OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
Paper • 2507.07984 • Published • 43
Datasets:
Dataset Viewer
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
type: string
new_observations: list<item: string>
child 0, item: string
turn_id: int64
scan_id: string
origin_question: string
option: list<item: string>
child 0, item: string
answer: string
user_message: string
system_prompt: string
to
{'scan_id': Value('string'), 'turn_id': Value('int64'), 'type': Value('string'), 'new_observations': List(Value('string')), 'origin_question': Value('string'), 'option': List(Value('string')), 'answer': 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 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
type: string
new_observations: list<item: string>
child 0, item: string
turn_id: int64
scan_id: string
origin_question: string
option: list<item: string>
child 0, item: string
answer: string
user_message: string
system_prompt: string
to
{'scan_id': Value('string'), 'turn_id': Value('int64'), 'type': Value('string'), 'new_observations': List(Value('string')), 'origin_question': Value('string'), 'option': List(Value('string')), 'answer': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
This page contains the data for the paper "OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding."
🌐 Homepage | 📑 Paper | 💻 Code | 📖 arXiv
Introduction
Download OST-Bench for evaluation only:
huggingface-cli download rbler/OST-Bench --include OST_bench.json,img.zip --repo-type dataset
Download OST-Bench for both training and evaluation:
huggingface-cli download rbler/OST-Bench --repo-type dataset
Dataset Description
The imgs/img_train zipfile contains image data corresponding to 1.4k/7k scenes. Each scene has its own subfolder, which stores the observations captured by the agent while exploring that scene.
OST_bench.json/OST_bench_train.json consists of 10k/50k data samples, where each sample represents one round of Q&A (question and answer) and includes the new observations for that round. The structure of each sample (dictionary) is as follows:
{
"scan_id" (str): Unique identifier for the scene scan,
"system_prompt" (str): Shared context/prompt for the multi-turn conversation,
"turn_id" (int): Index of the current turn in the dialogue,
"type" (str): Question subtype/category,
"origin_question" (str): Original question text,
"answer" (str): Ground-truth answer,
"option" (list[str]): Multiple-choice options,
"new_observations" (list[str]): Relative paths to new observation images (within `imgs` dir),
"user_message" (str): Formatted input prompt for the model,
}
Samples with the same scan_id belong to the same multi-turn conversation group. During model evaluation, each multi-turn conversation group is processed as a unit: the shared system_prompt is provided, and new observations along with questions are fed in sequentially according to turn_id.
Evaluation Instructions
Please refer to our evaluation code for details.
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