tao-analyze-gaps-vlm-bcq

name	tao-analyze-gaps-vlm-bcq
description	Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure cases from a predictions JSON for DEFT root-cause analysis on a binary-classification VLM workflow.
license	Apache-2.0
compatibility	Requires docker + nvidia-container-toolkit.
metadata	{"author":"NVIDIA Corporation","version":"0.1.0"}
allowed-tools	Read Bash
tags	["gap-analysis","rcca","vlm","evaluation","false-positive","false-negative"]

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