Rtvi Cv Customize Model
NVIDIAOfficialCreated by NVIDIA CORPORATION
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive procedure for swapping the primary GPU inference engine (GIE) in the VSS Alerts Blueprint verification (2d_cv) mode. It covers the end-to-end process of preparing a custom ONNX model, configuring the DeepStream nvinfer engine, building a custom bounding box parser, and updating the runtime environment to ensure the new model is correctly deployed and executed within the VSS perception container.
What it does
- Guides the export and staging of custom ONNX detection models.
- Details the configuration of DeepStream nvinfer properties for custom models.
- Explains the creation and integration of custom C++ bounding box parsers.
- Provides instructions for runtime TensorRT engine generation and caching.
- Outlines necessary modifications to Docker compose and startup scripts for deployment.
- Offers troubleshooting steps for common parser and model loading failures.
When to use it
- When you need to replace the default detector in the VSS Alerts Blueprint verification mode.
- When debugging issues with ONNX model staging or missing TensorRT engine builds.
- When resolving DeepStream parser load failures or symbol mismatches.
- When you need to integrate a custom detection model into an existing VSS v3.2.1 deployment.
Advantages
- Enables model flexibility within the VSS perception pipeline.
- Provides a structured approach to handling custom inference configurations.
- Includes specific guidance on avoiding common pitfalls like ghost directory mounts.
- Ensures compatibility with the VSS Alerts Blueprint architecture.
Usage examples
- “Replace the VSS Alerts Blueprint verification detector with a YOLO11 ONNX model and redeploy perception-alerts.”
- The user receives a step-by-step guide to staging the ONNX file, updating the nvinfer config, and modifying the startup script to use the new model.
- “DeepStream says dlsym failed for the bbox parser after loading the custom parser library. What should I check?”
- The agent identifies the need to verify that the parser symbol name matches exactly between the C++ code, the nvinfer config, and the macro definition.
- “I mounted the ONNX file path directly in compose before the host file existed. Is that okay?”
- The agent explains that this creates a ghost directory and advises mounting the parent directory instead.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/rtvi-cv-customize-model/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip rtvi-cv-customize-model.zip -d .agents/skills
Documentation by NVIDIA CORPORATION
CV Detection Model Customization — VSS Alerts Blueprint (2d_cv mode only)
The RT-CV perception container (vss-rt-cv) runs a DeepStream pipeline with a configurable primary GIE (GPU Inference Engine). By default it uses GDINO or RTDETR. This guide covers replacing it with any ONNX-format model, using YOLOv11 COCO 80 as the worked example.
This only applies to --mode verification (2d_cv). Real-time alerts mode (2d_vlm) has no CV detector.
For instance-segmentation or detection-plus-mask models, complete references/segmentation-model-contract.md before writing parser or handoff code.
When to use
Use this skill when the user wants to:
swap the stock
vss-rt-cvdetector for another ONNX model in verification mode,debug a broken ONNX staging path, ghost-directory bind mount, or missing runtime TRT engine build,
fix a DeepStream parser load failure such as
dlsym failedon the bbox parser symbol.
Do not use this skill for:
2d_vlmreal-time alerts mode,scaffolding a brand-new standalone RTVI CV microservice (use
rtvi-cv-scaffold-vss-service).
Instructions
Keep the answer scoped to VSS Alerts Blueprint verification mode (
2d_cv) unless the user explicitly asks to compare modes.Paths beginning with
deploy/docker/are relative to the VSS Blueprint repository, not the DeepStream repository. Clone or reuse a VSS checkout that is v3.2.1 or compatible, then run these commands from that repository root (see VSS Quickstart).If the user asks about ONNX staging or a compose mount, explicitly say that mounting a missing file path is wrong: use the stock parent-directory mount, keep the ONNX under
${VSS_DATA_DIR}/models/yolo, and re-stage the ONNX file after anydev-profile.sh upthat recreates the models directory.If the user asks about parser load failures, line up
parse-bbox-func-name, theextern "C"function symbol, andCHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(...), then rebuild the.sowith both DeepStream and CUDA include paths present.If the model is detection-plus-mask or instance segmentation, route the mask-contract decision first through references/segmentation-model-contract.md before editing parser or handoff logic.
