Wolfram Research

EfficientNet Trained on ImageNet with NoisyStudent

Identify the main object in an image

Released in 2019, this model utilizes the techniques of NoisyStudent data augmentation on the EfficientNet architectures to effectively perform image classification.

Number of models: 8

Training Set Information

Performance

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["EfficientNet Trained on ImageNet with NoisyStudent"]
Out[1]=

NetModel parameters

This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:

In[2]:=
NetModel["EfficientNet Trained on ImageNet with NoisyStudent", \
"ParametersInformation"]
Out[2]=

Pick a non-default net by specifying the parameters:

In[3]:=
NetModel[{"EfficientNet Trained on ImageNet with NoisyStudent", 
  "Architecture" -> "B7"}]
Out[3]=

Pick a non-default uninitialized net:

In[4]:=
NetModel[{"EfficientNet Trained on ImageNet with NoisyStudent", 
  "Architecture" -> "B7"}, "UninitializedEvaluationNet"]
Out[4]=

Basic usage

Classify an image:

In[5]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/ae5c6162-69ae-4eea-a72b-24b4b3e6d6b5"]
Out[5]=

The prediction is an Entity object, which can be queried:

In[6]:=
pred["Definition"]
Out[6]=

Get a list of available properties of the predicted Entity:

In[7]:=
pred["Properties"]
Out[7]=

Obtain the probabilities of the 10 most likely entities predicted by the net:

In[8]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/c83d2d88-f298-4269-a365-4bfa52aed1ee"]
Out[8]=

An object outside the list of the ImageNet classes will be misidentified:

In[9]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/d5486954-5730-4714-9c04-b0b0a1aff34f"]
Out[9]=

Obtain the list of names of all available classes:

In[10]:=
EntityValue[
 NetExtract[
   NetModel["EfficientNet Trained on ImageNet with NoisyStudent"], 
   "Output"][["Labels"]], "Name"]
Out[10]=

Feature extraction

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

In[11]:=
extractor = 
 Take[NetModel[
   "EfficientNet Trained on ImageNet with NoisyStudent"], {1, -3}]
Out[11]=

Get a set of images:

In[12]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/a8dba6de-8b3d-4df4-ad5a-8ade826dd125"]

Visualize the features of a set of images:

In[13]:=
FeatureSpacePlot[imgs, FeatureExtractor -> extractor, 
 LabelingSize -> 200, ImageSize -> Full, AspectRatio -> 1/2]
Out[13]=

Visualize convolutional weights

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

In[14]:=
NetModel["EfficientNet Trained on ImageNet with NoisyStudent"]
Out[14]=
In[15]:=
weights = 
  NetExtract[
   NetModel[
    "EfficientNet Trained on ImageNet with NoisyStudent"], \
{"stem_conv", "Weights"}];

Show the dimensions of the weights:

In[16]:=
Dimensions[weights]
Out[16]=

Visualize the weights as a list of 64 images of size 3x3:

In[17]:=
ImageAdjust[Image[#, Interleaving -> False]] & /@ Normal[weights]
Out[17]=

Transfer learning

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

In[18]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/78043dd5-b0d1-4f83-b231-598a919dfc35"]
In[19]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/e22bad46-0b4b-47c1-88cb-7bd15397b57e"]

Remove the linear layer from the pre-trained net:

In[20]:=
tempNet = 
 Take[NetModel[
   "EfficientNet Trained on ImageNet with NoisyStudent"], {1, -3}]
Out[20]=

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

In[21]:=
newNet = NetChain[<|"pretrainedNet" -> tempNet, 
   "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, 
  "Output" -> NetDecoder[{"Class", {"bicycle", "motorcycle"}}]]
Out[21]=

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

In[22]:=
trainedNet = 
 NetTrain[newNet, trainSet, 
  LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]
Out[22]=

Perfect accuracy is obtained on the test set:

In[23]:=
ClassifierMeasurements[trainedNet, testSet, "Accuracy"]
Out[23]=

Net information

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

In[24]:=
Information[
 NetModel["EfficientNet Trained on ImageNet with NoisyStudent"], \
"ArraysElementCounts"]
Out[24]=

Obtain the total number of parameters:

In[25]:=
Information[
 NetModel["EfficientNet Trained on ImageNet with NoisyStudent"], \
"ArraysTotalElementCount"]
Out[25]=

Obtain the layer type counts:

In[26]:=
Information[
 NetModel["EfficientNet Trained on ImageNet with NoisyStudent"], \
"LayerTypeCounts"]
Out[26]=

Export to MXNet

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

In[27]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["EfficientNet Trained on ImageNet with NoisyStudent"], 
  "MXNet"]
Out[27]=

Export also creates a net.params file containing parameters:

In[28]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[28]=

Get the size of the parameter file:

In[29]:=
FileByteCount[paramPath]
Out[29]=

Requirements

Wolfram Language 12.1 (March 2020) or above

Resource History

Reference