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...DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md
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# Converting RetinaNet Model from TensorFlow* to the Intermediate Representation {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_RetinaNet_From_Tensorflow} | ||
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This tutorial explains how to convert RetinaNet model to the Intermediate Representation (IR). | ||
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[Public RetinaNet model](https://github.com/fizyr/keras-retinanet) does not contain pretrained TensorFlow\* weights. | ||
To convert this model to the TensorFlow\* format, you can use [Reproduce Keras* to TensorFlow* Conversion tutorial](https://docs.openvinotoolkit.org/latest/omz_models_model_retinanet_tf.html). | ||
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After you convert the model to TensorFlow* format, run the Model Optimizer command below: | ||
```sh | ||
python mo.py --input "input_1[1 1333 1333 3]" --input_model retinanet_resnet50_coco_best_v2.1.0.pb --data_type FP32 --transformations_config ./extensions/front/tf/retinanet.json | ||
``` | ||
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Where `transformations_config` command-line parameter specifies the configuration json file containing model conversion hints for the Model Optimizer. | ||
The json file contains some parameters that need to be changed if you train the model yourself. It also contains information on how to match endpoints | ||
to replace the subgraph nodes. After the model is converted to IR, the output nodes will be replaced with DetectionOutput layer. |
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