# Converting YOLO* Models to the Intermediate Representation (IR) {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow}

This tutorial explains how to convert real-time object detection YOLOv1\*, YOLOv2\*, and YOLOv3\* public models to the Intermediate Representation (IR). All YOLO\* models are originally implemented in the DarkNet\* framework and consist of two files:
* `.cfg` file with model configurations  
* `.weights` file with model weights

Depending on a YOLO model version, the Model Optimizer converts it differently:

- YOLOv3 has several implementations. This tutorial uses a TensorFlow implementation of YOLOv3 model, which can be directly converted to the IR.
- YOLOv1 and YOLOv2 models must be first converted to TensorFlow\* using DarkFlow\*.

## <a name="yolov3-to-ir"></a>Convert YOLOv3 Model to IR

On GitHub*, you can find several public versions of TensorFlow YOLOv3 model implementation. This tutorial explains how to convert YOLOv3 model from
the [https://github.com/mystic123/tensorflow-yolo-v3](https://github.com/mystic123/tensorflow-yolo-v3) repository (commit ed60b90) to IR , but the process is similar for other versions of TensorFlow YOLOv3 model.

### <a name="yolov3-overview"></a>Overview of YOLOv3 Model Architecture
Originally, YOLOv3 model includes feature extractor called `Darknet-53` with three branches at the end that make detections at three different scales. These branches must end with the YOLO `Region` layer.

`Region` layer was first introduced in the DarkNet framework. Other frameworks, including TensorFlow, do not have the
`Region` implemented as a single layer, so every author of public YOLOv3 model creates it using
simple layers. This badly affects performance. For this reason, the main idea of YOLOv3 model conversion to IR is to cut off these
custom `Region`-like parts of the model and complete the model with the `Region` layers where required.

### Dump YOLOv3 TensorFlow\* Model
To dump TensorFlow model out of  [https://github.com/mystic123/tensorflow-yolo-v3](https://github.com/mystic123/tensorflow-yolo-v3) GitHub repository (commit ed60b90), follow the instructions below:

1. Clone the repository:<br>
```sh
git clone https://github.com/mystic123/tensorflow-yolo-v3.git
cd tensorflow-yolo-v3
```
2. (Optional) Checkout to the commit that the conversion was tested on:<br>
```sh
git checkout ed60b90
```
3. Download [coco.names](https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names) file from the DarkNet website **OR** use labels that fit your task.
4. Download the [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) (for the YOLOv3 model) or [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) (for the YOLOv3-tiny model) file **OR** use your pretrained weights with the same structure
5. Run a converter:
- for YOLO-v3:
```sh
python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3.weights
```
- for YOLOv3-tiny:
```sh
python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3-tiny.weights --tiny
```
At this step, you may receive a warning like `WARNING:tensorflow:Entity <...> could not be transformed and will be executed as-is.`. To workaround this issue, switch to gast 0.2.2 with the following command:
```sh
pip3 install --user gast==0.2.2
```

If you have YOLOv3 weights trained for an input image with the size different from 416 (320, 608 or your own), please provide the `--size` key with the size of your image specified while running the converter. For example, run the following command for an image with size 608:
```sh
python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3_608.weights --size 608
```

### Convert YOLOv3 TensorFlow Model to the IR

To solve the problems explained in the <a href="#yolov3-overview">YOLOv3 architecture overview</a> section, use the `yolo_v3.json` or `yolo_v3_tiny.json` (depending on a model) configuration file with custom operations located in the `<OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf` repository.

It consists of several attributes:<br>
```sh
[
  {
    "id": "TFYOLOV3",
    "match_kind": "general",
    "custom_attributes": {
      "classes": 80,
      "anchors": [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326],
      "coords": 4,
      "num": 9,
      "masks":[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
      "entry_points": ["detector/yolo-v3/Reshape", "detector/yolo-v3/Reshape_4", "detector/yolo-v3/Reshape_8"]
    }
  }
]
```
where:
- `id` and `match_kind` are parameters that you cannot change.
- `custom_attributes` is a parameter that stores all the YOLOv3 specific attributes:
    - `classes`, `coords`, `num`, and `masks` are attributes that you should copy from the configuration file
    file that was used for model training. If you used DarkNet officially shared weights,
    you can use `yolov3.cfg` or `yolov3-tiny.cfg` configuration file from https://github.com/pjreddie/darknet/tree/master/cfg. Replace the default values in `custom_attributes` with the parameters that
    follow the `[yolo]` titles in the configuration file.
    - `anchors` is an optional parameter that is not used while inference of the model, but it used in a demo to parse `Region` layer output
    - `entry_points` is a node name list to cut off the model and append the Region layer with custom attributes specified above.


