[MMRotate](https://github.com/open-mmlab/mmrotate) is an open-source toolbox for rotated object detection based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
- Adding `$(pwd)/build/lib` to `PYTHONPATH` is for importing mmdeploy SDK python module - `mmdeploy_python`, which will be presented in chapter [SDK model inference](#sdk-model-inference).
- When [inference onnx model by ONNX Runtime](#backend-model-inference), it requests ONNX Runtime library be found. Thus, we add it to `LD_LIBRARY_PATH`.
**Method III:** Build from source
If neither **I** nor **II** meets your requirements, [building mmdeploy from source](../01-how-to-build/build_from_source.md) is the last option.
You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/tools/deploy.py) to convert mmrotate models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/02-how-to-run/convert_model.md#usage).
The command below shows an example about converting `rotated-faster-rcnn` model to onnx model that can be inferred by ONNX Runtime.
```shell
cd mmdeploy
# download rotated-faster-rcnn model from mmrotate model zoo
It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmrotate) of all supported backends for mmrotate. The config filename pattern is:
- **{backend}:** inference backend, such as onnxruntime, tensorrt, pplnn, ncnn, openvino, coreml etc.
- **{precision}:** fp16, int8. When it's empty, it means fp32
- **{static | dynamic}:** static shape or dynamic shape
- **{shape}:** input shape or shape range of a model
Therefore, in the above example, you can also convert `rotated-faster-rcnn` to other backend models by changing the deployment config file `rotated-detection_onnxruntime_dynamic` to [others](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmrotate), e.g., converting to tensorrt-fp16 model by `rotated-detection_tensorrt-fp16_dynamic-320x320-1024x1024.py`.
```{tip}
When converting mmrotate models to tensorrt models, --device should be set to "cuda"
Before moving on to model inference chapter, let's know more about the converted model structure which is very important for model inference.
The converted model locates in the working directory like `mmdeploy_models/mmrotate/ort` in the previous example. It includes:
```
mmdeploy_models/mmrotate/ort
├── deploy.json
├── detail.json
├── end2end.onnx
└── pipeline.json
```
in which,
- **end2end.onnx**: backend model which can be inferred by ONNX Runtime
- \***.json**: the necessary information for mmdeploy SDK
The whole package **mmdeploy_models/mmrotate/ort** is defined as **mmdeploy SDK model**, i.e., **mmdeploy SDK model** includes both backend model and inference meta information.
Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/demo).
