Agent skill · research science · nvidia
tao-train-sparse4d
Prepare calibrated multi-camera data and train, evaluate, run inference, export, or quantize TAO Sparse4D 3D detection and tracking models. Includes 2D-to-3D geometric distillation with released LTT weights. Use for Sparse4D adaptation and temporal multi-camera perception workflows.
Why this skill is useful
Provides domain-specific knowledge and commands for training and evaluating Sparse4D models that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 7k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Sparse4D Standalone install? If the TAO skill bank plugin did not initialize the session, run tao-setup for host preflight, credentials, and cross-skill discovery. Sparse4D predicts 3D boxes and tracking IDs from synchronized, calibrated multi-camera images. Start with the selected model's experiment specification and checkpoint. The canonical TAO documentation covers the model workflow. Instructions 1. Identify the requested action, data supervision, selected checkpoint, and platform. Read the action contract and the selected platform skill. Preserve the user's platform, model, data, and training-budget choices. 2. Match the backbone, ordered classes, image preprocessing, anchors, and temporal dimensions to the selected checkpoint. Follow Backbone and checkpoint selection below before preparing the specification. 3. Use existing compatible info PKLs when supplied. For raw data, follow local conversion and artifact checks. For calibrated real scenes with 2D targets, read geometric distillation; that route does not require 3D annotations or conversion-generated anchors. 4. Start from references/spectemplate<action>.yaml and the selected release's experiment specification. Resolve paths against actual staged artifacts. Bind resultsdir and any action-specific resultsdir to the current job output directory; released specifications can contain old output paths. Write nested YAML, not flat dotted keys; do not fabricate train/val/test files or scene suffixes. Check the installed runtime exposes the fields used by the requested workflow. 5. Resolve the model-level image for model actions and the action-level Data Services image for datasetconvert. For a requested TAO release, use the matching skill-bank release branch and its image pins; do not substitute the default branch's release. Follow tao-launch-workflow and the chosen platform's submit / status / logs / cancel contract, retaining its job record and explicit output directory. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-train-sparse4d