Examples
"Replace the VSS Alerts Blueprint verification detector with a YOLO11 ONNX model and redeploy
perception-alerts.""DeepStream says
dlsym failedfor the bbox parser after loading the custom parser library. What should I check?""I mounted the ONNX file path directly in compose before the host file existed. Is that okay?"
Architecture
NVStreamer (RTSP) → SDR (port 9010) → DeepStream (perception-alerts)
└─ primary-gie (nvinfer)
├─ ONNX → TRT engine (built once, then cached)
├─ custom bbox parser (.so)
└─ label file (.txt)
└─ Kafka → mdx-raw
└─ vss-behavior-analytics
└─ mdx-incidents
└─ vlm-as-verifierVSS source location
This skill is documentation-only and does not ship the VSS deployment sources. Use a VSS v3.2.1 or compatible checkout; run all paths and commands from that repository root. Clone/LFS setup, stock Alerts profile files, and the YOLOv11 customization file tree are in references/vss-source-layout.md.
Adapting to a Different Model
YOLOv11 COCO 80 is the worked example throughout all steps below. The same pattern applies to any ONNX-format detector — substitute at these points:
| Step | What to change |
|---|---|
| Step 1 | Replace the export procedure with whatever your model's training library requires. Confirm the resulting .onnx exists on the host before continuing. |
| Steps 2–3 | Inspect your model's actual output tensor name, shape, layout, and whether NMS is applied in-graph. Do not assume it matches YOLOv11. The deepstream-dev skill (skills/deepstream-dev/) has a generation-by-generation YOLO output format table and references/nvinfer_config.md for full property reference. |
| Step 4 | Update output-blob-names to your tensor name, infer-dims to your input shape, and cluster-mode to match whether NMS is in-graph (4) or not (2). |
| Step 5 | Update the --onnx path, the input tensor name in --minShapes/--optShapes/--maxShapes (not images for non-YOLO models), and the sed commands to reference your nvinfer .txt file. Add a new if [[ $MODEL_NAME_2D == "YOURMODEL" ]] block rather than editing the YOLO block. |
Step 1: Stage Your ONNX Model (Host, One-Time)
Obtain a TensorRT-compatible ONNX model and place it at ${VSS_DATA_DIR}/models/yolo/yolo11s.onnx before continuing.
See references/yolov11-onnx-export.md for the export settings and tensor layout used in this reference. Adapt the parser and nvinfer config in later steps to match your model's actual output.
After every full dev-profile.sh up, restore ownership of the recreated models/yolo directory and re-stage your ONNX file:
sudo mkdir -p "${VSS_DATA_DIR}/models/yolo"
sudo chown -R $(id -u):$(id -g) "${VSS_DATA_DIR}/models/yolo"
# Copy your ONNX model to ${VSS_DATA_DIR}/models/yolo/yolo11s.onnxSteps 2–3: Inspect the Model Output and Build the Parser
Read and follow
references/yolov11-parser.md before creating
${VSS_PROFILE_DIR}/deepstream/custom_parser/nvdsparseyolov11.cpp or editing
the parser build in Dockerfiles/perception.Dockerfile.
Before writing any parser code, confirm these four things for your model:
Output tensor name and shape — drives
output-blob-namesandinfer-dimsin Step 4Pre-NMS or post-NMS — drives
cluster-modein Step 4; seedeepstream-devRule 13 for the generation-by-generation breakdownCoordinate semantics —
cx/cy/w/hcenter-format orx1/y1/x2/y2corner-format; the YOLOv11 example uses center-format (see references/yolov11-parser.md)Parser symbol name — the
extern "C"function name must matchparse-bbox-func-namein Step 4 andCHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(...)exactly
Confirm the model output tensor layout instead of assuming it matches
YOLOv11. Keep the parser symbol identical in the exported C++
function, CHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(...), and the Step 4
parse-bbox-func-name setting.
