README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - question-answering
  - multiple-choice
  - visual-question-answering
  - image-text-to-text
language:
  - en
pretty_name: OST-Bench
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: scan_id
      dtype: string
    - name: turn_id
      dtype: int64
    - name: type
      dtype: string
    - name: new_observations
      sequence: string
    - name: origin_question
      dtype: string
    - name: option
      sequence: string
    - name: answer
      dtype: string
  splits:
    - name: test
      num_examples: 10000
configs:
  - config_name: default
    data_files:
      - split: test
        path: OST_bench.json

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.