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tao-train-sparse4d

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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.

skills/tao-train-sparse4d/SKILL.md

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# 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](https://docs.nvidia.com/tao/tao-toolkit/latest/text/cv_finetuning/pytorch/sparse4d/sparse4d.html) covers the model workflow.

## Instructions

1. Identify the requested action, data supervision, selected checkpoint, and platform. Read [the action contract](references/skill_info.yaml) 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](references/local_docker_conversion.md). For calibrated real scenes with 2D targets, read [geometric distillation](references/geometric_distillation.md); that route does not require 3D annotations or conversion-generated anchors.
4. 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.
5. 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.
6. 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.
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