For instance segmentation or detection-plus-mask models, complete the contract decisions table in references/segmentation-model-contract.md before writing parser or handoff code.
Step 4: nvinfer Config
${VSS_PROFILE_DIR}/deepstream/configs/yolov11.txt — the primary GIE sub-config referenced by the DeepStream run config.
model-engine-file and batch-size are patched at container startup by the ds-start.sh block in Step 5. Copy the values below as-is; Step 5 will overwrite both with the correct engine path and sensor count before DeepStream reads the file.
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373 # 1/255 input normalization
model-engine-file=/opt/storage/yolo11s_fp16.engine
onnx-file=/opt/storage/yolo/yolo11s.onnx
batch-size=1 # patched at startup from NUM_SENSORS
network-mode=2 # 0=FP32, 1=INT8, 2=FP16
network-type=0 # 0=Detector
num-detected-classes=80
interval=0
gie-unique-id=1
output-blob-names=output0 # must match your model's output tensor name
infer-dims=3;640;640 # C;H;W
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloE # must match extern "C" function name in .so
custom-lib-path=/opt/deepstream-yolo/libnvdsparseyolov11.so
labelfile-path=/opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/metropolis_perception_app/mounted-configs/yolo-coco-labels.txt
cluster-mode=2 # 2=NMS — required: Ultralytics ONNX has no NMS in-graph
[class-attrs-all]
pre-cluster-threshold=0.25
topk=300
nms-iou-threshold=0.45Step 5: Runtime TRT Engine Build (ds-start.sh)
TRT engines are GPU-architecture-specific. Build the engine on first startup when it is missing, then reuse it on subsequent restarts. Rebuild an existing engine only when FORCE_REBUILD=true. The stock Alerts compose file bind-mounts ${VSS_DATA_DIR}/models/ at /opt/storage/, so the generated engine persists on the host.
Which ds-start.sh? Stock Alerts runs services/rtvi/rtvi-cv/ds-start.sh (via extends), not ${VSS_PROFILE_DIR}/deepstream/init-scripts/ds-start.sh. This skill edits the profile copy, then remounts it in Step 6 so those edits actually run. Full map of the three paths, remount trade-offs, and DS_CONFIG_FILE pairing: references/ds-start-entrypoint.md.
Edit ${VSS_PROFILE_DIR}/deepstream/init-scripts/ds-start.sh, then override the stock rtvi-cv ds-start.sh bind mount in the Alerts profile compose file as shown in Step 6. Without that override, profile-script edits never execute.
Critical — honor DS_CONFIG_FILE. The profile script sets CONFIG_FILE=${1:-...} and never reads DS_CONFIG_FILE. The Alerts compose command invokes ds-start.sh with no positional args, while Step 6 sets DS_CONFIG_FILE to the absolute mounted-configs run config (not the stock .../configs/... path). Replace the script's existing CONFIG_FILE=... assignment with:
# Prefer $1 when provided; otherwise use DS_CONFIG_FILE from compose (absolute path).
# Without this, YOLO_CONFIG resolves to ./yolov11.txt and the stock detector keeps running.
CONFIG_FILE="${1:-${DS_CONFIG_FILE:-/opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/metropolis_perception_app/mounted-configs/run_config-api-rtdetr-protobuf.txt}}"Then add the YOLO block (before the GDINO/RT-DETR model branches is fine):
if [[ $MODEL_NAME_2D == "YOLO" ]]; then
YOLO_ONNX=/opt/storage/yolo/yolo11s.onnx
YOLO_ENGINE=/opt/storage/yolo11s_fp16.engine
YOLO_ENGINE_TMP="${YOLO_ENGINE}.building"
FORCE_REBUILD=${FORCE_REBUILD:-false}
if [[ ! -f "$YOLO_ONNX" ]]; then
echo "ERROR: ONNX not found at ${YOLO_ONNX}. Stage your model there before starting."
