Wolfram Neural Net Repository

Immediate Computable Access to Neural Net Models

Inception V1 Trained on Places365 Data

Identify the scene type of an image

Released in 2016 by the MIT Computer Science and Artificial Intelligence Laboratory as a pre-trained model for the launch of the Places365 dataset (a subset of the Places2 dataset). The model is based on the Inception V1 architecture.

Number of layers: 147 | Parameter count: 6,347,677 | Trained size: 26 MB |

Training Set Information

Performance

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["Inception V1 Trained on Places365 Data"]
Out[1]=

Basic usage

Find the type of scene of an image:

In[2]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/a817913b-3304-441e-bd4b-ebb82688778d"]
Out[2]=

Obtain the probabilities of the most likely scenes:

In[3]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/00abde0b-d496-4d15-8a30-29e8852cda39"]
Out[3]=

Net information

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

In[4]:=
NetInformation[ NetModel["Inception V1 Trained on Places365 Data"], \ "ArraysElementCounts"]
Out[4]=

Obtain the total number of parameters:

In[5]:=
NetInformation[ NetModel["Inception V1 Trained on Places365 Data"], \ "ArraysTotalElementCount"]
Out[5]=

Obtain the layer type counts:

In[6]:=
NetInformation[ NetModel["Inception V1 Trained on Places365 Data"], \ "LayerTypeCounts"]
Out[6]=

Display the summary graphic:

In[7]:=
NetInformation[ NetModel["Inception V1 Trained on Places365 Data"], "SummaryGraphic"]
Out[7]=

Export to MXNet

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

In[8]:=
jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["Inception V1 Trained on Places365 Data"], "MXNet"]
Out[8]=

Export also creates a net.params file containing parameters:

In[9]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[9]=

Get the size of the parameter file:

In[10]:=
FileByteCount[paramPath]
Out[10]=

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

In[11]:=
ResourceObject["Inception V1 Trained on Places365 Data"]["ByteCount"]
Out[11]=

Represent the MXNet net as a graph:

In[12]:=
Import[jsonPath, {"MXNet", "NodeGraphPlot"}]
Out[12]=

Requirements

Wolfram Language 11.1 (March 2017) or above

Resource History

  • Date Created: 2 February 2017
  • Latest Update: 21 June 2018

Reference