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Inception V1 Trained on ImageNet Competition Data

Identify the main object in an image

Released in 2014 by Google Inc. (and also known as GoogLeNet), this model won the ImageNet Competition in 2014, achieving 88.9% top-five (68.7% top-one) accuracy on the ImageNet 2012 competition dataset, using about 50 MB of parameters. It was the first model to introduce Inception blocks, in which convolutions with different kernel sizes are evaluated in parallel and then catenated together. ImageNet classes are mapped to Wolfram Language Entities through their unique WordNet IDs.

Number of layers: 147 | Parameter count: 6,998,552 | Trained size: 28 MB

Training Set Information

Examples

Resource retrieval

Retrieve the resource object:

In[1]:=
ResourceObject["Inception V1 Trained on ImageNet Competition Data"]
Out[1]=

Get the pre-trained net:

In[2]:=
NetModel["Inception V1 Trained on ImageNet Competition Data"]
Out[2]=

Basic usage

Classify an image:

In[3]:=
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The prediction is an Entity object, which can be queried:

In[4]:=
pred["Definition"]
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Get a list of available properties of the predicted Entity:

In[5]:=
pred["Properties"]
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Obtain the probabilities of the ten most likely entities predicted by the net:

In[6]:=
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An object outside the list of the ImageNet classes will be misidentified:

In[7]:=
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Obtain the list of names of all available classes:

In[8]:=
EntityValue[
 NetExtract[
   NetModel["Inception V1 Trained on ImageNet Competition Data"], 
   "Output"][["Labels"]], "Name"]
Out[8]=

Feature extraction

Remove the last three layers of the trained net so that the net produces a vector representation of an image:

In[9]:=
extractor = 
 Take[NetModel[
   "Inception V1 Trained on ImageNet Competition Data"], {1, -4}]
Out[9]=

Get a set of images:

In[10]:=

Visualize the features of a set of images:

In[11]:=
FeatureSpacePlot[imgs, FeatureExtractor -> extractor, 
 LabelingFunction -> (ImageResize[#, 100] &), ImageSize -> 800]
Out[11]=

Visualize convolutional weights

Extract the weights of the first convolutional layer in the trained net:

In[12]:=
weights = 
  NetExtract[
   NetModel[
    "Inception V1 Trained on ImageNet Competition Data"], \
{"conv1_7x7_s2", "Weights"}];

Show the dimensions of the weights:

In[13]:=
Dimensions[weights]
Out[13]=

Visualize the weights as a list of 64 images of size 7x7:

In[14]:=
ImageAdjust[Image[#, Interleaving -> False]] & /@ weights
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Transfer learning

Use the pre-trained model to build a classifier for telling apart images of dogs and cats. Create a test set and a training set:

In[15]:=
In[16]:=

Remove the linear layer from the pre-trained net:

In[17]:=
tempNet = 
 Take[NetModel[
   "Inception V1 Trained on ImageNet Competition Data"], {1, -4}]
Out[17]=

Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:

In[18]:=
newNet = NetChain[<|"pretrainedNet" -> tempNet, 
   "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, 
  "Output" -> NetDecoder[{"Class", {"cat", "dog"}}]]
Out[18]=

Train on the dataset, freezing all the weights except for those in the "linearNew" layer (use TargetDevice -> "GPU" for training on a GPU):

In[19]:=
trainedNet = 
 NetTrain[newNet, trainSet, 
  LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]
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Perfect accuracy is obtained on the test set:

In[20]:=
ClassifierMeasurements[trainedNet, testSet, "Accuracy"]
Out[20]=

Export to MXNet

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

In[21]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["Inception V1 Trained on ImageNet Competition Data"], 
  "MXNet"]
Out[21]=

Export also creates a net.params file containing parameters:

In[22]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
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Get the size of the parameter file:

In[23]:=
FileByteCount[paramPath]
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The size is similar to the byte count of the resource object:

In[24]:=
ResourceObject[
  "Inception V1 Trained on ImageNet Competition Data"]["ByteCount"]
Out[24]=

Represent the MXNet net as a graph:

In[25]:=
Import[jsonPath, {"MXNet", "NodeGraphPlot"}]
Out[25]=

Requirements

Wolfram Language 11.1 (March 2017) or above

Reference