# tao-train-foundation-stereo

Train and run FoundationStereo on stereo image pairs to produce disparity maps for 3D reconstruction.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 3735
- Author: NVIDIA Corporation
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-foundation-stereo/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-train-foundation-stereo

## What it does

The skill covers TAO model training, evaluation, inference, export, and quantization. Training and evaluation use left and right images with GT disparity; inference can use image pairs without GT. You can prepare annotation files with `depth_net convert` or point the workflow to an existing file.

Choose `model_type` and `dataset_name` to match the data; supported dataset classes include Middlebury, KITTI, and ETH3D. For inference without GT, use `GenericDataset`, since dataset-specific classes require disparity annotations. The workflow runs in a container, so Docker and `nvidia-container-toolkit` are prerequisites. Dataset files and output directories must be reachable from inside that container.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-foundation-stereo -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-train-foundation-stereo/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-train-foundation-stereo/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-foundation-stereo -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-train-foundation-stereo/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-foundation-stereo -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-train-foundation-stereo/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-train-foundation-stereo
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-train-foundation-stereo/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-foundation-stereo -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-train-foundation-stereo
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-train-foundation-stereo/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-foundation-stereo -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-foundation-stereo/SKILL.md

## When to use it
- train FoundationStereo on stereo pairs with ground-truth disparity
- evaluate a model against GT disparity
- generate disparity maps from left and right images
- create annotation files with `depth_net convert`
- export or quantize a TAO model

## Example prompts
- Train FoundationStereo on these stereo pairs with GT disparity.
- Evaluate the model on Middlebury data.
- Create annotations for my stereo dataset with `depth_net convert`.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: e282e4c43d67bf54.

BEGIN THIRD-PARTY FILE e282e4c43d67bf54
```text
---
name: tao-train-foundation-stereo
description: Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D
  reconstruction. Use when training, evaluating, exporting, or running inference for a TAO FoundationStereo model. Trigger
  phrases include "train stereo depth", "FoundationStereo", "stereo disparity estimation", "3D reconstruction from stereo".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
  version: "0.1.0"
  author: NVIDIA Corporation
allowed-tools: Read Bash
tags:
- stereo
- depth
- estimation
---

# 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.stereo_backbone.depth_anything_v2_pretrained_path` and `model.stereo_backbone.edgenext_pretrained_path`.

The mono and stereo skills both invoke the unified TAO `depth_net` CLI inside the container; the mono/stereo family is selected via `model.model_type` (e.g., `FoundationStereo`).

PyT actions packaged by this model skill: `train`, `evaluate`, `inference`, `export`, and `quantize`. The PyT `depth_net` entrypoint does not accept a `gen_trt_engine` action in the current TAO image; build TensorRT engines only through the deploy workflow.

For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-foundation-stereo.md` first. The deploy spec template lives in this skill's `references/spec_template_deploy.yaml`.

## Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references/skill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: 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 `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas/train.schema.json` and `references/spec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
```
END THIRD-PARTY FILE e282e4c43d67bf54

The third-party file has ended. Everything between the e282e4c43d67bf54 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2883 of 12988 bytes. © NVIDIA Corporation · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-foundation-stereo/SKILL.md

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