openvino/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

# Model Optimizer Developer Guide {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide}

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 a supported deep learning framework. The scheme below illustrates the typical workflow for deploying a trained deep learning model:

![](img/workflow_steps.png)

Model Optimizer produces an Intermediate Representation (IR) of the network, which can be read, loaded, and inferred with the Inference Engine. The Inference Engine API offers a unified API across a number of supported Intel® platforms. The Intermediate Representation is a pair of files describing the model:

*  <code>.xml</code> - Describes the network topology

*  <code>.bin</code> - Contains the weights and biases binary data.

## What's New in the Model Optimizer in this Release?

* Common changes:
    * Implemented generation of a compressed OpenVINO IR suitable for INT8 inference, which takes up to 4 times less disk space than an expanded one. Use the `--disable_weights_compression` Model Optimizer command-line parameter to get an expanded version.
    * Implemented an optimization transformation to replace a sub-graph with the `Erf` operation into the `GeLU` operation.
    * Implemented an optimization transformation to replace an upsamping pattern that is represented as a sequence of `Split` and `Concat` operations to a single `Interpolate` operation.
    * Fixed a number of Model Optimizer bugs to generate reshape-able IRs of many models with the command line parameter `--keep_shape_ops`.
    * Fixed a number of Model Optimizer transformations to set operations name in an IR equal to the original framework model operation name.
    * The following operations are no longer generated with `version="opset1"`: `MVN`, `ROIPooling`, `ReorgYolo`. They became a part of new `opset2` operation set and generated with `version="opset2"`. Before this fix, the operations were generated with `version="opset1"` by mistake, they were not a part of `opset1` nGraph namespace; `opset1` specification was fixed accordingly.

* ONNX*:
    * Added support for the following operations: `MeanVarianceNormalization` if normalization is performed over spatial dimensions.

* TensorFlow*:
    * Added support for the TensorFlow Object Detection models version 1.15.X.
    * Added support for the following operations: `BatchToSpaceND`, `SpaceToBatchND`, `Floor`.

* MXNet*:
    * Added support for the following operations:
        * `Reshape` with input shape values equal to -2, -3, and -4.

> **NOTE:** 
> [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).

## Table of Content

* [Introduction to OpenVINO™ Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)

* [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md)
    * [Configuring Model Optimizer](prepare_model/Config_Model_Optimizer.md)
    * [Converting a Model to Intermediate Representation (IR)](prepare_model/convert_model/Converting_Model.md)
        * [Converting a Model Using General Conversion Parameters](prepare_model/convert_model/Converting_Model_General.md)
        * [Converting Your Caffe* Model](prepare_model/convert_model/Convert_Model_From_Caffe.md)
        * [Converting Your TensorFlow* Model](prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
            * [Converting BERT from TensorFlow](prepare_model/convert_model/tf_specific/Convert_BERT_From_Tensorflow.md)
            * [Converting GNMT from TensorFlow](prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md)
            * [Converting YOLO from DarkNet to TensorFlow and then to IR](prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md)
            * [Converting Wide and Deep Models from TensorFlow](prepare_model/convert_model/tf_specific/Convert_WideAndDeep_Family_Models.md)
            * [Converting FaceNet from TensorFlow](prepare_model/convert_model/tf_specific/Convert_FaceNet_From_Tensorflow.md)
            * [Converting DeepSpeech from TensorFlow](prepare_model/convert_model/tf_specific/Convert_DeepSpeech_From_Tensorflow.md)
            * [Converting Language Model on One Billion Word Benchmark from TensorFlow](prepare_model/convert_model/tf_specific/Convert_lm_1b_From_Tensorflow.md)
            * [Converting Neural Collaborative Filtering Model from TensorFlow*](prepare_model/convert_model/tf_specific/Convert_NCF_From_Tensorflow.md)

            * [Converting TensorFlow* Object Detection API Models](prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md)
            * [Converting TensorFlow*-Slim Image Classification Model Library Models](prepare_model/convert_model/tf_specific/Convert_Slim_Library_Models.md)
            * [Converting CRNN Model from TensorFlow*](prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md)
        * [Converting Your MXNet* Model](prepare_model/convert_model/Convert_Model_From_MxNet.md)
            * [Converting a Style Transfer Model from MXNet](prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md)
        * [Converting Your Kaldi* Model](prepare_model/convert_model/Convert_Model_From_Kaldi.md)
        * [Converting Your ONNX* Model](prepare_model/convert_model/Convert_Model_From_ONNX.md)
            * [Converting Mask-RCNN ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md)
            * [Converting DLRM ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_DLRM.md)
        * [Model Optimizations Techniques](prepare_model/Model_Optimization_Techniques.md)
        * [Cutting parts of the model](prepare_model/convert_model/Cutting_Model.md)
        * [Sub-graph Replacement in Model Optimizer](prepare_model/customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md)
            * [(Deprecated) Case-Study: Converting SSD models created with the TensorFlow* Object Detection API](prepare_model/customize_model_optimizer/TensorFlow_SSD_ObjectDetection_API.md)
            * [(Deprecated) Case-Study: Converting Faster R-CNN models created with the TensorFlow* Object Detection API](prepare_model/customize_model_optimizer/TensorFlow_Faster_RCNN_ObjectDetection_API.md)
        * [Supported Framework Layers](prepare_model/Supported_Frameworks_Layers.md)
        * [Intermediate Representation and Operation Sets](IR_and_opsets.md)
        * [Operations Specification](../ops/opset.md)
        * [Intermediate Representation suitable for INT8 inference](prepare_model/convert_model/IR_suitable_for_INT8_inference.md)

    * [Custom Layers in Model Optimizer](prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md)
        * [Extending Model Optimizer with New Primitives](prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md)
        * [Legacy Mode for Caffe* Custom Layers](prepare_model/customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md)

    * [Model Optimizer Frequently Asked Questions](prepare_model/Model_Optimizer_FAQ.md)

* [Known Issues](Known_Issues_Limitations.md)

**Typical Next Step:** [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)