# Develop your own application
Source: [https://docs.qualcomm.com/doc/80-70015-15B/topic/develop-own-app.html](https://docs.qualcomm.com/doc/80-70015-15B/topic/develop-own-app.html)
Developers can write AI/ML applications, using one of the following
methods.
- Using Qualcomm IM SDK – Developers can leverage existing reference applications to
deploy their models or extend capabilities of Qualcomm IM SDK to add support for
their models and use cases. More details are given in the next section.
- Native C/C++ apps using AI SDK APIs – Developers can leverage AI SDK samples for
native AI/ML application development. Developers need to implement
pre-/postprocessing in C/C++.
Qualcomm IM SDK, is a unified SDK enabling seamless multimedia and artificial
intelligence/machine learning (AI/ML) application deployment. This SDK uses GStreamer,
an open-source multimedia framework and exposes easy APIs and plugins in both multimedia
and ML domains. For details, refer to the [official SDK documentation](https://docs.qualcomm.com/bundle/publicresource/topics/80-70015-50/overview.html).
Qualcomm IM SDK implements the following plugins for AI/ML applications. A complete list
of plugins can be found in the Qualcomm IM SDK [plugin documentation](https://docs.qualcomm.com/bundle/publicresource/topics/80-70015-50/qim-sdk-plugins.html).
**Download source code for development**
The eSDK (extensible SDK) needs to be setup to develop application/plugin code. See [Compile application,
Qualcomm IM SDK](https://docs.qualcomm.com/doc/80-70015-15B/topic/compile-app-esdk.html) for instructions on setting up the eSDK and downloading and
compiling the source code.
Below are the ML plugins for pre-/postprocessing available with Qualcomm IM SDK.
Developers can use these plugins to develop their own use case.
| Plugin | Functionality |
| --- | --- |
| qtimlvconverter | Transforms incoming video buffers into neural-network tensors while performing required format conversion and resizing. |
| qtimlvclassification | Performs postprocessing of output tensors for classification use cases. |
| qtimlvdetection | Performs postprocessing of output tensors for detection use cases. |
| qtimlvsegmentation | Performs postprocessing of output tensors for pixel-level use cases, like image segmentation, depth-map, etc. |
| qtimlvpose | Performs postprocessing of output tensors for pose estimation use cases. |
| qtimlvsuperresolution | Performs postprocessing of the output tensors for video super resolution use cases. |
Qualcomm IM SDK, currently 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. |
Note: A list of verified models from Qualcomm AI Hub is available in a different table
below. Developers 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.
Last Published: Jan 21, 2026
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