exit 1
fi
if [[ ! -s "$YOLO_ENGINE" || "${FORCE_REBUILD,,}" == "true" ]]; then
echo "Building TensorRT engine: ${YOLO_ENGINE}"
rm -f "$YOLO_ENGINE_TMP"
if /usr/src/tensorrt/bin/trtexec \
--onnx=${YOLO_ONNX} \
--minShapes=images:1x3x640x640 \
--optShapes=images:${NUM_SENSORS}x3x640x640 \
--maxShapes=images:${NUM_SENSORS}x3x640x640 \
--fp16 --saveEngine=${YOLO_ENGINE_TMP}; then
mv "$YOLO_ENGINE_TMP" "$YOLO_ENGINE"
else
rm -f "$YOLO_ENGINE_TMP"
echo "ERROR: TensorRT engine build failed; existing engine was preserved."
exit 1
fi
else
echo "Reusing cached TensorRT engine: ${YOLO_ENGINE}"
fi
# Patch the run config to use yolov11.txt for the primary GIE
# (relative name is intentional: DeepStream resolves it against CONFIG_FILE's dir)
sed -i '/^\[primary-gie\]/,/^\[/{s/config-file=.*/config-file=yolov11.txt/;}' "$CONFIG_FILE"
# Patch engine path and batch size into yolov11.txt (same dir as CONFIG_FILE)
YOLO_CONFIG="$(dirname "${CONFIG_FILE}")/yolov11.txt"
sed -i "s|model-engine-file=.*|model-engine-file=${YOLO_ENGINE}|" "${YOLO_CONFIG}"
sed -i "/^\[property\]/,/^\[/{s/^batch-size=.*/batch-size=${NUM_SENSORS}/;}" "${YOLO_CONFIG}"
fiTo add a different model, add a new if [[ $MODEL_NAME_2D == "YOURMODEL" ]] block. Update:
--onnxpath and--saveEngineoutput path--minShapes/--optShapes/--maxShapesinput tensor name and dimensions for your modelThe
sedcommands to point at your nvinfer.txtfile
Step 6: Compose + Env
In the existing ${VSS_PROFILE_DIR}/compose.yml, update the perception-alerts service:
perception-alerts:
build:
context: $VSS_APPS_DIR/developer-profiles/dev-profile-alerts
dockerfile: Dockerfiles/perception.Dockerfile
volumes:
# Keep the stock parent-directory mount. Do not replace it with a direct
# mount of yolo11s.onnx: Docker creates a ghost directory if the host
# file does not exist.
- $VSS_DATA_DIR/models/:/opt/storage/
- $VSS_APPS_DIR/developer-profiles/dev-profile-alerts/deepstream/configs/:/opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/metropolis_perception_app/mounted-configs/
# Override stock rtvi-cv ds-start.sh (extends bind). Without this,
# edits under deepstream/init-scripts/ never run — see
# references/ds-start-entrypoint.md.
- $VSS_APPS_DIR/developer-profiles/dev-profile-alerts/deepstream/init-scripts/ds-start.sh:/opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/metropolis_perception_app/ds-start.sh:ro
environment:
MODEL_NAME_2D: ${MODEL_NAME_2D}
NUM_SENSORS: ${NUM_SENSORS}
FORCE_REBUILD: ${FORCE_REBUILD:-false}
# Use mounted-configs (where yolov11.txt lives), not stock .../configs/...
DS_CONFIG_FILE: /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/metropolis_perception_app/mounted-configs/run_config-api-rtdetr-protobuf.txtSet in ${VSS_PROFILE_DIR}/.env:
MODEL_NAME_2D="YOLO"
NUM_SENSORS=1
FORCE_REBUILD=falseKeep FORCE_REBUILD=false for normal starts. Compose's --force-recreate option recreates the container but does not rebuild the TensorRT engine unless FORCE_REBUILD=true.
Gotcha — generated.env wins at restart. Step 7's redeploy uses --env-file .../generated.env, which is produced by dev-profile.sh from .env. Editing .env alone does not change FORCE_REBUILD for that compose command. For a one-shot engine rebuild, pass the override on the shell (see Step 7). To persist the value for later dev-profile.sh runs, edit .env and re-run dev-profile.sh so it regenerates generated.env.
