---
license: apache-2.0
language:
- zh
---
# QZhou-Flowchart-QA-Benchmark: Real-World Flowchart Understanding Benchmark

## Overview

While the open-source community has various chart and document benchmarks, there is no specialized evaluation set for flowchart understanding. **QZhou-Flowchart-QA-Benchmark** fills this gap by providing a dedicated benchmark to effectively assess multimodal models' flowchart comprehension abilities.

## Dataset Composition

### Part 1: Web-Collected Real-World Flowcharts (Public)

Manually curated flowcharts from image search engines, covering actual deployment scenarios including:
- Government services and administrative processes
- Banking and financial operations
- Campus management systems
- Daily office workflows
- Financial processing procedures

**Quality Diversity:** We deliberately control the distribution of image resolution and clarity, introducing varying degrees of blur and size differences to better reflect real-world application environments.

**Annotation:** All questions and answers are carefully labeled and verified by human annotators.

### Part 2: Enterprise Office Flowcharts (Coming Soon)

Real flowcharts from production office environments, including:
- HR management processes
- Financial reimbursement workflows
- Internal approval procedures

*Note: This portion is currently undergoing anonymization and will be released in a future update.*

## Question Diversity

FlowchartBench ensures comprehensive query coverage, considering various questioning angles and possibilities:
- Upstream and downstream node queries
- Conditional branch reasoning
- Path analysis and node relationships
- Structural understanding
- Spatial reasoning with X/Y axes

## Performance Leaderboard

State-of-the-art results on QZhou-Flowchart-QA-Benchmark:

| Model | QZhou-Flowchart-QA-Benchmark (%) |
|-------|-------------------|
| **QZhou-Flowchart-VL-32B (Ours)** | **87.83** |
| Qwen3-VL-Plus-Thinking (235B) | 86.09 |
| Gemini-2.5-Pro | 84.42 |
| doubao-seed-1-6 | 83.83 |
| GPT-5 | 79.29 |
| GLM-4.5V | 75.97 |
| Qwen2.5-VL-32B | 73.90 |

### Comparison with Base Model

| Model | MMMU | CMMU | MathVista | DocVQA | QZhou-Flowchart-QA-Benchmark |
|-------|------|------|-----------|--------|----------------|
| Qwen2.5-VL-32B | 66.67 | 76.38 | 74.20 | 93.96 | 73.90 |
| **QZhou-Flowchart-VL-32B** | **67.78** | **76.46** | **76.50** | 93.87 | **87.83** |

## Usage

```python
from datasets import load_dataset

# Load benchmark
benchmark = load_dataset("Kingsoft-LLM/QZhou-Flowchart-QA-Benchmark", split="test")

# Evaluate your model
for sample in benchmark:
    prediction = model.predict(sample['image'], sample['question'])
    accuracy = evaluate(prediction, sample['answer'])
```

## Evaluation Protocol

- **Answer Matching:** Two evaluation methods based on question type:
  - **Exact Match:** For multiple-choice questions, direct comparison with ground truth
  - **Normalized Edit Distance:** For open-ended questions, score calculated as `1 - (edit_distance / max_length)`
- **Metrics:** Overall accuracy, breakdown by question type, domain, and complexity level
- **Submission:** Open a GitHub issue with your model predictions to be added to the leaderboard

## Key Features

✅ **Real-world scenarios** - Flowcharts from actual deployments  
✅ **Manual annotation** - Human-verified questions and answers  
✅ **Quality diversity** - Various resolutions, clarity levels, and sizes  
✅ **Comprehensive coverage** - 20+ question types across multiple domains  
✅ **Rigorous evaluation** - Standardized protocol for fair comparison