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Tao Generate Image Grounding

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk
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From the author

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'.
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The tao-generate-image-grounding skill provides a structured workflow for generating phrase-grounded training data. It utilizes a vision-language model (VLM) to perform a two-step process: first, it cleans captions and extracts referring expressions with character spans; second, it predicts pixel-space bounding boxes for those expressions. The pipeline supports various VLM backends, including Gemini and OpenAI-compatible endpoints like NIM or vLLM, and includes built-in checkpointing to allow for efficient, resumable processing of large image datasets.

What it does

  • Extracts referring expressions and character spans from image captions.
  • Generates pixel-space bounding boxes for identified expressions.
  • Supports multiple VLM backends including Gemini and OpenAI-compatible services.
  • Provides per-sample checkpointing for resumable, fault-tolerant processing.
  • Allows for flexible pipeline execution, enabling step-by-step or full-run processing.
  • Outputs structured JSONL annotations suitable for training grounding models.

When to use it

  • When you need to create phrase-grounded training datasets from existing image-caption pairs.
  • When you want to auto-label images with bounding boxes for object detection or grounding tasks.
  • When you need to validate or refine caption-to-image alignment using a VLM.
  • When you have a large dataset and require a resumable, automated annotation workflow.

Advantages

  • Automates labor-intensive manual annotation tasks using powerful VLM teacher models.
  • Highly configurable with support for various self-hosted or cloud-based VLM endpoints.
  • Resilient workflow design with built-in checkpointing to prevent data loss during long runs.
  • Flexible execution allows for inspecting intermediate results before full-scale processing.

Usage examples

“Run the image grounding pipeline on my dataset of images and captions.”
The agent will guide you through setting up the input JSONL, selecting your VLM backend (e.g., Gemini or a local vLLM server), and executing the two-step extraction and grounding process.
“I need to extract referring expressions from my captions but not ground them yet.”
The agent will configure the workflow to run only step 0, producing a JSONL file containing cleaned captions and identified expressions with character spans.
“How do I re-run the grounding step after fixing my configuration?”
The agent will instruct you to set workflow.force_reprocess to true to ignore existing checkpoints and re-run the pipeline from scratch.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/tao-generate-image-grounding/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-generate-image-grounding.zip -d .agents/skills

Documentation by NVIDIA Corporation

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

Steps are individually selectable via workflow.steps. Each step writes a per-sample checkpoint to step_<N>_*/.ckpt/<sample_id>.json and skips already-processed records on re-run. Set workflow.force_reprocess: true to ignore checkpoints and reprocess from scratch.

Instructions

Initial setup

When a user wants to run this pipeline, walk through these steps:

  1. Input JSONL: Ask for the JSONL path. Each line must be one object like {"image_path": "...", "caption": "..."}. image_path can be absolute or relative.

  2. Image root: If any image_path values are relative, set data.image_root to the directory they should resolve from.

  3. API access: Ask the user which VLM endpoint they want to use. Present these five options and act on the choice:

    1. Gemini — set vlm.backend: "gemini"; require GOOGLE_API_KEY (env var or vlm.gemini.api_key).

    2. NIM (e.g. https://inference-api.nvidia.com/v1) — set vlm.backend: "openai"; collect base_url, model_name, and api_key.

    3. TAO inference microservice (self-hosted, OpenAI-compatible). Confirm whether the server is already running:

      • Running — collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".

      • Not running — guide the user through the skills/applications/tao-run-inference-service skill, which stands up a local TAO inference microservice with an OpenAI-compatible API. Before promising a specific model, check skills/applications/tao-run-inference-service/references/service.yaml for valid_network_arch_config_basenames. Once the server is up, collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".

    4. vLLM (self-hosted, OpenAI-compatible). Confirm whether the server is already running:

      • Running — collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".

      • Not running — follow references/vllm_server.md to install and launch a vLLM server, then collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".

    5. Custom (any other OpenAI-compatible endpoint) — set vlm.backend: "openai"; collect base_url, model_name, and (optionally) api_key.

    If the user has no endpoint and does not want to set one up, stop and help resolve API access first.

  4. Workflow steps: Choose one of:

    • Full pipeline: ["0", "1"]

    • Expression extraction only: ["0"]

    • Grounding only: ["1"], which requires existing step-0 output at results_dir/step_0_expression_extraction/annotations.jsonl

  5. Resume vs fresh run: By default, the workflow reuses checkpoints and skips completed records. To reprocess everything, set image_grounding.workflow.force_reprocess=true.

Running the pipeline

The pipeline runs inside the TAO Toolkit container via the auto_label CLI:

auto_label generate -e /path/to/spec.yaml \
    results_dir=/results \
    image_grounding.data.input_jsonl=/data/captions.jsonl \
    image_grounding.data.image_root=/data/images \
    image_grounding.vlm.gemini.api_key=$GOOGLE_API_KEY

Generate a default spec: auto_label default_specs results_dir=/results module_name=auto_label, then set autolabel_type: "image_grounding". All fields support Hydra dot-notation overrides on the command line.

See references/configuration.md for the full YAML structure, all parameters, model/endpoint setup, and error patterns.

Recommended pilot workflow

  1. Run on 5-10 images with both steps

  2. Inspect step_0_expression_extraction/annotations.jsonl — are cleaned_caption and expressions[] accurate? Are the right noun phrases captured?

  3. Inspect step_1_grounding/annotations.jsonl — do the bboxes in expressions[].instances[] look right? Are confidence scores reasonable?

  4. If quality is insufficient, switch the VLM to a stronger model (e.g. gemini-2.5-pro) or raise media_resolution/max_output_tokens, then re-run with force_reprocess=true.

  5. Scale to the full dataset once satisfied.

Configuration

Key configuration fields (full reference in references/configuration.md):

FieldDefaultDescription
workflow.steps["0","1"]Which pipeline steps to execute ("0" = expressions, "1" = grounding)
workflow.max_workers4Parallel threads per step (watch API rate limits)
workflow.force_reprocessfalseIgnore per-sample checkpoints and reprocess from scratch
vlm.backend"gemini""gemini" or "openai" (OpenAI-compatible endpoint)
data.input_jsonlrequiredPath to input JSONL with image_path + caption per line
data.image_root""Optional prefix for resolving relative image_path entries

Inputs

A single JSONL file at data.input_jsonl. One JSON object per line:

FieldRequiredDescription
image_pathyesAbsolute path, or relative path resolved against data.image_root
captionyesFree-text caption for the image
image_idnoStable identifier; auto-derived from the filename if missing
width, heightnoImage dimensions in pixels; default to 1920×1080 for bbox clamping if missing

Outputs

All outputs go to results_dir/:

  • step_0_expression_extraction/annotations.jsonl — per-record output enriched with cleaned_caption and expressions[] (each with text, expression_id, char_span, noun_chunk, empty instances[]).

  • step_1_grounding/annotations.jsonl — same records with expressions[].instances[] filled in (each instance has bbox: [x1,y1,x2,y2] in pixel space, score in [0.0, 1.0], and bbox_id).

  • results_dir/annotations.jsonl — copy of the last step's output for convenience.

  • step_<N>_*/.ckpt/<sample_id>.json — per-sample checkpoints used for resume.

Prerequisites

  • Container: nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt <!-- versions-key: images.tao_toolkit.pyt -->

  • API access: At least one VLM endpoint (Gemini API key or OpenAI-compatible endpoint capable of image input)

Files

skill.oms.sig

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an API key

The author says: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Corporation
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/tao-generate-image-grounding · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

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