tao-analyze-gaps-vlm-bcq

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 \

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:

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

Error Patterns