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Add iSeeBetter to supported PyTorch models list (#6177) (#6278)

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Maxim Vafin
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# Converting a PyTorch* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
## Supported Topologies
Here is the list of models that are tested and guaranteed to be supported. However, you can also use these instructions to convert PyTorch\* models that are not presented in the list.
* [Torchvision Models](https://pytorch.org/docs/stable/torchvision/index.html): alexnet, densenet121, densenet161,
densenet169, densenet201, resnet101, resnet152, resnet18, resnet34, resnet50, vgg11, vgg13, vgg16, vgg19.
The models can be converted using [regular instructions](#typical-pytorch).
* [Cadene Pretrained Models](https://github.com/Cadene/pretrained-models.pytorch): alexnet, fbresnet152, resnet101,
resnet152, resnet18, resnet34, resnet152, resnet18, resnet34, resnet50, resnext101_32x4d, resnext101_64x4d, vgg11.
The models can be converted using [regular instructions](#typical-pytorch).
* [ESPNet Models](https://github.com/sacmehta/ESPNet/tree/master/pretrained) can be converted using [regular instructions](#typical-pytorch).
* [MobileNetV3](https://github.com/d-li14/mobilenetv3.pytorch) can be converted using [regular instructions](#typical-pytorch).
* [iSeeBetter](https://github.com/amanchadha/iSeeBetter) can be converted using [regular instructions](#typical-pytorch).
Please refer to [`iSeeBetterTest.py`](https://github.com/amanchadha/iSeeBetter/blob/master/iSeeBetterTest.py) script for code to initialize the model.
* F3Net topology can be converted using steps described in [Convert PyTorch\* F3Net to the IR](pytorch_specific/Convert_F3Net.md)
instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
* QuartzNet topologies from [NeMo project](https://github.com/NVIDIA/NeMo) can be converted using steps described in
[Convert PyTorch\* QuartzNet to the IR](pytorch_specific/Convert_QuartzNet.md) instruction which is used instead of
steps 2 and 3 of [regular instructions](#typical-pytorch).
* YOLACT topology can be converted using steps described in [Convert PyTorch\* YOLACT to the IR](pytorch_specific/Convert_YOLACT.md)
instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
* [RCAN](https://github.com/yulunzhang/RCAN) topology can be converted using steps described in [Convert PyTorch\* RCAN to the IR](pytorch_specific/Convert_RCAN.md)
instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
* [BERT_NER](https://github.com/kamalkraj/BERT-NER) topology can be converted using steps described in [Convert PyTorch* BERT-NER to the IR](pytorch_specific/Convert_Bert_ner.md)
instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
## Typical steps to convert PyTorch\* model <a name="typical-pytorch"></a>
PyTorch* framework is supported through export to ONNX\* format. A summary of the steps for optimizing and deploying a model that was trained with the PyTorch\* framework:
1. [Export PyTorch model to ONNX\*](#export-to-onnx).
2. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for ONNX\*.
1. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for ONNX\*.
2. [Export PyTorch model to ONNX\*](#export-to-onnx).
3. [Convert an ONNX\* model](Convert_Model_From_ONNX.md) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
4. Test the model in the Intermediate Representation format using the [Inference Engine](../../../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided [sample applications](../../../IE_DG/Samples_Overview.md).
5. [Integrate](../../../IE_DG/Samples_Overview.md) the Inference Engine in your application to deploy the model in the target environment.
## Supported Topologies
Here is the list of models that were tested and are guaranteed to be supported.
It is not a full list of models that can be converted to ONNX\* and to IR.
|Package Name|Supported Models|
|:----|:----|
| [Torchvision Models](https://pytorch.org/docs/stable/torchvision/index.html) | alexnet, densenet121, densenet161, densenet169, densenet201, resnet101, resnet152, resnet18, resnet34, resnet50, vgg11, vgg13, vgg16, vgg19 |
| [Pretrained Models](https://github.com/Cadene/pretrained-models.pytorch) | alexnet, fbresnet152, resnet101, resnet152, resnet18, resnet34, resnet152, resnet18, resnet34, resnet50, resnext101_32x4d, resnext101_64x4d, vgg11 |
**Other supported topologies**
* [ESPNet Models](https://github.com/sacmehta/ESPNet/tree/master/pretrained)
* [MobileNetV3](https://github.com/d-li14/mobilenetv3.pytorch)
* F3Net topology can be converted using [Convert PyTorch\* F3Net to the IR](pytorch_specific/Convert_F3Net.md) instruction.
* QuartzNet topologies from [NeMo project](https://github.com/NVIDIA/NeMo) can be converted using [Convert PyTorch\* QuartzNet to the IR](pytorch_specific/Convert_QuartzNet.md) instruction.
* YOLACT topology can be converted using [Convert PyTorch\* YOLACT to the IR](pytorch_specific/Convert_YOLACT.md) instruction.
* [RCAN](https://github.com/yulunzhang/RCAN) topologies can be converted using [Convert PyTorch\* RCAN to the IR](pytorch_specific/Convert_RCAN.md) instruction.
* [BERT_NER](https://github.com/kamalkraj/BERT-NER) can be converted using [Convert PyTorch* BERT-NER to the IR](pytorch_specific/Convert_Bert_ner.md) instruction.
## Export PyTorch\* Model to ONNX\* Format <a name="export-to-onnx"></a>
PyTorch models are defined in a Python\* code, to export such models use `torch.onnx.export()` method.
PyTorch models are defined in a Python\* code, to export such models use `torch.onnx.export()` method. Usually code to
evaluate or test the model is provided with the model code and can be used to initialize and export model.
Only the basics will be covered here, the step to export to ONNX\* is crucial but it is covered by PyTorch\* framework.
For more information, please refer to [PyTorch\* documentation](https://pytorch.org/docs/stable/onnx.html).