Agent skill · research science · nvidia
tao-train-foundation-stereo
Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D
Why this skill is useful
Provides specific commands and workflows for training and evaluating stereo depth estimation models using FoundationStereo that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Depth Net Stereo Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery). Stereo depth estimation using FoundationStereo architecture. Predicts disparity maps from stereo image pairs for 3D reconstruction. Uses pretrained Depth Anything v2 and EdgeNeXt encoders. Set model.stereobackbone.depthanythingv2pretrainedpath and model.stereobackbone.edgenextpretrainedpath. The mono and stereo skills both invoke the unified TAO depthnet CLI inside the container; the mono/stereo family is selected via model.modeltype (e.g., FoundationStereo). PyT actions packaged by this model skill: train, evaluate, inference, export, and quantize. The PyT depthnet entrypoint does not accept a gentrtengine action in the current TAO image; build TensorRT engines only through the deploy workflow. For TAO Deploy TensorRT actions (gentrtengine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-foundation-stereo.md first. The deploy spec template lives in this skill's references/spectemplatedeploy.yaml. Train Action Policy This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skillinfo.yaml and resolve the run override from either an explicit automlpolicy value or the user's workflow request. Use automlpolicy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automlpolicy: off for this run only. When automlpolicy: on, automlenabled: true, and both schemas/train.schema.json and references/spectemplatetrain.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skilldir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automlpolicy. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-train-foundation-stereo