---
title: MechVQA
canonical_url: "https://www.modelscope.cn/datasets/xiaofengalg/MechVQA"
md_url: "https://www.modelscope.cn/datasets/xiaofengalg/MechVQA.md"
repository: xiaofengalg/MechVQA
last_updated: 2026-08-04
license: "Apache License 2.0"
downloads: 206263
stars: 0
---

# MechVQA

> MechVQA - xiaofengalg 在 ModelScope 开源的数据集。MechVQA VQA SFT is a bilingual visual question answering dataset for supervised fine-tuning on mechanical engineering drawings. This public release contains 13,515 question-answer records paired with 3,371 unique,…

xiaofengalg/MechVQA 是 ModelScope 魔搭社区上的数据集，采用 Apache License 2.0 许可。

- **Repository**: xiaofengalg/MechVQA
- **License**: Apache License 2.0
- **Downloads**: 206263
- **Stars**: 0
- **Last updated**: 2026-08-04

Source: https://www.modelscope.cn/datasets/xiaofengalg/MechVQA

---

# MechVQA VQA SFT

MechVQA VQA SFT is a bilingual visual question answering dataset for
supervised fine-tuning on mechanical engineering drawings. This public release
contains **13,515 question-answer records** paired with **3,371 unique,
content-addressed images**. Assistant targets use
`<think>...</think><answer>...</answer>` formatting.

This repository is the **VQA-only SFT train/validation release** associated
with the MechVQA project. The public evaluation benchmark is maintained
separately in the [MechVQA code repository](https://github.com/xiaofengShi/MechVQA).

## Dataset Details

| Item | Description |
|---|---|
| Dataset name | MechVQA VQA SFT |
| Task | Mechanical-drawing visual question answering and multimodal SFT |
| Languages | Chinese and English |
| License | Apache License 2.0 |
| Size | 13,515 records and 3,371 unique images |
| Public release date | 2026-08-03 |
| Curators | Qian Kou, Xiaofeng Shi, Yulin Li, Xiaosong Qiu, Xinyang Wang, Hua Zhou, and Dongxing Cao |

### Dataset Sources

- **Project repository:** [xiaofengShi/MechVQA](https://github.com/xiaofengShi/MechVQA)
- **Paper:** [MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding](https://arxiv.org/abs/2605.30794)
- **Hugging Face mirror:** [XiaofengAlg/MechVQA](https://huggingface.co/datasets/XiaofengAlg/MechVQA)

## Dataset Contents

| Split | Records | Unique images |
|---|---:|---:|
| Train | 12,749 | 3,126 |
| Validation | 766 | 245 |
| **Total** | **13,515** | **3,371** |

The train and validation splits have no image-hash overlap.

### Files

- `data/train.jsonl`: 12,749 training records
- `data/val.jsonl`: 766 validation records
- `images/`: 3,371 PNG or JPEG images stored under content-addressed paths
- `dataset_info.json`: standalone LLaMA Factory dataset registration
- `checksums.sha256`: SHA-256 manifest for the release package
- `audit/`: build lineage, exclusions, image manifest, validation result, and
  data-quality report

### Languages, Capabilities, and Difficulty

| Dimension | Train | Validation | Total |
|---|---:|---:|---:|
| Chinese | 8,912 | 547 | 9,459 |
| English | 3,837 | 219 | 4,056 |
| Recognition | 5,101 | 224 | 5,325 |
| Reasoning | 2,384 | 73 | 2,457 |
| Judging | 5,264 | 469 | 5,733 |
| Easy | 5,207 | 347 | 5,554 |
| Medium | 4,222 | 261 | 4,483 |
| Hard | 3,320 | 158 | 3,478 |

Records cover ten paper-aligned VQA subcategories: Anomaly Detection, Assembly
Relationship, Consistency Judgment, Dimension & Annotation, Geometric
Calculation, Identification & Counting, Item Localization, Projection &
Multi-view, Structure Understanding, and Text & Table.

## Dataset Structure

Each JSONL record contains three public fields:

```json
{
  "messages": [
    {"role": "user", "content": "<image>...question..."},
    {
      "role": "assistant",
      "content": "<think>...reasoning...</think><answer>...answer...</answer>"
    }
  ],
  "images": ["images/ab/<sha256>.png"],
  "metadata": {
    "question_type": "VQA",
    "data_source": "...",
    "difficulty": "Easy|Medium|Hard",
    "capability": "Recognition|Reasoning|Judging",
    "subcategory": "...",
    "language": "中文|英文"
  }
}
```

- `messages`: ShareGPT-style user and assistant turns. User content retains the
  `<image>` placeholder; assistant content contains the SFT target.
- `images`: paths relative to the dataset repository root. Image paths are
  content-addressed using the underlying file SHA-256.
- `metadata`: a small public projection for filtering and analysis.

