@nvidia/tao-analyze-gaps-vlm-bcq

@nvidia/tao-analyze-gaps-vlm-bcq — AI coding skill

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SKILL.md
nametao-analyze-gaps-vlm-bcq
descriptionExtract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions.
licenseApache-2.0
compatibilityRequires docker + nvidia-container-toolkit.
allowed-toolsRead Bash

VLM Binary Classification Gap Analysis

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).

Reads a VLM predictions JSON, compares each model response against ground truth, and writes FP/FN failure cases to a JSONL file with a summary report.

Purpose

After running a VLM on a binary yes/no evaluation task, the predictions need to be compared against ground truth to identify failure cases. This skill produces a structured list of FP (false positive) and FN (false negative) samples that downstream RCCA stages (e.g., cosmos generation, root cause analysis) consume to drive a DEFT iteration.

Usage

Invoke the vlm_bcq action inside the TAO Toolkit data services container with Hydra-style key=value overrides:

gap_analysis vlm_bcq \
  predictions_json=/path/to/results.json \
  results_dir=/path/to/output/gaps

Include videos_dir when video_id values in the predictions are relative paths:

gap_analysis vlm_bcq \
  predictions_json=/path/to/results.json \
  results_dir=/path/to/output/gaps \
  videos_dir=/path/to/videos/root

After the run, surface the FP/FN counts from kpi_gaps_report.txt and point downstream stages at kpi_gaps.jsonl.

Inputs

  • predictions_json: Path to predictions JSON file. Must be a JSON array where each item has video_id, response, and gt fields. response and gt are parsed with word-boundary matching — 'yes' or 'no' anywhere in the string is recognized. Samples where both or neither are present are skipped with a warning.
  • videos_dir (optional): Base directory for resolving relative video_id paths. If omitted, video_id values are used as absolute paths.

Predictions JSON format:

[
  {
    "video_id": "/path/to/video.mp4",
    "response": "Yes, there is a collision.",
    "gt": "B. No",
    "question": "Is there a collision?"
  }
]

Outputs

  • kpi_gaps.jsonl: One JSON object per line for each FP/FN case. Fields: video_id (absolute path), error_type (FP or FN), question, ground_truth, response.
  • kpi_gaps_report.txt: Human-readable table with total FP/FN counts.

If no gaps are found, no files are written and a message is logged.

Key Parameters

Parameter Required Description
predictions_json Yes Path to predictions JSON file
results_dir Yes Output directory; created if it does not exist
videos_dir No Base directory for resolving relative video_id paths

Error Patterns

Error Cause Fix
FileNotFoundError predictions_json does not exist Check the path
ValueError: must be a JSON array Predictions file is not a list Wrap predictions in [...]
ValueError: missing 'gt'/'response'/'video_id' A prediction item is missing a required field Inspect and fix the predictions JSON
Samples silently skipped response or gt contains both or neither 'yes'/'no' Check logs for warnings; inspect those samples

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