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/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 Do not report unpublished experimental fixes or another run's results as validation of the selected image.

Backbone and checkpoint selection