# VWG-Bench evaluation toolkit

This toolkit validates VWG-Bench metadata and evaluates generated videos with
the benchmark's VLM-as-Judge protocol.

## Installation

```bash
python -m venv .venv
source .venv/bin/activate
python -m pip install -e .
```

Set judge credentials through the environment:

```bash
export GEMINI_API_KEY="..."
export VWG_JUDGE_MODEL="gemini-2.5-pro"
```

Credentials are never loaded from source-controlled files.

## Expected generated-video layout

By default, evaluation looks for:

```text
videos/
├── 0_seed0.mp4
├── 0_seed1.mp4
├── 0_seed2.mp4
└── ...
```

Use `--filename-template` for another convention. Available placeholders are
`{id}`, `{id06}`, and `{seed}`.

## Validate the dataset

```bash
vwg-bench validate-data \
  --dataset-root /path/to/VWG-Bench
```

This verifies metadata fields, IDs, all 380 images, dimensions, 38 task
groups, image shapes, and SHA-256 hashes.

## Evaluate VWG-Bench

```bash
bash scripts/eval_vwg.sh \
  /path/to/VWG-Bench \
  /path/to/videos \
  outputs/model_name/results.jsonl \
  0,1,2
```

The evaluator samples at most 16 frames, includes the true final frame, and
computes applicable 1–5 metrics:

- video quality;
- progress consistency;
- implicit-rule following;
- progress-goal realization;
- last-frame-goal realization.

Metrics without an applicable annotation are omitted rather than assigned
zero. Results are resumable by `result_id`.

## Other reported benchmarks

Four cleaned entry scripts are provided:

```text
scripts/eval_vwg.sh
scripts/eval_mme_cof.sh
scripts/eval_ruler_bench.sh
scripts/eval_v_reasonbench.sh
```

MME-CoF uses the local five-aspect VLM evaluator. RULER-Bench and
V-ReasonBench are format adapters only and require pinned upstream
repositories. See `external_benchmarks/README.md` before reporting results.

## Release verification

```bash
bash scripts/validate_release.sh /path/to/VWG-Bench
```

## License

The evaluation toolkit is released under CC BY-NC 4.0. See `LICENSE.md`.