Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
Model Optimizer process assumes you have a network model trained using supported deep learning frameworks: Caffe*, TensorFlow*, Kaldi*, MXNet* or converted to the ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the network, which can be inferred with the Inference Engine.
NOTE: Model Optimizer does not infer models. Model Optimizer is an offline tool that runs before the inference takes place.
The scheme below illustrates the typical workflow for deploying a trained deep learning model:
The IR is a pair of files describing the model:
.xml- Describes the network topology
.bin- Contains the weights and biases binary data.
Below is a simple command running Model Optimizer to generate an IR for the input model:
To learn about all Model Optimizer parameters and conversion technics, see the Converting a Model to IR page.
TIP: You can quick start with the Model Optimizer inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI that enables you to import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment on various Intel® platforms.
|Model Optimizer Concept. |
|Model Optimizer Basic|
|Choosing the Right Precision. |