> ## Documentation Index
> Fetch the complete documentation index at: https://dragonwingdocs.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Download source code for development
> Set up the Qualcomm SDK and download the source code to develop AI/ML application and plugin code.
The Standard SDK (SDK) is required to develop application and plugin code. See the [Concepts of Building Native GStreamer Application](https://imsdkdocs.qualcomm.com/qimsdk-overview/building-native-app) for instructions on setting up the SDK and downloading and compiling the source code.
AI/ML applications are built from the IM SDK's preprocessing (`qtimlvconverter`), inference (`qtimltflite`, `qtimlqnn`, `qtimlonnx`, `qtimlsnpe`), and post-processing (`qtimlpostprocess`) plugins. For how these map to the pipeline and hardware engines, see the [IM SDK overview](../topic/develop-your-own-application-im-sdk); for the complete plugin catalog, see the [plugin reference](https://imsdkdocs.qualcomm.com/plugin-reference/introduction) in Discover SDKs.
**Preprocessing plugin**
| Plugin | Functionality |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `qtimlvconverter` | Transforms incoming video buffers into neural-network tensors while performing required format conversion and resizing. |
**Postprocessing plugin**
| Plugin | Functionality |
| ------------------ | ------------------------------------------------------------------------------------------------------------------ |
| `qtimlpostprocess` | A customizable plugin that provides a library interface for postprocessing the tensor output of inference plugins. |
The following module types are supported by the `qtimlpostprocess` plugin. Each module type handles postprocessing for a specific use case.
| Module Type | Functionality |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `audio-classification` | Performs postprocessing of output tensors for audio classification use cases. |
| `image-classification` | Performs postprocessing of output tensors for image classification use cases. |
| `image-segmentation` | Performs postprocessing of output tensors for pixel-level use cases such as image segmentation and depth mapping. |
| `object-detection` | Performs postprocessing of output tensors for object detection use cases. |
| `pose-estimation` | Performs postprocessing of output tensors for pose estimation use cases. |
| `super-resolution` | Performs postprocessing of output tensors for video super resolution use cases. |
The `qtimlpostprocess` plugin supports the following use cases and related models.
| **Use cases supported by Qualcomm IM SDK** | **Supported Models** |
| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- |
| Classification | Models like Mobilenet. Currently Qualcomm AI Hub has 11 classification models supported. New models will keep getting added to AI Hub. |
| Detection | Models like ssd-mobilenet, yolov5, yolo-nas, and yolov8 |
| Segmentation | Models like deeplabv3\_resnet and ffnet |
| Pose detection | Models like posenet\_mobilenet |
| Super resolution | Models like QuickSRNet, XLSR, etc. |
For verified Qualcomm AI Hub models and ready-to-run pipeline examples, see [Discover SDKs → IM SDKs](https://imsdkdocs.qualcomm.com).
You can use many other models with similar postprocessing requirements. However, it is recommended to verify postprocessing support in the relevant Qualcomm IM SDK plugins before integrating your own model.