# 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"
   ```
