# Machine learning use cases

Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/machine-learning-use-cases.html](https://docs.qualcomm.com/doc/80-70015-50/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, complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70015-50/topic/gstreamer-application-use-cases.html).

The AI sample applications need model and label files in the device to run the
            application. The prerequisites to run the AI sample applications are as follows:

To push the files to the device, run the following commands on the Linux host:

1. Download the model and label
                files:

        wget https://github.com/quic/sample-apps-for-qualcomm-linux/releases/download/GA1.2-rel/GA1.2-rel.zipCopy to clipboard
2. Extract the
                files:

        unzip GA1.2-rel.zipCopy to clipboard
3. Push the model and label files to the device:
    - **QCS6490**

            scp -r GA1.2-rel/QCS6490/* root@<IP address of target device>:/opt/Copy to clipboard
    - **QCS9075**

            scp -r GA1.2-rel/QCS9075/* root@<IP address of target device>:/opt/Copy to clipboard

The use cases described use a MobileNet TFLite 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

- **[TensorFlow Lite use cases](https://docs.qualcomm.com/doc/80-70015-50/topic/tensorflow-lite-use-cases.html)**  

TensorFlow Lite is a set of tools that enables on-device machine learning by helping         developers run their models on mobile, embedded, and edge devices. TensorFlow lite 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/80-70015-50/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/80-70015-50/topic/gstreamer-application-use-cases.html)

Last Published: Oct 27, 2025

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