EfficientNet-V2 Trained on ImageNet Competition Data

Identify the main object in an image

Number of models: 7

Training Set Information

ImageNet Large Scale Visual Recognition Challenge 2012 classification dataset, consisting of 1.2 million training images, with one thousand classes of objects.

Performance

The models achieve the following accuracies on the original ImageNet validation set.

Examples

Download Example Notebook

Open in Wolfram Cloud

Resource retrieval

Get the pre-trained net:

In[1]:=

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]:=

Out[2]=

Pick a non-default net by specifying the parameters:

In[3]:=

Out[3]=

Pick a non-default uninitialized net:

In[4]:=

Out[4]=

Basic usage

Classify an image:

In[5]:=

(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/176c8658-0030-43cc-bd84-5825fa715e55"]

Out[5]=

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

In[6]:=

Out[6]=

Get a list of available properties of the predicted Entity:

In[7]:=

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/85377d40-587b-40e0-8487-15ce4a72161f"]

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/0f2aa57c-8166-412b-bb2d-6e15ce66d212"]

Out[9]=

Obtain the list of names of all available classes:

In[10]:=

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]:=

Out[11]=

Get a set of images:

In[12]:=

(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/0e6e12b1-30d7-4797-bbd8-4f18ee34e049"]

Use the net as a feature extractor to build a clustering tree of the images:

In[13]:=

Out[13]=

Transfer learning

Use the pre-trained model to build a classifier for telling apart indoor and outdoor photos. Create a test set and a training set:

In[14]:=

(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/a496e5c3-e3b0-4f4c-a3fa-bb1b369b4e32"]

In[15]:=

Remove the linear layer from the pre-trained net:

In[16]:=

Out[16]=

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

In[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]:=

Out[19]=

Net information

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

In[20]:=

Out[20]=

Obtain the total number of parameters:

In[21]:=

Out[21]=

Obtain the layer type counts:

In[22]:=

Out[22]=

Export to ONNX

Export the net to the ONNX format:

In[23]:=

Out[23]=

Get the size of the ONNX file:

In[24]:=

Out[24]=

Check some metadata of the ONNX model:

In[25]:=

Out[25]=

Import the model back into the Wolfram Language. However, the NetEncoder and NetDecoder will be absent because they are not supported by ONNX:

In[26]:=

Out[26]=

Construction Notebook

Download Construction Notebook

Open in Wolfram Cloud

Resource History

Date Created: 2 December 2021

Reference

M. Tan, Q. V. Le, "EfficientNetV2: Smaller Models and Faster Training," arXiv:2104.00298 (2021)
Available from:
- https://github.com/google/automl/tree/master/efficientnetv2