Convert TensorFlow* FaceNet Models to Intermediate Representation

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.

Convert TensorFlow FaceNet Model to IR

To generate FaceNet IR provide TensorFlow FaceNet model to Model Optimizer with parameters:

python3 ./
--input_model path_to_model/model_name.pb \
--freeze_placeholder_with_value "phase_train->False"

Batch joining pattern transforms to placeholder with model default shape if --input_shape or --batch/-b was not provided. Otherwise, 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