What is tao-train-sparse4d?

A computer-vision tool for detecting and tracking objects in three dimensions using several cameras over time. It keeps information about detected objects so they can be followed between frames.

Train, evaluate, export, optimize, and run multi-camera 3D detection and tracking models.

It helps systems understand where objects are and how they move when using multiple camera views.

tao-train-sparse4d — Skill for Claude Code by NVIDIA-TAO

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add NVIDIA-TAO/tao-skill-bank --skill tao-train-sparse4d

Clone the repo

git clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bank

Made for: Claude Code.

Or install tao-skills, the plugin that ships this one along with the rest of its 82 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d/github.svg)](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d)

Your own site

<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-sparse4d.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,774 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00070	$0.02774
Opus 5.5	$0.00028	$0.01110
Sonnet 5.5	$0.00014	$0.00555
Haiku 4.5	$0.00007	$0.00277

Measured 11d ago against content hash dbf765865711, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tao-train-sparse4d scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/normalize_depth_paths.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/models/tao-train-sparse4d/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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.
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.
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.
Start from references/spec_template_<action>.yaml and the selected release's experiment specification. Resolve paths against actual staged artifacts. Bind results_dir and any action-specific results_dir 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.
Resolve the model-level image for model actions and the action-level Data Services image for dataset_convert. 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. Existing user authorization applies; request missing launch approval only for work not already authorized.
Verify the produced artifacts and model metrics. A successful process or short training run is a smoke result, not evidence of improved 3D accuracy. Do not report unpublished experimental fixes or another run's results as validation of the selected image.

Read the full file on GitHub · 107 lines

Files

What ships with it

23 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

11d ago Changed dbf765865711
19d ago Changed · -140 lines · -40 tokens per session 633a1828d5d8
1mo ago First seen · 247 lines · 110 tokens per session scan A a4740918a3c9

Subscribe to this mod's changes

tao-train-sparse4d is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (97 stars, last pushed 5d ago), licensed Apache-2.0. It adds 70 tokens to every session and 2,774 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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