# Prerequisites Complete these preconditions before running the AI/ML sample applications. 1. Activate SSH on your Qualcomm Linux or Ubuntu host to sign in with SSH and connect to the target device. For instructions see: - [Sign in using SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-70029-254/how_to.html#use-ssh) for Qualcomm Linux - [Sign in using SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-90441-1/Use_Ubuntu_on_RB3_Gen2_3.html#sign-in-to-the-rb3-gen-2-console-using-ssh) for Ubuntu Server Note If SSH is already set up and Wi-Fi is connected, skip this step. 2. Sign in to the target device using SSH: - For Qualcomm Linux: ssh root@ Copy to clipboard - For Ubuntu Server: ssh ubuntu@ 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.sh Copy to clipboard chmod +x download_artifacts.sh Copy to clipboard ./download_artifacts.sh Copy to clipboard Note For the Ubuntu build, use the `sudo` command. 4. The YOLOv8 model isn’t available by default. You can use the following options to either download the models using a script or export them with Qualcomm AI Hub APIs on the Linux host computer. If you are using [Multistream batch inference](https://docs.qualcomm.com/doc/80-70029-50/topic/multistream-batch-inference.html) application, you can generate a batch model. 1. 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.sh Copy to clipboard chmod +x export_model.sh Copy to clipboard Replace *API\_TOKEN* with the selected key: ./export_model.sh --api-token= Copy to clipboard 2. Export the [YOLOv8-Detection-Quantized](https://github.com/quic/ai-hub-models/tree/main/qai_hub_models/models/yolov8_det) model using Qualcomm AI Hub APIs. Run the following command from the python environment that’s created from `export_model.sh`. For example, to export the YOLOv8 LiteRT model: python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=tflite --device="Dragonwing RB3 Gen 2 Vision Kit" Copy to clipboard Activate the miniconda environment that’s created from the `export_model.sh` script: source miniconda/bin/activate Copy to clipboard 3. Generate a batch model. To change the batch size of the model, update *<N>* in the following `export` command: python -m qai_hub_models.models..export --batch-size --device="Dragonwing RB3 Gen 2 Vision Kit" Copy to clipboard For example, to export the YOLOv8 LiteRT model with `--batch-size 4`, run the following command from the python environment that’s created from `export_model.sh`: python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=tflite --device="Dragonwing RB3 Gen 2 Vision Kit" --batch-size 4 Copy to clipboard For example, to export the YOLOv8 QNN model, run the following command from the python environment that’s created from `export_model.sh`: python -m qai_hub_models.models.yolov8_det.export --quantize w8a8 --target-runtime=qnn_context_binary --device="Dragonwing RB3 Gen 2 Vision Kit" --compile-options="--qairt_version 2.40" --profile-options "--qairt_version 2.40" Copy to clipboard - Update the `--qairt_version` value in the command to match the version installed on your device. - To check the QAIRT version, run the following command on the target device: qnn-net-run --version Copy to clipboard - To find the QAIRT SDK versions that AIHUB supports for the export command, run the following command on your host machine: qai-hub list-frameworks Copy to clipboard - Run the following command in the AIHUB environment to view the supported devices and update the device parameter accordingly: qai-hub list-devices Copy to clipboard 5. The `download_artifacts.sh` script downloads a sample `video.mp4`file to the `/etc/media`directory of the target device. If you are using a custom video, then ensure that you push the video to `/etc/media` and update the file path in the `config.JSON` file of the application. 6. Use the HDMI port to connect the display to the target device. For instructions, see [Set up HDMI display](https://docs.qualcomm.com/bundle/publicresource/topics/80-70029-18/samples.html). 7. In the terminal of the target device, run the following command in the SSH shell to activate the display: - For Qualcomm Linux: export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1 Copy to clipboard - For Ubuntu Server: sudo -i Copy to clipboard export XDG_RUNTIME_DIR=/run/user/$(id -u ubuntu)/ && export WAYLAND_DISPLAY=wayland-1 Copy to clipboard ## Troubleshooting - If you face issues with the camera or display, see [Camera troubleshooting](https://docs.qualcomm.com/bundle/publicresource/topics/80-70029-17/troubleshooting.html) and [Display troubleshooting](https://docs.qualcomm.com/bundle/publicresource/topics/80-70029-18/debug.html). - 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 to the target device: scp root@:/etc/models Copy to clipboard For example: scp mobilenet_v2_quantized.tflite root@:/etc/models Copy to clipboard - To run the sample applications from the UART shell, remount the file system with read/write permissions and execute the following commands on the target device: - For Qualcomm Linux: mount -o remount,rw /usr Copy to clipboard - For Ubuntu Server: mount -o remount,rw / Copy to clipboard - For Ubuntu Server, copy the model files to the user `home` folder and then use `sudo` command to copy the files to the `/etc/models` directory: scp ubuntu@:/home/ubuntu ssh ubuntu@ sudo cp /home/ubuntu/ /etc/models Copy to clipboard Last Published: Apr 02, 2026 [Previous Topic Run AI/ML sample applications](https://docs.qualcomm.com/bundle/publicresource/80-70029-50/topics/ai-ml-sample-applications.md) [Next Topic Image classification](https://docs.qualcomm.com/bundle/publicresource/80-70029-50/topics/gst-ai-classification.md)