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--- |
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license: apache-2.0 |
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language: |
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- zh |
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--- |
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# QZhou-Flowchart-QA-Benchmark: Real-World Flowchart Understanding Benchmark |
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## Overview |
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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. |
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## Dataset Composition |
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### Part 1: Web-Collected Real-World Flowcharts (Public) |
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Manually curated flowcharts from image search engines, covering actual deployment scenarios including: |
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- Government services and administrative processes |
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- Banking and financial operations |
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- Campus management systems |
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- Daily office workflows |
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- Financial processing procedures |
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**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. |
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**Annotation:** All questions and answers are carefully labeled and verified by human annotators. |
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### Part 2: Enterprise Office Flowcharts (Coming Soon) |
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Real flowcharts from production office environments, including: |
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- HR management processes |
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- Financial reimbursement workflows |
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- Internal approval procedures |
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*Note: This portion is currently undergoing anonymization and will be released in a future update.* |
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## Question Diversity |
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FlowchartBench ensures comprehensive query coverage, considering various questioning angles and possibilities: |
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- Upstream and downstream node queries |
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- Conditional branch reasoning |
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- Path analysis and node relationships |
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- Structural understanding |
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- Spatial reasoning with X/Y axes |
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## Performance Leaderboard |
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State-of-the-art results on QZhou-Flowchart-QA-Benchmark: |
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| Model | QZhou-Flowchart-QA-Benchmark (%) | |
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|-------|-------------------| |
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| **QZhou-Flowchart-VL-32B (Ours)** | **87.83** | |
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| Qwen3-VL-Plus-Thinking (235B) | 86.09 | |
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| Gemini-2.5-Pro | 84.42 | |
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| doubao-seed-1-6 | 83.83 | |
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| GPT-5 | 79.29 | |
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| GLM-4.5V | 75.97 | |
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| Qwen2.5-VL-32B | 73.90 | |
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### Comparison with Base Model |
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| Model | MMMU | CMMU | MathVista | DocVQA | QZhou-Flowchart-QA-Benchmark | |
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|-------|------|------|-----------|--------|----------------| |
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| Qwen2.5-VL-32B | 66.67 | 76.38 | 74.20 | 93.96 | 73.90 | |
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| **QZhou-Flowchart-VL-32B** | **67.78** | **76.46** | **76.50** | 93.87 | **87.83** | |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load benchmark |
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benchmark = load_dataset("Kingsoft-LLM/QZhou-Flowchart-QA-Benchmark", split="test") |
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# Evaluate your model |
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for sample in benchmark: |
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prediction = model.predict(sample['image'], sample['question']) |
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accuracy = evaluate(prediction, sample['answer']) |
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``` |
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## Evaluation Protocol |
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- **Answer Matching:** Two evaluation methods based on question type: |
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- **Exact Match:** For multiple-choice questions, direct comparison with ground truth |
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- **Normalized Edit Distance:** For open-ended questions, score calculated as `1 - (edit_distance / max_length)` |
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- **Metrics:** Overall accuracy, breakdown by question type, domain, and complexity level |
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- **Submission:** Open a GitHub issue with your model predictions to be added to the leaderboard |
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## Key Features |
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✅ **Real-world scenarios** - Flowcharts from actual deployments |
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✅ **Manual annotation** - Human-verified questions and answers |
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✅ **Quality diversity** - Various resolutions, clarity levels, and sizes |
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✅ **Comprehensive coverage** - 20+ question types across multiple domains |
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✅ **Rigorous evaluation** - Standardized protocol for fair comparison |
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