{"slug":"deepstream-import-vision-model","title":"deepstream-import-vision-model","summary":"Use this skill to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-26T16:30:36.402832Z","repo":{"url":"https://github.com/NVIDIA/skills","stars":3445,"forks":416,"license":"Apache-2.0","updatedAt":"2026-09-25T03:14:56Z"},"bodyHtml":"

DeepStream Import Vision Model

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Automated end-to-end pipeline: HuggingFace model → TensorRT engine → DeepStream\nmulti-stream benchmark → PDF report.

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Overview

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This self-contained skill uses four phase-specific reference documents. Together they automate the full model bringup workflow for NVIDIA DeepStream, from\ndownloading a model on HuggingFace or NVIDIA NGC to a publication-ready benchmark report.

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Supported input formats: ONNX (direct), SafeTensors (auto-exported via torch.onnx.export).

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Current scope: object detection models only. Classification, segmentation, pose estimation, and other vision tasks are not yet supported — the pipeline fails fast if a non-detection architecture is detected in config.json.

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Prerequisites

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Host: only Docker + the NVIDIA driver. Everything else runs inside the DeepStream container —\nDeepStream, TensorRT/trtexec, the Python export venv (torch/onnx/onnxruntime), wkhtmltopdf,\ndeepstream-app/gst-launch-1.0 — and is bootstrapped by setup.sh. Nothing is installed on the host.\nRuns identically on Linux and Windows (Docker Desktop + WSL2 backend, required for GPU).

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See references/windows.md for the cross-platform run model and the\nper-shell docker run mount token.

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Installation

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bash <path-to-deepstream-import-vision-model>/install.sh --target <your-project-path>\n
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Preview what will be installed first with --dry-run:

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bash <path-to-deepstream-import-vision-model>/install.sh --target <your-project-path> --dry-run\n
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Where <path-to-deepstream-import-vision-model> is the location of this skill in your repo, e.g.:

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The script copies the complete skill into the target project for Claude Code, Codex, and Cursor. Re-running it safely refreshes an existing installation.

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Usage

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Claude Code:

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Use deepstream-import-vision-model to run this model: https://huggingface.co/onnx-community/yolov8n\n
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Codex:

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Use $deepstream-import-vision-model to deploy and benchmark https://huggingface.co/onnx-community/yolov8n\n
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Cursor:

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@deepstream-import-vision-model run this model: https://huggingface.co/onnx-community/yolov8n\n
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The skill runs the full pipeline autonomously — no manual steps required.

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Pipeline Steps

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
StepPhase referenceAction
1–3references/model-acquire.mdBrowse HF repo, download ONNX or export SafeTensors
4–5references/engine-build.mdBuild dynamic TRT engine, run trtexec benchmarks
6–7references/pipeline-run.mdCustom bbox parser, DeepStream single + multi-stream
8references/report-generation.md5 charts, HTML report, PDF
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Output Structure

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Per-model outputs are written to models/<model_name>/ in your project:

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models/<model_name>/\n  model/          ONNX file(s)\n  parser/         Custom nvinfer bbox parser (.cpp, .so)\n  config/         nvinfer config, DS app config, labels.txt\n  scripts/        Model-specific run helpers\n  benchmarks/     TRT engines, trtexec logs\n  reports/        benchmark_report.md / .html / .pdf + charts/\n  samples/        Output videos, test frames, KITTI detections\n
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Files in this package

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deepstream-import-vision-model/\n├── SKILL.md                    Top-level skill definition\n├── README.md                   This file\n├── references/                 Phase-specific runbooks\n├── scripts/                    Utility scripts by pipeline phase\n└── tests/                      Installer and script regression tests\n
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