# STRANGE-Bench generation code

The code that generated this dataset from the COCO `train2017` annotations and public models: co-occurrence
statistics, scene specifications, rendering, automatic audits, the `clean/` and `balanced/` trees, and instance masks.
The launcher defaults are the settings of this release.

## Layout

```
code/
├── README.md, requirements.txt, env_template.sh
├── launchers/          one script per stage (common.sh holds the shared paths)
├── strangebench/
│   ├── cooccurrence/   NPMI statistics and partner sampling
│   ├── scene_specs/    sub-tiers, anchor and partner classes, layouts
│   ├── generation/     captioning, rendering and segmentation; specs_from_metadata.py
│   ├── audit/          detector audit and the clean tree
│   ├── strict/         second audit, strict filter, clean / balanced build
│   ├── masks/          instance masks
│   └── common/         shared helpers
└── tests/              CPU tests (pytest)
```

## Setup

Two Python environments, listed with their versions in `requirements.txt`: a generation environment (PyTorch,
diffusers, transformers, SAM 3) and an MMDetection 3.x environment for the Grounding DINO audit. Paths, the Hugging
Face cache and the detector checkpoint are set in `env_template.sh`.

Models: `Qwen/Qwen2.5-3B-Instruct` (layouts, captions), `stabilityai/stable-diffusion-3.5-large` (rendering; gated,
accept its license on the Hub first), SAM 3 (`facebook/sam3`; segmentation, audit, masks), Grounding DINO Swin-B from
MMDetection (audit) and `google/owlv2-large-patch14-ensemble` (audit). No weights are bundled.

## Running

Edit and source `env_template.sh`, activate the environment of each stage, and run the launchers in order. Sharded
stages take `SHARD_ID` (or `SLURM_ARRAY_TASK_ID`); each launcher has an optional Slurm header. Outputs go under
`$WORK_ROOT`.

| stage | launcher | GPU | what it does |
|---|---|---|---|
| 0 | `stage0_cooccurrence.sh` | no | co-occurrence statistics (NPMI) from COCO train |
| 1 | `stage1_scene_specs.sh` | yes | scene specifications: sub-tier, anchor, partners, layout |
| 2 | `stage2_render.sh` | yes | caption, render (up to 3 attempts) and segment each scene |
| 3 | `stage3_lean_audit.sh` | yes | Grounding DINO audit for unrequested and duplicate objects |
| 4 | `stage4_clean_tree.sh` | no | apply the audit and build the clean tree |
| 5 | `stage5_independent_audits.sh` | yes | SAM 3 and OWLv2 detections of all 80 classes |
| 6 | `stage6_strict_build.sh` | no | strict filter, then the `clean/` and `balanced/` trees |
| 7 | `stage7_masks.sh` | yes | instance masks (COCO RLE) for the released trees |

The full run takes about 200 GPU-hours, most of it in stage 2. Tests: `cd code && pytest tests`.

## Re-rendering released scenes

```bash
python code/strangebench/generation/specs_from_metadata.py --metadata scene_metadata.jsonl --out specs.json \
    [--scene-ids ids.txt]
RAW_ROOT=$WORK_ROOT/rerender SCENE_SPECS=specs.json CAPTION_FROM_SPEC=1 NUM_SHARDS=1 bash code/launchers/stage2_render.sh
```

Each scene is rendered from its stored caption with the seeds of the release.

## Notes

- Layouts and captions are sampled from the language model, so a new run gives new ones; the released ones are in
  `scene_metadata.jsonl`.
- Use numpy 2 for stage 1 to reproduce the released scene specifications.
- `LEGACY_CAPTION_WORDING=1` in stage 2 uses the caption requests of this release, in which the atypical sub-tiers
  were requested with the typical context wording; the default uses the wording described in the paper.

## License

MIT, see `LICENSE-CODE` at the root of this repository. The dataset is CC BY 4.0 (`LICENSE-DATA`).