Tao Analyze Gaps Vlm Bcq

VLM Binary Classification Gap Analysis

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 |