OpenVINO TensorFlow Frontend Capabilities and Limitations

TensorFlow Frontend is C++ based Frontend for conversion of TensorFlow models and is available as a preview feature starting from 2022.3. That means that you can start experimenting with --use_new_frontend option passed to Model Optimizer to enjoy improved conversion time for limited scope of models or directly loading TensorFlow models through read_model() method.

The current limitations:

  • IRs generated by new TensorFlow Frontend are compatible only with OpenVINO API 2.0

  • There is no full parity yet between legacy Model Optimizer TensorFlow Frontend and new TensorFlow Frontend so primary path for model conversion is still legacy frontend

  • Model coverage and performance is continuously improving so some conversion phase failures, performance and accuracy issues might occur in case model is not yet covered. Known unsupported models: object detection models and all models with transformation configs, models with TF1/TF2 control flow, Complex type and training parts

  • read_model() method supports only \*.pb format while Model Optimizer (or convert_model call) will accept other formats as well which are accepted by existing legacy frontend