To generate the IR of the YOLOv3 TensorFlow model, run:<br>
```sh
python3 mo_tf.py
--input_model /path/to/yolo_v3.pb
--tensorflow_use_custom_operations_config $MO_ROOT/extensions/front/tf/yolo_v3.json
--batch 1
```

To generate the IR of the YOLOv3-tiny TensorFlow model, run:<br>
```sh
python3 mo_tf.py
--input_model /path/to/yolo_v3_tiny.pb
--tensorflow_use_custom_operations_config $MO_ROOT/extensions/front/tf/yolo_v3_tiny.json
--batch 1
```

where:

* `--batch` defines shape of model input. In the example, `--batch` is equal to 1, but you can also specify other integers larger than 1.
* `--tensorflow_use_custom_operations_config` adds missing `Region` layers to the model. In the IR, the `Region` layer has name `RegionYolo`.

> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).

OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection C++ Demo](@ref omz_demos_object_detection_demo_ssd_async_README).

## Convert YOLOv1 and YOLOv2 Models to the IR

Before converting  Choose a YOLOv1 or YOLOv2 model version that best suits your task. Download model configuration file and corresponding weight file:
* From [DarkFlow repository](https://github.com/thtrieu/darkflow): configuration files are stored in the `cfg` directory, links to weight files are given in the `README.md` file. The files from this repository are adapted for conversion to TensorFlow using DarkFlow.
* From DarkNet website and repository: configuration files are stored in the `cfg` directory of the [repository](https://github.com/pjreddie/darknet), links to weight files are given on the [YOLOv1](https://pjreddie.com/darknet/yolov1/) and [YOLOv2](https://pjreddie.com/darknet/yolov2/) websites.

To convert DarkNet YOLOv1 and YOLOv2 models to IR,  follow the next steps:

1. <a href="#install-darkflow">Install DarkFlow </a>
2. <a href="#yolov1-v2-to-tf">Convert DarkNet YOLOv1 or YOLOv2 model to TensorFlow</a> using DarkFlow
3. <a href="#yolov1-v2-to-ir">Convert TensorFlow YOLOv1 or YOLOv2 model to IR</a>

#### <a name="install-darkflow"></a>Install DarkFlow*

You need DarkFlow to convert YOLOv1 and YOLOv2 models to TensorFlow. To install DarkFlow:
1. Install DarkFlow [required dependencies](https://github.com/thtrieu/darkflow#dependencies).
2. Clone DarkFlow git repository:<br>
```sh
git clone https://github.com/thtrieu/darkflow.git
```
3. Go to the root directory of the cloned repository:<br>
```sh
cd darkflow
```
4. Install DarkFlow using the instructions from the `README.md` file in the [DarkFlow repository](https://github.com/thtrieu/darkflow/blob/master/README.md#getting-started).

#### <a name="yolov1-v2-to-tf"></a>Convert DarkNet\* YOLOv1 or YOLOv2 Model to TensorFlow\*

To convert YOLOv1 or YOLOv2 model to TensorFlow, go to the root directory of the cloned DarkFlow repository and run the following command:<br>
```sh
python3 ./flow --model <path_to_model>/<model_name>.cfg --load <path_to_model>/<model_name>.weights --savepb
```

If the model was successfully converted, you can find the `<model_name>.meta` and `<model_name>.pb` files
in `built_graph`  subdirectory of the cloned DarkFlow repository.

File `<model_name>.pb` is a TensorFlow representation of the YOLO model.

#### <a name="yolov1-v2-to-ir"></a>Convert TensorFlow YOLOv1 or YOLOv2 Model to the IR

Converted TensorFlow YOLO model is missing `Region` layer and its parameters. Original YOLO `Region` layer parameters are stored in the configuration `<path_to_model>/<model_name>.cfg`
file under the `[region]` title.   

To recreate the original model structure, use the corresponding yolo `.json` configuration file with custom operations and `Region` layer
parameters when converting the model to the IR. This file is located in the `<OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf` directory.

If chosen model has specific values of this parameters,
create another configuration file with custom operations and use it for conversion.

To generate the IR of the YOLOv1 model, provide TensorFlow YOLOv1 or YOLOv2 model to the Model Optimizer with the following parameters:<br>
```sh
python3 ./mo_tf.py
--input_model <path_to_model>/<model_name>.pb       \
--batch 1                                       \
--scale 255 \
--tensorflow_use_custom_operations_config <OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf/<yolo_config>.json
```
where:

* `--batch` defines shape of model input. In the example, `--batch` is equal to 1, but you can also specify other integers larger than 1.
* `--scale` specifies scale factor that input values will be divided by. 
The model was trained with input values in the range `[0,1]`. OpenVINO&trade; toolkit samples read input images as values in `[0,255]` range, so the scale 255 must be applied.
* `--tensorflow_use_custom_operations_config` adds missing `Region` layers to the model. In the IR, the `Region` layer has name `RegionYolo`.
For other applicable parameters, refer to [Convert Model from TensorFlow](../Convert_Model_From_TensorFlow.md).

> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
