Wolfram Neural Net Repository

Immediate Computable Access to Neural Net Models

Ademxapp Model A Trained on ImageNet Competition Data

Identify the main object in an image

Released in 2016 by the University of Adelaide, this model exploits recent progress in the understanding of residual architectures. With only 17 residual units it is able to outperform previous, much deeper architectures. ImageNet classes are mapped to Wolfram Language Entities through their unique WordNet ID.

Number of layers: 142 | Parameter count: 109,215,656 | Trained size: 437 MB |

Training Set Information

Performance

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["Ademxapp Model A Trained on ImageNet Competition Data"]
Out[1]=

Basic usage

Classify an image:

In[2]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/8a018e72-5e66-4107-aff8-66f288a2db0e"]
Out[2]=

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

In[3]:=
pred["Definition"]
Out[3]=

Get a list of available properties of the predicted Entity:

In[4]:=
pred["Properties"]
Out[4]=

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

In[5]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/ca569394-6e46-48f0-9520-bd7c5b5b30da"]
Out[5]=

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

In[6]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/c2d1f61f-839b-4dcb-a077-06fcdec2ce09"]
Out[6]=

Obtain the list of names of all available classes:

In[7]:=
EntityValue[ NetExtract[ NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], "Output"][["Labels"]], "Name"]
Out[7]=

Feature extraction

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

In[8]:=
extractor = Take[NetModel[ "Ademxapp Model A Trained on ImageNet Competition Data"], {1, -3}]
Out[8]=

Get a set of images:

In[9]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/aea1c796-a310-4cd0-bd20-400743ba6354"]

Visualize the features of a set of images:

In[10]:=
FeatureSpacePlot[imgs, FeatureExtractor -> extractor, LabelingSize -> 100, ImageSize -> 600]
Out[10]=

Visualize convolutional weights

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

In[11]:=
weights = NetExtract[ NetModel[ "Ademxapp Model A Trained on ImageNet Competition Data"], \ {"conv1a", "Weights"}];

Show the dimensions of the weights:

In[12]:=
Dimensions[weights]
Out[12]=

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

In[13]:=
ImageAdjust[Image[#, Interleaving -> False]] & /@ Normal[weights]
Out[13]=

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[14]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/e90d7ed5-673a-4964-a4f1-5d98081615b4"]
In[15]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/d952f7c4-e1d4-4a9e-83d4-b83172dbfbb2"]

Remove the linear layer from the pre-trained net:

In[16]:=
tempNet = Take[NetModel[ "Ademxapp Model A Trained on ImageNet Competition Data"], {1, -3}]
Out[16]=

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

In[17]:=
newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"cat", "dog"}}]]
Out[17]=

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

In[18]:=
trainedNet = NetTrain[newNet, trainSet, LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]
Out[18]=

Perfect accuracy is obtained on the test set:

In[19]:=
ClassifierMeasurements[trainedNet, testSet, "Accuracy"]
Out[19]=

Net information

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

In[20]:=
NetInformation[ NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], \ "ArraysElementCounts"]
Out[20]=

Obtain the total number of parameters:

In[21]:=
NetInformation[ NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], \ "ArraysTotalElementCount"]
Out[21]=

Obtain the layer type counts:

In[22]:=
NetInformation[ NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], \ "LayerTypeCounts"]
Out[22]=

Display the summary graphic:

In[23]:=
NetInformation[ NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], \ "SummaryGraphic"]
Out[23]=

Export to MXNet

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

In[24]:=
jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["Ademxapp Model A Trained on ImageNet Competition Data"], "MXNet"]
Out[24]=

Export also creates a net.params file containing parameters:

In[25]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[25]=

Get the size of the parameter file:

In[26]:=
FileByteCount[paramPath]
Out[26]=

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

In[27]:=
ResourceObject[ "Ademxapp Model A Trained on ImageNet Competition \ Data"]["ByteCount"]
Out[27]=

Requirements

Wolfram Language 11.2 (September 2017) or above

Resource History

  • Date Created: 8 September 2017
  • Latest Update: 21 June 2018

Reference