evalstone/evalscope/docs/en/benchmarks/measure_bench.md

MeasureBench

Overview

MeasureBench is a comprehensive benchmark for evaluating the ability of vision-language models (VLMs) to read values from measuring instruments. It covers both real-world photographs and synthetically generated images of 26 instrument types across 4 design categories.

Task Description

• Task Type: Free-form Visual Question Answering (instrument reading)
• Input: An image of a measuring instrument + a reading question
• Output: The instrument's current reading (numeric value or time, with unit)
• Domains: Ammeters, clocks, thermometers, scales, speedometers, and 21 more instrument types

Key Features

• 2,442 total samples across two splits: real_world (1,272) and synthetic_test (1,170)
• 26 instrument types, 4 design categories (dial, digital, analog, linear)
• Accepts a tolerance interval around the correct value rather than requiring an exact match
• For clocks: handles both 12-hour and 24-hour ambiguity via multiple valid intervals
• Unit recognition is evaluated separately from numeric accuracy

Evaluation Notes

• Default splits: real_world and synthetic_test (treated as separate subsets)
• Primary metric: Accuracy (accuracy) — all_correct: number and unit both correct
• Secondary metrics: number_acc (numeric only), unit_acc (unit only)
• Two evaluators: interval_matching (single valid range) and multi_interval_matching (e.g. clock AM/PM)
• Model output is expected in the format Answer: <value> <unit> on the last line
• image_type is recorded in each sample's metadata; per-type results are visible in the subset_key column of review files but are not separately selectable via subset_list
• Paper | GitHub

Properties

Property	Value
Benchmark Name	measure_bench
Dataset ID	evalscope/MeasureBench
Paper	Paper
Tags	MultiModal, QA, Reasoning
Metrics	accuracy, number_acc, unit_acc
Default Shots	0-shot
Evaluation Split	real_world

Data Statistics

Metric	Value
Total Samples	2,442
Prompt Length (Mean)	150.9 chars
Prompt Length (Min/Max)	126 / 215 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
real_world	1,272	153.83	131	215
synthetic_test	1,170	147.71	126	192

Image Statistics:

Metric	Value
Total Images	2,442
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	108x79 - 3025x1599
Formats	jpeg, png

Sample Example

Subset: real_world

{
  "input": [
    {
      "id": "1341f508",
      "content": [
        {
          "image": "[BASE64_IMAGE: jpeg, ~75.8KB]"
        },
        {
          "text": "What is the reading of the instrument?\nProvide your final answer on the last line in the format: Answer: <value> <unit>. For example: Answer: 42.5 A"
        }
      ]
    }
  ],
  "target": "",
  "id": 0,
  "group_id": 0,
  "subset_key": "ammeter",
  "metadata": {
    "question_id": "ammeter_0",
    "image_type": "ammeter",
    "design": "dial",
    "evaluator": "interval_matching",
    "evaluator_kwargs": "{\"interval\": [9.5, 9.7], \"units\": [\"A\", \"Ampere\"]}"
  }
}

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 measure_bench \
    --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=['measure_bench'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)