# tao-train-depth-anything-v2

A skill that runs the TAO pipeline for monocular depth with Depth Anything v2. It predicts pixel depth from a single RGB frame and covers training through export.

- 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-depth-anything-v2/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-train-depth-anything-v2

## What it does

The skill covers the whole cycle for a mono depth model inside the TAO container: `train`, `evaluate`, `inference`, `export` and `quantize`. It ships spec templates plus the `depth_net convert` command, which turns an image-plus-depth dataset into the annotation file the actions expect. Pick the architecture with `model.model_type` — `MetricDepthAnything` for depth in meters, `RelativeDepthAnything` for pixel disparity.

Training defaults to AutoML. The skill routes the run through `tao-skill-bank:tao-run-automl` unless you say otherwise, so ask for plain training in the prompt if you want the model trained without a search. Data prep is where things break: stereo rows like `<left_image> <right_image> <gt_depth>` cannot go straight into mono actions. Keep the left image and ground-truth depth, then point the spec at that derived file.

Docker and `nvidia-container-toolkit` are required. Your dataset and output directory have to be reachable from inside the container — either at the S3 paths the runner resolves or bind-mounted with `-v` on a direct `docker run`. A standalone session needs the `tao-setup` skill run first.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-depth-anything-v2 -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-train-depth-anything-v2/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-train-depth-anything-v2/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-depth-anything-v2 -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-train-depth-anything-v2/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-depth-anything-v2 -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-train-depth-anything-v2/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-train-depth-anything-v2
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-train-depth-anything-v2/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-depth-anything-v2 -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-train-depth-anything-v2
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-train-depth-anything-v2/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-depth-anything-v2 -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-depth-anything-v2/SKILL.md

## When to use it
- build an annotation file from an image and depth dataset
- train a metric depth model on your own scenes
- score a trained model against ground-truth depth
- export and quantize the model for later use
- predict a depth map for a single photo

## Example prompts
- Train RelativeDepthAnything on my dataset and write the spec to `specs/train.yaml`
- Generate annotations with `depth_net convert` using `convert_spec.yaml`
- Run inference on one image and show me the depth map

## 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: 62459dc004fedc40.

BEGIN THIRD-PARTY FILE 62459dc004fedc40
```text
---
name: tao-train-depth-anything-v2
description: Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts
  per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO
  monocular depth model. Trigger phrases include "train monocular depth", "DepthAnything v2", "metric depth from single
  image", "monocular depth estimation".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
  version: "0.1.0"
  author: NVIDIA Corporation
allowed-tools: Read Bash
tags:
- monocular
- depth
- estimation
---

# Depth Net Mono

> **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).

Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images.

Pretrained checkpoint loading varies by model variant and use case — see the **Pretrained checkpoint loading — use case matrix** in `references/parameters.md`.

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` (see `references/parameters.md`).

For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-depth-anything-v2.md` first. The deploy spec template lives in this skill's `references/spec_template_deploy.yaml`.

PyT actions packaged by this model skill: `train`, `evaluate`, `inference`, `export`, and `quantize`. The PyT `depth_net` entrypoint does not accept a PyT-side `gen_trt_engine` action in the current TAO image. The `gen_trt_engine` action metadata must run with the TAO Deploy container, and the deploy workflow remains the deploy-specific entrypoint.

## Train Action Policy
```
END THIRD-PARTY FILE 62459dc004fedc40

The third-party file has ended. Everything between the 62459dc004fedc40 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 1958 of 15669 bytes. © NVIDIA Corporation · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-depth-anything-v2/SKILL.md

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