# Model Optimizer

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:

 ![workflow\_steps.png](https://docs.openvinotoolkit.org/latest/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:

* `.xml` - Describes the network topology
* `.bin` - Contains the weights and biases binary data.

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

* TensorFlow\*
  * Added support for the following TensorFlow\* topologies: quantized image classification topologies, TensorFlow Object Detection API RFCN version 1.10+, Tiny YOLO v3, full DeepLab v3 without need to remove pre-processing part.
  * Added support for batch more than 1 for TensorFlow\* Object Detection API Faster/Mask RCNNs and RFCNs.
  * Added support of the following TensorFlow\* operations: ReverseSequence, ReverseV2, ZerosLike, Exp, Sum. The full list of supported operations is defined in the [Supported Framework Layers](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html).
* Caffe\*
  * Added support of the following Caffe\* operations: StridedSlice, Bias. The full list of supported operations is defined in the [Supported Framework Layers](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html).
  * Added support for the following Caffe\* topologies: RefineDet.
  * Caffe fallback for shape inference is deprecated.
* MXNet\*
  * Added support of the following MXNet\* operations: Embedding, Zero with "shape" equal to 0, RNN with mode="gru", "rnn\_relu", "rnn\_tanh" and parameters "num\_layer", "bidirectional", "clip". The full list of supported operations is defined in the [Supported Framework Layers](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html).
  * Added support of bidirectional LSTMs.
  * Added support of LSTMs with batch more than 1 and multi layers.
  * Fixed loading Gluon models with attributes equal to "None".
* ONNX\*
  * Added support for the following ONNX\* operations: ArgMax, Clip, Exp, DetectionOutput, PriorBox, RNN, GRU with parameters "direction", "activations", "clip", "hidden\_size", "linear\_before\_reset", LSTM support extended for parameters "activations", "clip", "direction" attributes supported. The full list of supported operations is defined in the [Supported Framework Layers](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html).
  * Extended support of ConvTranspose operation to support ND.
  * Resolved issue with Gemm operation with biases.
* Common
  * Implemented experimental feature to generate the IR with "Shape" layers. The feature makes possible to change model input shapes in the Inference Engine (using dedicated reshape API) instead of re-generating models in the Model Optimizer. The feature is enabled by providing command line parameter `--keep_shape_ops` to the Model Optimizer.
  * Introduced new graph transformation API to perform graph modifications in the Model Optimizer.
  * Added ability to enable/disable the Model Optimizer extension using the environment variables `MO_ENABLED_TRANSFORMS` and `MO_DISABLED_TRANSFORMS` respectively.
  * Fixed issue with Deconvolution shape inference for case with stride not equal to 1.
  * Added Concat optimization pass that removes excess edges between Concat operations.
  * Updated the IR version from 4 to 5. The IR of version 2 can be generated using the `--generate_deprecated_IR_V2` command line parameter.