Using Sample Apps#

DL Streamer ships with 40+ ready-to-run samples that turn common media-analytics tasks into working pipelines you can launch in minutes. They are the fastest way to see an element or a full use case in action, and a great starting point to copy from when building your own application.

Each sample lives in its own folder with a README.md and a run script. Browse them online in the samples directory on GitHub or, after installation, under /opt/intel/dlstreamer/samples.

Looking for a specific element? See the Elements page — most elements are demonstrated by one or more of the samples listed in the Available Sample Apps.

How to run#

  • Install DL Streamer first — see the Get Started guide.

  • Download the models the samples use with the conversion scripts under scripts/download_models. They export models from Hugging Face, Ultralytics, TIMM and other sources to OpenVINO IR:

    • download_hf_models.py — Hugging Face models (VLMs, CLIP, Whisper, …).

    • download_ultralytics_models.py — Ultralytics YOLO models.

    • download_timm_models.py — TIMM image-classification models.

    • download_other_models.sh — other helper models (e.g. centerface, hsemotion, deeplabv3, mars-small128).

    See the download_models README for prerequisites, per-script requirements files and usage examples. Each sample’s own README.md lists the exact model(s) it needs (see the Models column in the Available Sample Apps).

  • Samples with C/C++ code provide a build_and_run.sh; other samples provide a .sh script that builds and runs a gst-launch-1.0 or Python command line.

Platform support: Samples target Linux (Ubuntu 22.04/24.04) by default. A subset also ships a Windows variant (PowerShell/.bat scripts, D3D11/ksvideosrc backends). See the Windows samples folder.

Next step#

Browse the Available Sample Apps to find a sample by use case, key elements, or models.