Add dataset card

README.md

@@ -1,4 +1,18 @@
 ---
+license: mit
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- spatial-reasoning
+- 3d
+- counting
+- video
+- benchmark
+pretty_name: Spatial Scene Bench
+size_categories:
+- 1K<n<10K
 dataset_info:
 - config_name: full
   features:
@@ -62,3 +76,52 @@ configs:
   - split: test
     path: mini/test-*
 ---
+
+# Spatial Scene Bench
+
+[Code](https://github.com/selfishout/spatial-scene-bench) | [lmms-eval task](https://github.com/EvolvingLMMs-Lab/lmms-eval)
+
+Counting and relational reasoning over procedurally rendered 3D scenes, from single images to 16-frame orbit
+videos. Every answer is computed from the 3D scene itself, and ambiguous questions (heavily occluded objects,
+near-ties in depth or distance) are filtered out at generation time.
+
+## Configs
+
+| Config | Questions | Description |
+| --- | --- | --- |
+| `full` | 4,387 | 200 scenes, all 11 question types |
+| `mini` | 275 | first 25 questions of each type |
+
+```python
+from datasets import load_dataset
+ds = load_dataset("alithema/spatial-scene-bench", "mini", split="test")
+```
+
+## Fields
+
+- `question_type`: one of `count`, `count_relation`, `relation`, `compare_count`, `closest`, `distance`,
+  `ego_direction`, `video_count`, `video_closest`, `video_distance`, `video_ego_direction`
+- `modality`: `image` (one 320×240 view) or `video` (16 orbit frames)
+- `answer_type`: `count` (integer), `yesno`, `choice` (A–D, options in `options`), or `numeric` (meters)
+- `images`: the view or the video frames, in order
+
+## Scoring
+
+Exact match for counts, yes/no and multiple choice; VSI-Bench Mean Relative Accuracy (MRA) for distances. The
+headline score is the macro average over question types. Reference implementations: `spatial_scene_bench.evaluate`
+in the code repository and the `spatial_scene_bench` task in lmms-eval.
+
+| Model | Overall (mini) |
+| --- | --- |
+| Random baseline | 25.8 |
+| Qwen2.5-VL-3B-Instruct (zero-shot) | 50.3 |
+
+## Regenerating
+
+The dataset is fully reproducible from the generator (seed 2025):
+
+```bash
+pip install "spatial-scene-bench[export] @ git+https://github.com/selfishout/spatial-scene-bench.git"
+ssb-generate --out data/ssb --num-scenes 200 --video-frames 16 --seed 2025
+python scripts/export_hf.py --data data/ssb --out data/ssb_hf
+```