During the OpenVINO™ toolkit installation, you would have installed the Model Optimizer dependencies. Now let's understand how to convert the model with common optimization parameters.
Use the mo.py script from the <INSTALL_DIR>/deployment_tools/model_optimizer directory to run the Model Optimizer and convert the model to the Intermediate Representation (IR). The simplest way to convert a model is to run mo.py with a path to the input model file:
python3 mo.py --input_model INPUT_MODEL
The mo.py script is the universal entry point that can deduce the framework that has produced the input model by a standard extension of the model file:
.caffemodel - Caffe* models
.pb - TensorFlow* models
.params - MXNet* models
.onnx - ONNX* models
.nnet - Kaldi* models.
If the model files do not have standard extensions, you can use the --framework {tf,caffe,kaldi,onnx,mxnet} option to specify the framework type explicitly.
For example, the following commands are equivalent-
python3 mo.py --input_model /user/models/model.pb
python3 mo.py --framework tf --input_model /user/models/model.pb
Some models require using additional arguments to specify conversion parameters, such as --scale, --scale_values, --mean_values, --mean_file.
To learn more about general model optimizer parameters, refer to Converting a Model Using General Conversion Parameters.
