
 NetModel["ColorNet Image Colorization Trained on ImageNet \
Competition Data"], "ArraysTotalElementCount"]
Out[8]=

Obtain the layer type counts:

In[9]:=
NetInformation[
 NetModel["ColorNet Image Colorization Trained on ImageNet \
Competition Data"], "LayerTypeCounts"]
Out[9]=

Display the summary graphic:

In[10]:=
NetInformation[
 NetModel["ColorNet Image Colorization Trained on ImageNet \
Competition Data"], "SummaryGraphic"]
Out[10]=

Export to MXNet

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

In[11]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["ColorNet Image Colorization Trained on ImageNet \
Competition Data"], "MXNet"]
Out[11]=

Export also creates a net.params file containing parameters:

In[12]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[12]=

Get the size of the parameter file:

In[13]:=
FileByteCount[paramPath]
Out[13]=

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

In[14]:=
ResourceObject[
  "ColorNet Image Colorization Trained on ImageNet Competition \
Data"]["ByteCount"]
Out[14]=

Represent the MXNet net as a graph:

In[15]:=
Import[jsonPath, {"MXNet", "NodeGraphPlot"}]
Out[15]=

Requirements

Wolfram Language 11.2 (September 2017) or above

Resource History

Reference