Step 7: Deploy
Run from the video-search-and-summarization repository root
(VSS_ROOT / VSS_DEPLOY_DIR / VSS_PROFILE_DIR from VSS source location):
: "${VSS_ROOT:=$PWD}"
: "${VSS_DEPLOY_DIR:=${VSS_ROOT}/deploy/docker}"
: "${VSS_PROFILE_DIR:=${VSS_DEPLOY_DIR}/developer-profiles/dev-profile-alerts}"1. Single-GPU host (only GPU 0): edit ${VSS_PROFILE_DIR}/.env so these
keys match the required final values below. Edit the source profile env file,
not generated.env (dev-profile.sh regenerates that). Stock Alerts reserves
GPU 0 and puts RT-VLM / LLM / VLM on GPU 1; dev-profile.sh CLI flags cannot
clear RESERVED_DEVICE_IDS or set FIXED_SHARED_DEVICE_IDS. Confirm device
IDs exist with nvidia-smi --query-gpu=index --format=csv,noheader,nounits
before deploying.
RESERVED_DEVICE_IDS=''
FIXED_SHARED_DEVICE_IDS='0'
RT_CV_DEVICE_ID='0'
RT_VLM_DEVICE_ID='0'
LLM_DEVICE_ID='0'
VLM_DEVICE_ID='0'2. Deploy the official Alerts verification profile. Select the hardware profile supported by your host, as documented by the VSS Quickstart. Do not present the remaining commands as a copy-paste handoff unless execution is blocked.
"${VSS_DEPLOY_DIR}/scripts/dev-profile.sh" up \
-p alerts \
-m verification \
-H <H100|L40S|RTXPRO4500BW|RTXPRO6000BW|DGX-SPARK|IGX-THOR|AGX-THOR|OTHER>
# 3. dev-profile.sh recreates the model directory. Restore ownership and
# re-stage your ONNX model.
export VSS_DATA_DIR="${VSS_DEPLOY_DIR}/data-dir"
sudo mkdir -p "${VSS_DATA_DIR}/models/yolo"
sudo chown -R $(id -u):$(id -g) "${VSS_DATA_DIR}/models/yolo"
# Copy your ONNX model to ${VSS_DATA_DIR}/models/yolo/yolo11s.onnx
# 4. Rebuild and recreate the customized perception service using the compose
# project generated by dev-profile.sh.
cd "${VSS_DEPLOY_DIR}"
docker compose \
--env-file developer-profiles/dev-profile-alerts/generated.env \
up -d --build --force-recreate perception-alerts
# One-shot TensorRT rebuild (after ONNX / shape / GPU / TRT changes).
# Inline FORCE_REBUILD overrides generated.env for this invocation only:
# FORCE_REBUILD=true docker compose \
# --env-file developer-profiles/dev-profile-alerts/generated.env \
# up -d --build --force-recreate perception-alertsTRT engine build takes approximately 15–30 seconds on Blackwell hardware.
Subsequent restarts reuse the engine persisted under
${VSS_DATA_DIR}/models/.
Common Gotchas
See references/common-gotchas.md for single-GPU reservation failures, wipe/restage after dev-profile.sh up, ghost file mounts, dlsym parser mismatches, and Redis stream contamination.
Files
- .gitkeep0 B
- ATTRIBUTION.mdAdded by AutoGineer973 B
- BENCHMARK.md4.3 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md17.6 KiB
- evals/evals.json5.7 KiB
- references/common-gotchas.md1.9 KiB
- references/ds-start-entrypoint.md3.0 KiB
- references/segmentation-model-contract.md8.5 KiB
- references/vss-source-layout.md3.5 KiB
- references/yolov11-onnx-export.md1.6 KiB
- references/yolov11-parser.md4.4 KiB
- skill-card.md4.6 KiB
- skill.oms.sig6.1 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA CORPORATION
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/rtvi-cv-customize-model · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA CORPORATION or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download rtvi-cv-customize-model.zip