# Prerequisites Source: [https://docs.qualcomm.com/doc/80-70020-50/topic/download-model-and-label-files.html](https://docs.qualcomm.com/doc/80-70020-50/topic/download-model-and-label-files.html) Complete these preconditions before running the AI/ML sample applications. 1. Activate SSH on your Qualcomm Linux or Ubuntu 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/80-70020-254/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.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 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/80-70020-50/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 API_TOKEN with the selected key: ./export_model.sh --api-token=Copy to clipboard - 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: The current release (Qualcomm Linux 1.5) uses Qualcomm AI Runtime SDK v2.35. 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 --chipset="qualcomm-qcs6490-proxy" --compile-options="--qairt_version 2.38" --profile-options "--qairt_version 2.38"Copy to clipboard Note: Update the `qairt_version` in the command based on the QLI QAIRT version. 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 <N> in the following `export` command: python -m qai_hub_models.models..export --batch-size --device "QCS6490 (Proxy)"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 --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 [Get the model constants](https://docs.qualcomm.com/bundle/publicresource/topics/80-70020-15B/integrate-ai-hub-models.html#get-the-model-constants). 6. A sample `video.mp4`file is made available in the `/etc/media `directory of the target device with the download script from step 5. 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. 7. 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/80-70020-18/samples.html). 8. 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-1Copy to clipboard - For Ubuntu Server: sudo -iCopy to clipboard export XDG_RUNTIME_DIR=/run/user/$(id -u ubuntu)/ && export WAYLAND_DISPLAY=wayland-1Copy to clipboard ## Troubleshooting - If you face issues with the camera or display, see [Camera troubleshooting](https://docs.qualcomm.com/bundle/publicresource/topics/80-70020-17/troubleshooting.html) and [Display troubleshooting](https://docs.qualcomm.com/bundle/publicresource/topics/80-70020-18/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/modelsCopy to clipboard For example: scp mobilenet_v2_quantized.tflite root@:/etc/modelsCopy to clipboard Note: To run the sample applications from the UART shell, remount the file system with read/write permissions using the following command on the target device: mount -o remount,rw /usrCopy 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/modelsCopy to clipboard **Parent Topic:** [Run AI/ML sample applications](https://docs.qualcomm.com/doc/80-70020-50/topic/ai-ml-sample-applications.html) Last Published: Jan 30, 2026 [Previous Topic Run AI/ML sample applications](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/ai-ml-sample-applications.md) [Next Topic Image classification](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/gst-ai-classification.md)