# Download model and label files

Source: [https://docs.qualcomm.com/doc/80-70018-50/topic/download-model-and-label-files.html](https://docs.qualcomm.com/doc/80-70018-50/topic/download-model-and-label-files.html)

Download the model and label files for QCS6490, QCS9075, and QCS8275 to run the AI/ML
        sample applications.

Do the following on the Linux host computer:
1. Enable SSH and connect to Wi-Fi. For instructions, see [Sign in using SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-70018-254/how_to.html#use-ssh).
Note: If SSH is already enabled and Wi-Fi is connected,
                            skip this step.
2. Sign in to the target device using
                        SSH:

        ssh root@<ip address of target device>Copy to clipboard
3. On the target device, obtain the `download_artifacts.sh`
                        script, set executable permissions, and run it to download the model, media,
                        and label
                            files:

        cd /tmp/Copy to clipboard

        curl -L -O https://raw.githubusercontent.com/quic/sample-apps-for-qualcomm-linux/refs/heads/main/qualcomm-linux/scripts/download_artifacts.shCopy to clipboard

        chmod +x download_artifacts.shCopy to clipboard

        ./download_artifacts.shCopy to clipboard

Note: For the Ubuntu build, use the
                                `sudo` command.
4. The YOLOv8 and YOLO-NAS models aren't available by default. You can use the
                        following options to either download the models using a script or export
                        them with AI Hub APIs. 
    If you are using [Multistream batch inference](https://docs.qualcomm.com/doc/80-70018-50/topic/multistream-batch-inference.html) application, you can
                            generate a batch model.

    - Download the models using a script:
        1. Create a [Qualcomm AI Hub
                                            account](https://app.aihub.qualcomm.com/account/).
        2. Select the account name, then go to
                                            Settings in the upper right
                                        corner, and select the API token.
        3. Export the models on the Linux host computer and set the
                                        required
                                            permissions:

                curl -L -O https://raw.githubusercontent.com/quic/sample-apps-for-qualcomm-linux/refs/heads/main/qualcomm-linux/scripts/export_model.shCopy to clipboard

                chmod +x export_model.shCopy to clipboard

            Replace
                                                <var class="keyword varname">API_TOKEN</var> with the selected
                                            key:

                ./export_model.sh --api-token=<API_TOKEN>Copy to clipboard
    - Export the models using AI Hub APIs:
        - [YOLOv8-Detection-Quantized](https://github.com/quic/ai-hub-models/tree/main/qai_hub_models/models/yolov8_det_quantized)
        - [Yolo-NAS-Quantized](https://github.com/quic/ai-hub-models/tree/main/qai_hub_models/models/yolonas_quantized)

        The current release (GA1.4) uses Qualcomm AI Runtime SDK
                                    v2.32.

For example, to export the YOLOv8 QNN model, run
                                    the following
                                    command:

            python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=qnn --chipset="qualcomm-qcs6490-proxy" --compile-options="--qairt_version 2.32" --profile-options "--qairt_version 2.32"Copy to clipboard

For
                                    example, to export the YOLOv8 LiteRT model, run the following
                                    command:

            python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=tflite --chipset="qualcomm-qcs6490-proxy"Copy to clipboard
    - Generate a batch model. 
        To change the batch size of the model,
                                    update <var class="keyword varname">&lt;N&gt;</var> in the following
                                        `export`
                                    command:

            python -m qai_hub_models.models.<Model_Name>.export --batch-size <N> --device "QCS6490 (Proxy)"Copy to clipboard

For
                                    example, to export the YOLOv8 LiteRT model with
                                        `--batch-size 4`, run the following
                                    command:

            python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=tflite --chipset="qualcomm-qcs6490-proxy" --batch-size 4Copy to clipboard
5. Update the `q_offset` and `q_scale` constants
                        of the quantized LiteRT model in the JSON file. For instructions, see [Obtain model constants](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-15B/integrate-ai-hub-models.html#obtain-model-constants).

If any model isn't available after downloading the script file, you can
                    download the model from [IoT–Qualcomm AI Hub](https://aihub.qualcomm.com/iot/models/) and push it on the
                    target
                device:

    scp <model filename> root@<IP addr of the target device>:/etc/modelsCopy to clipboard

For
                example:

    scp mobilenet_v2_quantized.tflite root@<IP addr of the target device>:/etc/modelsCopy to clipboard

Note: If you want to run the sample applications from the UART
                shell, remount the file system with read/write permission using the following
                command on the target
                device:

    mount -o remount,rw /usrCopy to clipboard

**Parent Topic:** [Run AI/ML sample applications](https://docs.qualcomm.com/doc/80-70018-50/topic/ai-ml-sample-applications.html)

Last Published: Jan 30, 2026

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