Configure Sample Applications

This section provides a flexible workflow for running and customizing advanced multimedia and AI sample applications using the Qualcomm® Intelligent Multimedia Product (QIMP) SDK on Ubuntu. Developers can define input/output sources, runtime targets, and model precision using JSON configuration files—enabling seamless evaluation across CPU, GPU, and DSP. With support for frameworks like TFLite, QNN, and SNPE, and integration with AI Hub, this setup is ideal for building and optimizing edge AI pipelines tailored to specific use cases.

Running advanced multimedia and AI sample applications using the QIMP SDK on Ubuntu enables developers to:

• Prototype and validate AI workloads across heterogeneous compute targets (CPU, GPU, DSP), helping teams choose the most efficient runtime for their use case.
• Customize application behavior using JSON-based configuration, allowing precise control over input/output sources, model types, and runtime parameters.
• Accelerate development and deployment by leveraging pre-integrated models from AI Hub and supported frameworks like TFLite, QNN, and SNPE.
• Benchmark performance and optimize resource usage, which is critical for embedded systems and edge devices where compute and power budgets are constrained.
• Ensure compatibility and reproducibility across Qualcomm platforms by using standardized scripts and directory structures for models, labels, and media assets.

Prerequisites

• Ubuntu OS should be flashed
• Terminal access with appropriate permissions
• Basic familiarity with JSON configuration files and runtime environment variables.
• Access to AI Hub for model selection and export. Create AI Hub account
• If you haven’t previously installed the PPA packages, please run the following steps to install them.

  git clone -b ubuntu_setup --single-branch https://github.com/rubikpi-ai/rubikpi-script.git 
  cd rubikpi-script  
  ./install_ppa_pkgs.sh 

Use the steps below to configure the script and run the model.

1️⃣ Download and Run the Script This script will automatically fetch all required packages for running the sample applications, including:

• Models
• Labels
• Media files

cd /home/ubuntu 
curl -L -O https://raw.githubusercontent.com/quic/sample-apps-for-qualcomm-linux/refs/heads/main/download_artifacts.sh
sudo chmod +x download_artifacts.sh 
sudo ./download_artifacts.sh -v GA1.5-rel -c QCS6490

---

Explanation

• Use the -v flag to define the version you want to work with (e.g., GA1.5-rel).
• Use the -c flag to define the chipset your device is using.(e.g., QCS6490).

2️⃣ Verify Model/Label/Media Files
Before launching any sample applications, make sure the required files are in place.

Check the following directories:

• Model files → /etc/models/
• Label files → /etc/labels/
• Media files → /etc/media/

note

These files are essential for AI sample applications to function correctly. If they’re missing, re-run the artifact download script.

3️⃣ Updating JSON Config File
To run sample applications with a specific functionality, you’ll need a properly configured JSON file.

What to Do

• Update the required JSON config file based on your model and config requirements.
• Edit the file at e.g. - /etc/configs/config_classification.json to match your use case:

Configuration Parameters

Details

Update your JSON config file with the following key parameters:

• Input Source
  • Camera
  • File (Filesrc)
  • RTSP Stream
• Output Source
  • Waylandsink
  • Filesink
  • RTSP Stream
• Runtime Options
  • CPU
  • GPU
  • DSP
• Precision
  • INT8 / INT16
  • W8A8 / W8A16
  • FP32
• Model Type
  • Select from available models in AI Hub
• Labels
  • Select the correct labels file

Sample Application Configuration Matrix

Sample App Name	Details	AI Hub Model Type	Runtime	Script to Use
gst-ai-classification	Image classification	MobileNet-v2, ResNet101, GoogLeNet, MobileNet-v3-Large, ResNet18, ResNeXt50, ResNeXt101, SqueezeNet, WideResNet50, Shufflenet	CPU, GPU, DSP	Update JSON
gst-ai-object-detection	Object detection	Yolox, Yolov7, Yolov8-Detection (manual export)	CPU, GPU, DSP	Export model from AI Hub; Update script for Yolox/Yolov7 – Update JSON
gst-ai-pose-detection	Pose detection	hrnet_pose	CPU, GPU, DSP	TFLite works by default; update script for precision/runtime – Update JSON
gst-ai-segmentation	Image segmentation	FFNet-40S, FFNet-54S, FFNet-78S	CPU, GPU, DSP	Update JSON
gst-ai-superresolution	Video super-resolution	quicksrnetsmall, QuickSRNetMedium, QuickSRNetLarge, XLSR	CPU, GPU, DSP	Update JSON
gst-ai-multistream-batch-inference	Multistream batch inference	YoloV8-Detection (batch 4), DeeplabV3 (batch 4)	CPU, GPU, DSP	Export model from AI Hub; Update script – Update JSON
gst-ai-face-detection	Face detection	face_det_lite	CPU, GPU, DSP	Update JSON
gst-ai-face-recognition	Face recognition	face_det_lite, face_attrib_net, facemap_3dmm	CPU, GPU, DSP	Face registration required; otherwise output is 'unknown face recognized'
gst-ai-metadata-parser-example	Metadata parsing	Yolov8-Detection	CPU, GPU, DSP	Export model from AI Hub
gst-ai-usb-camera-app	AI USB camera	Yolov8-Detection	CPU, GPU, DSP	Export model from AI Hub
gst-ai-parallel-inference	Parallel inferencing	Yolov8-Detection, Deeplab, Hrnet, Inceptionv3	CPU, GPU, DSP	Export model from AI Hub; Update JSON for other models
gst-ai-daisychain-detection-classification	Daisy chain detection and classification	Inceptionv3 + YoloV8	CPU, GPU, DSP	Export model from AI Hub; Update JSON for other models
gst-ai-audio-classification	Audio classification	Inceptionv3 + YoloV8	CPU, GPU, DSP	Export model from AI Hub; Update JSON for other models
gst-ai-smartcodec-example	AI smart codecn	Inceptionv3 + YoloV8	CPU, GPU, DSP	Export model from AI Hub; Update JSON for other models

Use the SSH/SBC terminal to launch your sample application.

note

In case if the terminal is in root, the we need to set the following environment. Otherwise for ubuntu user, it is not required. export XDG_RUNTIME_DIR=/run/user/$(id -u ubuntu)

Example

For the AI Classification sample application, open the /etc/configs/config_classification.json configuration file and update default labels file.
Change:
"labels": "/etc/labels/classification.labels"
to:
"labels": "/etc/labels/imagenet_labels.txt"

Run the AI classification sample application.

gst-ai-classification

To display the available help options, run the following command in the SSH shell:

gst-ai-classification -h

To stop the use case, use CTRL + C

Reference Docs:

To further explore sample applications, see the Qualcomm Intelligent Multimedia SDK (IM SDK) Reference Guide. Qualcomm Intelligent Multimedia SDK (IM SDK) Reference