Add convert GluonCV docs (#1413)

* Add convert GluonCV docs

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -38,7 +38,8 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 **Other supported topologies**
 
-* Style transfer [model](https://github.com/zhaw/neural_style) can be converted using [instruction](mxnet_specific/Convert_Style_Transfer_From_MXNet.md),
+* [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) can be converted using the following [instructions](mxnet_specific/Convert_GluonCV_Models.md).
+* [Style transfer model](https://github.com/zhaw/neural_style) can be converted using the following [instructions](mxnet_specific/Convert_Style_Transfer_From_MXNet.md).
 
 ## Convert an MXNet* Model <a name="ConvertMxNet"></a>
 

docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_GluonCV_Models.md

@@ -0,0 +1,26 @@
+# Converting GluonCV* Models {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models}
+
+This document provides the instructions and examples on how to use Model Optimizer to convert [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) to IR.
+
+1. Choose the topology available from the [GluonCV Moodel Zoo](https://gluon-cv.mxnet.io/model_zoo/detection.html) and export to the MXNet format using the GluonCV API. For example, for the `ssd_512_mobilenet1.0` topology: 
+```python
+from gluoncv import model_zoo, data, utils
+from gluoncv.utils import export_block
+net = model_zoo.get_model('ssd_512_mobilenet1.0_voc', pretrained=True)
+export_block('ssd_512_mobilenet1.0_voc', net, preprocess=True, layout='HWC')
+```
+As a result, you will get an MXNet model representation in `ssd_512_mobilenet1.0.params` and `ssd_512_mobilenet1.0.json` files generated in the current directory. 
+2. Run the Model Optimizer tool specifying the `--enable_ssd_gluoncv` option. Make sure the `--input_shape` parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrates running the Model Optimizer for the SSD and YOLO-v3 models trained with the NHWC layout and located in the `<model_directory>`:
+* **For GluonCV SSD topologies:**
+```sh
+python3 mo_mxnet.py --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data
+```
+* **For YOLO-v3 topology:**
+   * To convert the model:
+   ```sh
+   python3 mo_mxnet.py --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3]
+   ```
+   * To convert the model with replacing the subgraph with RegionYolo layers:
+   ```sh
+   python3 mo_mxnet.py --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "mo/extensions/front/mxnet/yolo_v3_mobilenet1_voc.json"
+   ```

docs/doxygen/ie_docs.xml

@@ -62,6 +62,7 @@
                             </tab>
                             <tab type="usergroup" title="Converting a MXNet* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet">
                                 <tab type="user" title="Converting a Style Transfer Model from MXNet" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet"/>
+                                <tab type="user" title="Converting GluonCV* Models" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models"/>
                             </tab>
                             <tab type="usergroup" title="Converting Your Kaldi* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi">
                                 <tab type="user" title="Convert Kaldi* ASpIRE Chain Time Delay Neural Network (TDNN) Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model"/>