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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
json: struct<entities: struct<boxes: list<item: null>, goal: struct<bbox: struct<center_x: int64, center_y (... 538 chars omitted)
  child 0, entities: struct<boxes: list<item: null>, goal: struct<bbox: struct<center_x: int64, center_y: int64, height:  (... 233 chars omitted)
      child 0, boxes: list<item: null>
          child 0, item: null
      child 1, goal: struct<bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int6 (... 42 chars omitted)
          child 0, bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int64>
              child 0, center_x: int64
              child 1, center_y: int64
              child 2, height: int64
              child 3, width: int64
              child 4, x: int64
              child 5, y: int64
          child 1, pixel_pos: struct<x: int64, y: int64>
              child 0, x: int64
              child 1, y: int64
      child 2, player: struct<bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int6 (... 42 chars omitted)
          child 0, bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int64>
              child 0, center_x: int64
              child 1, center_y: int64
              child 2, height: int64
              child 3, width: int64
              child 4, x: int64
              child 5, y: int64
          child 1, pixel_pos: struct<x: int64, y: int64>
              child 0, x: int64
              child 1, y: int64
  child 1, game_type: string
  child 2, metadata: struct<road_width: int64, segments: list<item: list<item: list<item: double>>>, solution_path: list< (... 64 chars omitted)
      child 0, road_width: int64
      child 1, segments: list<item: list<item: list<item: double>>>
          child 0, item: list<item: list<item: double>>
              child 0, item: list<item: double>
                  child 0, item: double
      child 2, solution_path: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
      child 3, solution_segments: list<item: int64>
          child 0, item: int64
  child 3, render: struct<cell_size: int64, image_height: int64, image_width: int64>
      child 0, cell_size: int64
      child 1, image_height: int64
      child 2, image_width: int64
  child 4, version: string
__key__: string
__url__: string
png: null
to
{'png': Image(mode=None, decode=True), '__key__': Value('string'), '__url__': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1984, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              json: struct<entities: struct<boxes: list<item: null>, goal: struct<bbox: struct<center_x: int64, center_y (... 538 chars omitted)
                child 0, entities: struct<boxes: list<item: null>, goal: struct<bbox: struct<center_x: int64, center_y: int64, height:  (... 233 chars omitted)
                    child 0, boxes: list<item: null>
                        child 0, item: null
                    child 1, goal: struct<bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int6 (... 42 chars omitted)
                        child 0, bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int64>
                            child 0, center_x: int64
                            child 1, center_y: int64
                            child 2, height: int64
                            child 3, width: int64
                            child 4, x: int64
                            child 5, y: int64
                        child 1, pixel_pos: struct<x: int64, y: int64>
                            child 0, x: int64
                            child 1, y: int64
                    child 2, player: struct<bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int6 (... 42 chars omitted)
                        child 0, bbox: struct<center_x: int64, center_y: int64, height: int64, width: int64, x: int64, y: int64>
                            child 0, center_x: int64
                            child 1, center_y: int64
                            child 2, height: int64
                            child 3, width: int64
                            child 4, x: int64
                            child 5, y: int64
                        child 1, pixel_pos: struct<x: int64, y: int64>
                            child 0, x: int64
                            child 1, y: int64
                child 1, game_type: string
                child 2, metadata: struct<road_width: int64, segments: list<item: list<item: list<item: double>>>, solution_path: list< (... 64 chars omitted)
                    child 0, road_width: int64
                    child 1, segments: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 2, solution_path: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
                    child 3, solution_segments: list<item: int64>
                        child 0, item: int64
                child 3, render: struct<cell_size: int64, image_height: int64, image_width: int64>
                    child 0, cell_size: int64
                    child 1, image_height: int64
                    child 2, image_width: int64
                child 4, version: string
              __key__: string
              __url__: string
              png: null
              to
              {'png': Image(mode=None, decode=True), '__key__': Value('string'), '__url__': Value('string')}
              because column names don't match

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VR-Bench Dataset

VR-Bench is a benchmark dataset for evaluating spatial reasoning capabilities of Vision-Language Models (VLMs) and Video Generation Models.

Dataset Structure

The dataset is split into two subsets:

dataset_VR_split/
├── train/          # Training set (96 cases)
│   ├── maze/
│   ├── maze3d/
│   ├── pathfinder/
│   ├── sokoban/
│   └── trapfield/
└── eval/           # Evaluation set (24 cases)
    ├── maze/
    ├── maze3d/
    ├── pathfinder/
    ├── sokoban/
    └── trapfield/

Each game directory contains:

  • images/: Initial state images (PNG)
  • states/: Game state metadata (JSON)
  • videos/: Solution trajectory videos (MP4)

Games

  • Maze: 2D grid-based navigation with walls
  • TrapField: 2D grid-based navigation with traps
  • Sokoban: Box-pushing puzzle game
  • PathFinder: Irregular maze with curved paths
  • Maze3D: 3D maze with vertical navigation

Usage

For VLM Evaluation

from datasets import load_dataset

dataset = load_dataset("your-username/VR-Bench")
train_data = dataset["train"]
eval_data = dataset["eval"]

For Video Model Evaluation

Each video file shows the optimal solution trajectory for the corresponding game state.

Citation

If you use this dataset, please cite:

@article{vr-bench,
  title={VR-Bench: A Benchmark for Spatial Reasoning in Vision Models},
  author={Your Name},
  year={2024}
}

License

MIT License

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