| # 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`). |
|
|
