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OST-Bench

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test

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
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1219
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1914, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  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
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1219
              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

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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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