# Machine learning use cases

Source: [https://docs.qualcomm.com/doc/80-70017-50/topic/machine-learning-use-cases.html](https://docs.qualcomm.com/doc/80-70017-50/topic/machine-learning-use-cases.html)

The TensorFlow Lite runtime and Qualcomm Neural Processing SDK runtime are used for
        inference in the machine learning use cases.

Before you run the use cases, do the following:

- Complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70017-50/topic/gstreamer-application-use-cases.html).
- [Download model, label, and config JSON files for Qualcomm Neural Processing SDK](https://docs.qualcomm.com/doc/80-70017-50/topic/ai-ml-sample-applications.html#ai-ml-sample-applications__section_chl_dgz_scc).

Important:The AI procedures in this guide are compatible
            with the following runtimes:
- Qualcomm Neural Processing SDK – v2.29
- Qualcomm AI Engine direct – v2.29
- TensorFlow Runtime (now LiteRT)  – v2.16.1

Ensure that you download the matching SDKs to your host computer before starting
                AI/ML development.

The use cases described use a MobileNet LiteRT model to classify scenes a single camera
            stream and either overlay or compose the classification labels.

1. Create the Python 3.8 virtual
                environment:

        sudo apt-get install python3.8Copy to clipboard

        python3.8 -m venv py3.8Copy to clipboard

        source py3.8/bin/activateCopy to clipboard
2. Generate the yolov5.tflite
                model:

        git clone https://github.com/ultralytics/yolov5.gitCopy to clipboard

        cd yolov5Copy to clipboard

        python -m pip install -r requirements.txt tensorflow-cpuCopy to clipboard

        python export.py --weights yolov5m.pt --img 320 --include tflite --int8 -- data data/coco128.yamlCopy to clipboard

        scp yolov5m-int8.tflite root@<IP address of the device>:/opt/yolov5.tfliteCopy to clipboard

The YOLO-NAS and YOLOv5 models are trained on the same data set. Use the same label file
            for YOLOv5 as well.

    ssh root@<ip-addr of the target device>aCopy to clipboard

    cp /opt/yolonas.labels /opt/yolov5.labelsCopy to clipboard

- **[LiteRT use cases](https://docs.qualcomm.com/doc/80-70017-50/topic/tensorflow-lite-use-cases.html)**  

LiteRT is a set of tools that enables on-device machine learning by helping         developers run their models on mobile, embedded, and edge devices. LiteRT use cases enable         you to run use cases for image classification, object detection, image segmentation, and         pose estimation.
- **[Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/doc/80-70017-50/topic/qualcomm-neural-processing-sdk-use-cases.html)**  

Qualcomm Neural Processing SDK (formerly known as Qualcomm Snapdragon Neural         Processing Engine (SNPE)) is used to run deep neural networks for inference. The use cases         describe the image classification, object detection, and image segmentation scenarios using         different ML models.

**Parent Topic:** [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70017-50/topic/gstreamer-application-use-cases.html)

Last Published: Oct 01, 2026

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