SURDS

Overview

SURDS benchmarks fine-grained spatial understanding and reasoning by vision-language models in realistic driving scenes. It is derived from the six-camera nuScenes dataset and evaluates object-centric and relational spatial skills without supplying depth maps or visual markers.

Task Description

• Task Type: Multi-task visual spatial question answering

• Input: A 1600 x 900 driving-scene image and an English spatial reasoning question

• Output: A structured response ending in an answer inside <answer>...</answer>

• Domain: Autonomous driving and outdoor 3D spatial reasoning

Key Features

• 9,250 model queries generated deterministically from 5,919 validation images, following the official seed-42 code

• Six equally weighted task subsets: yaw orientation, pixel localization, depth range, pairwise distance, left/right ordering, and front/behind relation

• Yaw, distance, left/right, and front/behind are consistency tests: both complementary prompts for an evaluation unit must be correct to receive credit

• Images come from six nuScenes cameras and contain unmarked objects described by appearance rather than overlays

Evaluation Notes

• The official prompts and <think>...<answer>...</answer> response contract are reproduced verbatim

• Pixel localization uses the official centerness metric: predictions outside the target box receive 0, while points nearer the box center receive scores approaching 1; normalized coordinates and predicted boxes are also accepted

• The other five tasks use official normalized exact match, removing case, punctuation, articles, and extra whitespace

• Every subset contains 925 evaluation units; the overall normalized score is therefore the equal average of all six task scores. A full run makes 9,250 model requests but reports Num=5,550, because each complementary prompt pair is one official evaluation unit

• Invalid or missing <answer> blocks score 0, matching the official benchmark denominator semantics

• The dataset is evaluation-only and downloaded as a complete snapshot from ModelScope

• Resources: Paper | GitHub

Properties

Property	Value
Benchmark Name	surds
Dataset ID	evalscope/SURDS_eval
Paper	Paper
Tags	Grounding, MultiModal, QA, Reasoning
Metrics	normalized_score
Default Shots	0-shot
Evaluation Split	validation

Data Statistics

Metric	Value
Total Samples	9,250
Prompt Length (Mean)	728.31 chars
Prompt Length (Min/Max)	631 / 910 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
yaw	1,850	725.18	714	769
xy2d	925	677.32	672	717
depth	925	861.75	854	901
distance	1,850	770.04	743	910
left_right	1,850	658.04	631	798
front_behind	1,850	718.77	703	791

Image Statistics:

Metric	Value
Total Images	9,250
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	1600x900 - 1600x900
Formats	webp

Sample Example

Subset: yaw

{
  "input": [
    {
      "id": "ff9ad0a4",
      "content": [
        {
          "text": "Task Description: \nThe primary goal of this task is to identify the direction that the specified object is facing in the given image. The camera in the image is facing North, and you need to analyze the object's orientation based on this refe ... [TRUNCATED 232 chars] ... evant error checks.\nFinally, provide a concise and definitive response in the <answer> tag. Use the following format:\n<think>[Step-by-step reasoning with attention to detail and potential error checks]</think>\n<answer>[Final answer]</answer>\n"
        },
        {
          "image": "~/.cache/modelscope/hub/datasets/evalscope/SURDS_eval/validation/image/CAM_BACK_RIGHT/nuscenes_0033_CAM_BACK_RIGHT.webp"
        }
      ]
    }
  ],
  "target": "West",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "task": "yaw",
    "pair_id": "yaw-3",
    "variant_index": 0,
    "paired": true,
    "bbox": [
      662,
      504,
      774,
      545
    ],
    "options": [
      "North",
      "South",
      "East",
      "West"
    ],
    "image_size": [
      1600,
      900
    ],
    "image_path": "~/.cache/modelscope/hub/datasets/evalscope/SURDS_eval/validation/image/CAM_BACK_RIGHT/nuscenes_0033_CAM_BACK_RIGHT.webp"
  }
}

Note: Some content was truncated for display.

Prompt Template

No prompt template defined.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets surds \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['surds'],
    dataset_args={
        'surds': {
            # subset_list: ['yaw', 'xy2d', 'depth']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)