Datasets:

uclanlp
/

STRIDE

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<benchmark: string, split: string, source_dataset: string, n_questions: int64, n_templates: int64, n_scenes: int64, counts_by_template: struct<SC-1: int64, SC-2: int64, SC-3: int64, SC-4: int64, SP-2: int64, SP-3: int64, SP-3a: int64, TE-1: int64, TE-2: int64, TE-4: int64, TE-5: int64, TM-2: int64, TM-3: int64, TM-5: int64, TRJ-1: int64, TRJ-10: int64, TRJ-2: int64, TRJ-3: int64, TRJ-4: int64, TRJ-5: int64, TRJ-6: int64, TRJ-7: int64, TRJ-8: int64, TRJ-9: int64>, counts_by_task_family: struct<scenario-corner-case: int64, space-perception: int64, time-extrapolation: int64, time-memory: int64, trajectory-prediction: int64>, counts_by_format: struct<MCQ: int64, OEQ: int64>, image_paths_are_relative_to: string, notes: list<item: string>, packaged_at: string>
to
{'benchmark': Value('string'), 'split': Value('string'), 'source_dataset': Value('string'), 'n_questions': Value('int64'), 'n_templates': Value('int64'), 'n_scenes': Value('int64'), 'counts_by_template': {'SC-6': Value('int64'), 'SP-1': Value('int64'), 'SP-2': Value('int64'), 'SP-3': Value('int64'), 'SP-5': Value('int64'), 'SP-7': Value('int64'), 'SU-1': Value('int64'), 'SU-2': Value('int64'), 'SU-4': Value('int64'), 'SU-7': Value('int64'), 'TE-1': Value('int64'), 'TE-6': Value('int64'), 'TM-2': Value('int64'), 'TM-3': Value('int64'), 'TM-4': Value('int64'), 'TM-5': Value('int64'), 'TRJ-1': Value('int64'), 'TRJ-2': Value('int64'), 'TRJ-3': Value('int64'), 'TRJ-4': Value('int64'), 'TRJ-5': Value('int64'), 'TRJ-6': Value('int64'), 'TRJ-7': Value('int64')}, 'counts_by_task_family': {'scene-context': Value('int64'), 'space-perception': Value('int64'), 'space-understanding': Value('int64'), 'time-extrapolation': Value('int64'), 'time-memory': Value('int64'), 'trajectory-prediction': Value('int64')}, 'counts_by_format': {'OEQ': Value('int64'), 'MCQ': Value('int64')}, 'notes': List(Value('string')), 'packaged_at': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<benchmark: string, split: string, source_dataset: string, n_questions: int64, n_templates: int64, n_scenes: int64, counts_by_template: struct<SC-1: int64, SC-2: int64, SC-3: int64, SC-4: int64, SP-2: int64, SP-3: int64, SP-3a: int64, TE-1: int64, TE-2: int64, TE-4: int64, TE-5: int64, TM-2: int64, TM-3: int64, TM-5: int64, TRJ-1: int64, TRJ-10: int64, TRJ-2: int64, TRJ-3: int64, TRJ-4: int64, TRJ-5: int64, TRJ-6: int64, TRJ-7: int64, TRJ-8: int64, TRJ-9: int64>, counts_by_task_family: struct<scenario-corner-case: int64, space-perception: int64, time-extrapolation: int64, time-memory: int64, trajectory-prediction: int64>, counts_by_format: struct<MCQ: int64, OEQ: int64>, image_paths_are_relative_to: string, notes: list<item: string>, packaged_at: string>
              to
              {'benchmark': Value('string'), 'split': Value('string'), 'source_dataset': Value('string'), 'n_questions': Value('int64'), 'n_templates': Value('int64'), 'n_scenes': Value('int64'), 'counts_by_template': {'SC-6': Value('int64'), 'SP-1': Value('int64'), 'SP-2': Value('int64'), 'SP-3': Value('int64'), 'SP-5': Value('int64'), 'SP-7': Value('int64'), 'SU-1': Value('int64'), 'SU-2': Value('int64'), 'SU-4': Value('int64'), 'SU-7': Value('int64'), 'TE-1': Value('int64'), 'TE-6': Value('int64'), 'TM-2': Value('int64'), 'TM-3': Value('int64'), 'TM-4': Value('int64'), 'TM-5': Value('int64'), 'TRJ-1': Value('int64'), 'TRJ-2': Value('int64'), 'TRJ-3': Value('int64'), 'TRJ-4': Value('int64'), 'TRJ-5': Value('int64'), 'TRJ-6': Value('int64'), 'TRJ-7': Value('int64')}, 'counts_by_task_family': {'scene-context': Value('int64'), 'space-perception': Value('int64'), 'space-understanding': Value('int64'), 'time-extrapolation': Value('int64'), 'time-memory': Value('int64'), 'trajectory-prediction': Value('int64')}, 'counts_by_format': {'OEQ': Value('int64'), 'MCQ': Value('int64')}, 'notes': List(Value('string')), 'packaged_at': Value('string')}

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

STRIDE

STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases

Paper · Code · Project page

This Hub repo contains question annotations (and nuScenes group sidecars). Raw camera images are not included; download them from nuScenes and Waymo Open Dataset under their terms, then follow the GitHub prepare scripts.

Dataset summary

Split	Questions	Source images	Files
nuScenes	1,150	nuScenes trainval	`nuScenes/questions.json`, `nuScenes/formatted_metadata/`
Waymo	1,200	Waymo Open Dataset val	`Waymo/questions.json`
Mini	7 (demo only)	same as above	`Mini/`

39 templates in six families: spatial perception (SP), spatial understanding (SU), temporal memory (TM), temporal extrapolation (TE), trajectory prediction (TRJ), scene-context awareness (SC). Formats: MCQ, open-ended (OEQ), and waypoint regression (TRJ-5 / TRJ-6). Each question uses five frames.

Load

from huggingface_hub import hf_hub_download
import json

path = hf_hub_download(
    repo_id="uclanlp/STRIDE",
    filename="nuScenes/questions.json",
    repo_type="dataset",
)
payload = json.load(open(path))
tasks = payload["tasks"]
print(payload["meta"]["n_questions"], tasks[0]["id"], tasks[0]["question"][:80])

For inference and scoring, clone the code repo and run scripts/prepare_*_media.py / python -m stride.cli score.

License

STRIDE annotation files in this repository are released under MIT. nuScenes and Waymo images remain under their original licenses and must be obtained from those datasets. Do not redistribute those images through this Hub repo.

Downloads last month: 83

Total file size:

20 MB