Converting TensorFlow FaceNet Models


The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Supported Model Formats article.

Public pre-trained FaceNet models contain both training and inference part of graph. Switch between this two states is manageable with placeholder value. Intermediate Representation (IR) models are intended for inference, which means that train part is redundant.

There are two inputs in this network: boolean phase_train which manages state of the graph (train/infer) and batch_size which is a part of batch joining pattern.


Converting a TensorFlow FaceNet Model to the IR

To generate a FaceNet OpenVINO model, feed a TensorFlow FaceNet model to model conversion API with the following parameters:

--input_model path_to_model/model_name.pb       \
--freeze_placeholder_with_value "phase_train->False"

The batch joining pattern transforms to a placeholder with the model default shape if --input_shape or --batch/-b are not provided. Otherwise, the placeholder shape has custom parameters.

  • freeze_placeholder_with_value "phase_train->False" to switch graph to inference mode

  • batch`*/*`-b is applicable to override original network batch

  • input_shape is applicable with or without input

  • other options are applicable