# AI/ML sample applications

Source: [https://docs.qualcomm.com/doc/80-70014-50/topic/ai-ml-sample-applications.html](https://docs.qualcomm.com/doc/80-70014-50/topic/ai-ml-sample-applications.html)

The AI/ML sample applications provide custom use cases that show how to use the AI/ML
        features of the Qualcomm Linux platform

Before you run the AI sample applications, ensure that the model and label files are
            available on the device.

## Download the model and label files

To download and push the model and label files to the device, do the following on the
                Linux host:

1. To download the model and label files, run the following command:

        wget https://github.com/quic/sample-apps-for-qualcomm-linux/releases/download/v0.1.0/v0.1.0.tar.gzCopy to clipboard
2. To extract the files, run the following command:

        tar -zxvf v0.1.0.tar.gzCopy to clipboard
3. To push the model and label files to the device, do the following:
    1. Enable SSH in Permissive mode to securely log into the host device. For
                            instructions, see [How to SSH?](https://docs.qualcomm.com/bundle/publicresource/topics/80-70014-254/how_to.html#how-to-ssh-)
    2. Run the following command to push the
                            files:

            scp -r v0.1.0/* root@<IP address of target device>:/opt/Copy to clipboard

Note: The following sample applications can be built using the
                default models provided by Qualcomm. If you want to *Bring Your Own Model*, see
                    [AI Developer Workflow](https://docs.qualcomm.com/bundle/publicresource/topics/80-70014-15B).

- **[Classification](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-classification.html)**  

The **gst-ai-classification** application enables you to recognize the subject in         the image. The use cases use Qualcomm Neural Processing SDK runtime or TensorFlow Lite         (TFLite) runtime.
- **[Object detection](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-object-detection.html)**  

The **gst-ai-object-detection** application enables you to detect objects within         images and videos. The use cases demonstrate the execution of [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics), and [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) using the Qualcomm Neural Processing SDK         runtime.
- **[Pose detection](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-pose-detection.html)**  

 The **gst-ai-pose-detection** application enables you to detect the body pose of         the subject in an image or video. The use cases use a video stream from a camera, leverage         TFLite for pose detection, and display the results on the screen.
- **[Image segmentation](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-segmentation.html)**  

The **gst-ai-segmentation** application enables you to divide an image into         different and meaningful parts or segments and assign a label to each homogenous segment         based on the similarity of the attributes. The application shows how to use Qualcomm Neural         Processing SDK runtime and TFLite runtime for image segmentation.
- **[Parallel AI fusion](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-parallel-inference.html)**  

The **gst-ai-parallel-inference** application enables you to perform object         detection, object classification, pose detection, and image segmentation on a live camera         stream. The use cases use Qualcomm Neural Processing SDK runtime for object detection and         image segmentation, and TFLite runtime for classification and pose detection.
- **[Multi-input AI inferencing](https://docs.qualcomm.com/doc/80-70014-50/topic/gst-ai-multi-input-output-object-detection.html)**  

The **gst-ai-multi-input-output-object-detection** application enables you to         perform object detection on multiple video streams from different sources such as a camera,         a file, or over a network such as Real-Time Streaming Protocol (RTSP).
- **[Daisy chain detection and classification](https://docs.qualcomm.com/doc/80-70014-50/topic/daisy-chain-detection-and-classification.html)**  

The **gst-ai-daisychain-detection-classification** application enables you to         perform cascaded object detection and classification with a camera and a file source. The         use case involves detecting objects and classifying the detected objects.
- **[Mono depth from video](https://docs.qualcomm.com/doc/80-70014-50/topic/mono-depth-from-video.html)**  

The **gst-ai-monodepth** application enables you to infer depth from a live camera         stream.

**Parent Topic:** [Sample applications](https://docs.qualcomm.com/doc/80-70014-50/topic/example-applications.html)

**Related Resources**  

- [Qualcomm GST plugins](https://docs.qualcomm.com/doc/80-70014-50/topic/qim-sdk-plugins.html)

Last Published: Oct 27, 2025

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