# tao-generate-image-grounding

A two-pass pipeline where a vision model pulls referring phrases out of each caption and returns pixel bounding boxes for them. Useful when you need phrase-grounding training data.

- 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-generate-image-grounding/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-generate-image-grounding

## What it does

Input is a JSONL file with one `image_path` and `caption` per line. Step 0 has the VLM look at the image, clean the caption and pull out referring expressions with character spans. Step 1 gives every expression a set of pixel boxes and confidence scores. Pick the passes you want in `workflow.steps`: expression extraction alone, or grounding alone on top of an existing `results_dir/step_0_expression_extraction/annotations.jsonl`.

Execution happens inside the TAO Toolkit container through `auto_label generate`, and any config field can be overridden with Hydra dot-notation on the command line. The `gemini` backend is the default and reads `GOOGLE_API_KEY`; anything else means an OpenAI-compatible endpoint — NIM, vLLM, a self-hosted TAO inference service, or your own. Per-sample checkpoints land in `step_<N>_*/.ckpt/<sample_id>.json`, so a rerun picks up where it stopped unless `workflow.force_reprocess: true` is set. Four parallel threads are the default, which is the first knob to lower on a rate-limited API.

No docker, no nvidia-container-toolkit, no endpoint with credentials — and the pipeline has nothing to run on. Sessions started outside the TAO skill bank plugin should call `tao-setup` first. Try 5–10 images before committing: read the `cleaned_caption` and the boxes under `expressions[].instances[]`, because a weak model will happily ground a phrase in the wrong corner of the frame.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-image-grounding -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-image-grounding -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-image-grounding -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-generate-image-grounding
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-generate-image-grounding/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-image-grounding -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-generate-image-grounding
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-image-grounding -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-generate-image-grounding/SKILL.md

## When to use it
- extract noun phrases from captions that can be tied to pixels
- attach bounding boxes and confidence scores to each phrase
- build a dataset for referring-expression and grounding models
- resume a run from stored checkpoints instead of reprocessing everything
- point the labeling job at your own OpenAI-compatible model server

## Example prompts
- Run both passes on `captions.jsonl`: `auto_label generate -e spec.yaml image_grounding.data.input_jsonl=/data/captions.jsonl`
- Ground only, reusing the existing `step_0_expression_extraction/annotations.jsonl`, with `workflow.steps=["1"]`
- Reprocess the whole dataset with `image_grounding.workflow.force_reprocess=true`, ignoring checkpoints

## 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: 7848b1be8e978658.

BEGIN THIRD-PARTY FILE 7848b1be8e978658
````text
---
name: tao-generate-image-grounding
description: "Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them
  to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded
  annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding',
  'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'."
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
allowed-tools: Read Bash Write
tags:
  - image
  - grounding
  - bounding-boxes
  - auto-label
  - vlm
  - 2d-grounding
---

# Image Grounding Pipeline

> **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).

Turn `(image, caption)` pairs into per-image grounded annotations: cleaned captions, referring expressions with character spans, and pixel-space bounding boxes for each expression. A single VLM (Gemini or any OpenAI-compatible endpoint) handles both steps.

## Purpose

Generate phrase-grounded training data for referring-expression and grounding models. The VLM acts as a "teacher" annotator: Step 0 extracts referring expressions from the caption while looking at the image; Step 1 returns one bbox set per expression for each image.

## Pipeline Architecture

```
Step 0: Expression extraction  → VLM cleans caption, extracts referring expressions + char spans
Step 1: Phrase grounding       → VLM returns pixel bboxes + scores per expression
```
````
END THIRD-PARTY FILE 7848b1be8e978658

The third-party file has ended. Everything between the 7848b1be8e978658 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.

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