13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
139 lines
4.1 KiB
Markdown
139 lines
4.1 KiB
Markdown
# MeasureBench
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## Overview
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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.
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## Task Description
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- **Task Type**: Free-form Visual Question Answering (instrument reading)
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- **Input**: An image of a measuring instrument + a reading question
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- **Output**: The instrument's current reading (numeric value or time, with unit)
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- **Domains**: Ammeters, clocks, thermometers, scales, speedometers, and 21 more instrument types
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## Key Features
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- 2,442 total samples across two splits: real_world (1,272) and synthetic_test (1,170)
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- 26 instrument types, 4 design categories (dial, digital, analog, linear)
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- Accepts a tolerance interval around the correct value rather than requiring an exact match
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- For clocks: handles both 12-hour and 24-hour ambiguity via multiple valid intervals
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- Unit recognition is evaluated separately from numeric accuracy
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## Evaluation Notes
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- Default splits: **real_world** and **synthetic_test** (treated as separate subsets)
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- Primary metric: **Accuracy** (`accuracy`) — ``all_correct``: number *and* unit both correct
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- Secondary metrics: **number_acc** (numeric only), **unit_acc** (unit only)
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- Two evaluators: ``interval_matching`` (single valid range) and ``multi_interval_matching`` (e.g. clock AM/PM)
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- Model output is expected in the format ``Answer: <value> <unit>`` on the last line
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- ``image_type`` is recorded in each sample's metadata; per-type results are visible in the
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``subset_key`` column of review files but are not separately selectable via ``subset_list``
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- [Paper](https://arxiv.org/abs/2510.26865) | [GitHub](https://github.com/flageval-baai/MeasureBench)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `measure_bench` |
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| **Dataset ID** | [evalscope/MeasureBench](https://modelscope.cn/datasets/evalscope/MeasureBench/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/2510.26865) |
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| **Tags** | `MultiModal`, `QA`, `Reasoning` |
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| **Metrics** | `accuracy`, `number_acc`, `unit_acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `real_world` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 2,442 |
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| Prompt Length (Mean) | 150.9 chars |
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| Prompt Length (Min/Max) | 126 / 215 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `real_world` | 1,272 | 153.83 | 131 | 215 |
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| `synthetic_test` | 1,170 | 147.71 | 126 | 192 |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 2,442 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 108x79 - 3025x1599 |
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| Formats | jpeg, png |
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## Sample Example
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**Subset**: `real_world`
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```json
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{
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"input": [
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{
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"id": "1341f508",
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"content": [
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{
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"image": "[BASE64_IMAGE: jpeg, ~75.8KB]"
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},
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{
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"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"
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}
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]
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}
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],
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"target": "",
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"id": 0,
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"group_id": 0,
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"subset_key": "ammeter",
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"metadata": {
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"question_id": "ammeter_0",
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"image_type": "ammeter",
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"design": "dial",
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"evaluator": "interval_matching",
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"evaluator_kwargs": "{\"interval\": [9.5, 9.7], \"units\": [\"A\", \"Ampere\"]}"
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}
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}
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```
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## Prompt Template
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*No prompt template defined.*
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets measure_bench \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['measure_bench'],
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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