Wolfram Computation Meets Knowledge

ResNet-101 Trained on Augmented CASIA-WebFace Data

Represent a facial image as a vector

Released in 2016 and based on the ResNet-101 architecture, this facial feature extractor was trained using specific data augmentation techniques tailored for this task. Starting from the CASIA-WebFace dataset, a far greater per-subject appearance was achieved by synthesizing pose, shape and expression variations from each single image.

Number of layers: 345 | Parameter count: 42,605,504 | Trained size: 172 MB

Training Set Information

Examples

Resource retrieval

Retrieve the resource object:

In[1]:=
ResourceObject["ResNet-101 Trained on Augmented CASIA-WebFace Data"]
Out[1]=

Get the pre-trained net:

In[2]:=
NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"]
Out[2]=

Basic usage

Compute a feature vector for a given image:

In[3]:=
features = 
  NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"][\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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xyo=
"], {{0, 75}, {75, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
       "Byte", ColorSpace -> "RGB", Interleaving -> True, 
        MetaInformation -> Association[
         "Source" -> "http://wiki.d-addicts.com/File:RaymondLam.jpg", 
          "URL" -> "http://www.wolframcdn.com/waimage/hset028/891/\
8918eb5845add22945c22846d797ed18_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75, 75},
PlotRange->{{0, 75}, {0, 75}}]\)];

Get the length of the feature vector:

In[4]:=
Length[features]
Out[4]=

Use a batch of face images:

In[5]:=
CloudGet["https://www.wolframcloud.com/objects/c5c49d1f-c90a-455c-9a4e-c95eab183ae1"] (* Evaluate this cell to copy the example input from a cloud object *)

Compute the feature vectors for the batch of images, and obtain the dimensions of the features:

In[6]:=
features = 
  NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"][
   faces];
In[7]:=
Dimensions[features]
Out[7]=

Reduce the feature vectors to two dimensions with t-SNE:

In[8]:=
points = DimensionReduce[features, 2, Method -> "TSNE"]
Out[8]=
In[9]:=
Dimensions[points]
Out[9]=

Visualize the results:

In[10]:=
Graphics[
 MapThread[Inset[#1, #2, {0, 0}, 0.5] &, {faces, points}],
 ImageSize -> 600, ImagePadding -> 25]
Out[10]=

Obtain the five closest faces to a given one:

In[11]:=
FeatureNearest[faces, \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 50}, {50, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
     "Byte", ColorSpace -> "RGB", Interleaving -> True, 
      MetaInformation -> Association[
       "Source" -> "http://en.wikipedia.org/wiki/File:Elizabeth_Clare_\
Prophet_-_Guru_Ma.JPG", 
        "URL" -> "http://www.wolframcdn.com/waimage/hset028/aeb/\
aeb98eda6645fcb390425ec1e451a972_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{50, 50},
PlotRange->{{0, 50}, {0, 50}}]\), 5, 
 FeatureExtractor -> 
  NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"]]
Out[11]=

Net information

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

In[12]:=
NetInformation[
 NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"], \
"ArraysElementCounts"]
Out[12]=

Obtain the total number of parameters:

In[13]:=
NetInformation[
 NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"], \
"ArraysTotalElementCount"]
Out[13]=

Obtain the layer type counts:

In[14]:=
NetInformation[
 NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"], \
"LayerTypeCounts"]
Out[14]=

Display the summary graphic:

In[15]:=
NetInformation[
 NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"], \
"SummaryGraphic"]
Out[15]=

Export to MXNet

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

In[16]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["ResNet-101 Trained on Augmented CASIA-WebFace Data"], 
  "MXNet"]
Out[16]=

Export also creates a net.params file containing parameters:

In[17]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[17]=

Get the size of the parameter file:

In[18]:=
FileByteCount[paramPath]
Out[18]=

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

In[19]:=
ResourceObject[
  "ResNet-101 Trained on Augmented CASIA-WebFace Data"]["ByteCount"]
Out[19]=

Requirements

Wolfram Language 11.2 (September 2017) or above

Resource History

Reference