Internal audit fields and historical `original_q`, `original_a`, and
`correct_answer` fields are intentionally excluded from the public JSONL.

## Downloading the Dataset

### ModelScope CLI

```bash
pip install modelscope_hub
modelscope download xiaofengalg/MechVQA \
  --repo-type dataset \
  --local-dir ./MechVQA
```

### Python

```python
from pathlib import Path

from modelscope_hub import HubApi

root = Path(
    HubApi().download_repo(
        "xiaofengalg/MechVQA",
        repo_type="dataset",
        local_dir="./MechVQA",
        max_workers=8,
    )
)
print(root)
```

To open a referenced image:

```python
import json
from pathlib import Path

from PIL import Image

root = Path("./MechVQA")
with (root / "data/train.jsonl").open(encoding="utf-8") as handle:
    sample = json.loads(next(handle))

image = Image.open(root / sample["images"][0])
```

### LLaMA Factory

Use the downloaded release directory as both `dataset_dir` and `media_dir`:

```yaml
dataset_dir: /path/to/MechVQA
media_dir: /path/to/MechVQA
dataset: mechvqa_vqa_train
eval_dataset: mechvqa_vqa_val
template: qwen3_vl
```

The included `dataset_info.json` registers `mechvqa_vqa_train` and
`mechvqa_vqa_val` with ShareGPT formatting.

## Dataset Creation

### Curation and Processing

The release was built from reviewed VQA inputs using the following hard gates:

1. Keep only `question_type=VQA` records.
2. Keep only records whose quality status is `keep` or `fixed`.
3. Exclude the forbidden `mcmaster` data source.
4. Remove records whose exact image SHA-256 overlaps the official public
   MechVQA evaluation benchmark.
5. Remove train records that share an image with validation; validation has
   priority and no record is reassigned across splits.
6. Deduplicate exact message-plus-image training payloads, retaining the
   earliest record.
7. Materialize all images under relative, content-addressed paths and remove
   internal paths and audit-only fields from public records.

| Curation stage | Train | Validation | Total |
|---|---:|---:|---:|
| Reviewed VQA input | 15,727 | 1,948 | 17,675 |
| Quality status `keep` or `fixed` | 14,796 | 1,745 | 16,541 |
| After official benchmark decontamination | 13,220 | 766 | 13,986 |
| **Final release** | **12,749** | **766** | **13,515** |

The release records 4,160 exclusions: 1,134 for quality status, 2,555 for
official benchmark image overlap, 465 train-side cross-split image overlaps,
and 6 exact payload duplicates. Detailed lineage and exclusion records are in
`audit/lineage.jsonl` and `audit/exclusions.jsonl`.

### Validation

The packaged audit reports:

- 13,515 parseable public records
- 3,371 referenced image files and no unreferenced images
- zero train/validation image-hash overlap
- zero exact image-hash overlap with the official public benchmark
- no internal absolute paths or audit-only fields in public records
- successful release validation on 2026-08-03

The two JSONL files have these release hashes:

| File | SHA-256 |
|---|---|
| `data/train.jsonl` | `427d15a2a54a16bd5a4f3fb19e1476445f171d90a799eeada24ebebeffe110ee` |
| `data/val.jsonl` | `8bf9dfe4e2e2e82cd08275cfe8ece5b84c121559a26a182d9b36d9aa5065028d` |

## Uses

### Intended Uses

- Supervised fine-tuning of multimodal models for mechanical-drawing VQA
- Research on recognition, reasoning, and judgment over engineering drawings
- Controlled analysis or sampling by language, capability, subcategory, and
  difficulty
- Reproduction and extension of the MechVQA training setup

### Out-of-Scope Uses

- Treating generated answers or reasoning traces as certified engineering
  advice
- Unsupervised use in safety-critical manufacturing, inspection, or design
  decisions
- Using the training split itself as an unbiased evaluation benchmark
- Assuming performance transfers to every drawing standard, language, industry,
  or image acquisition condition

## Bias, Risks, and Limitations

- The distribution is uneven across languages, capabilities, subcategories,
  difficulties, and source collections. Chinese records are the majority.
- Quality review reduces known errors but does not guarantee that every answer
  or reasoning trace is correct, complete, or optimally concise.
- Benchmark decontamination uses exact image SHA-256 matching. It does not prove
  the absence of visually or semantically similar near-duplicates.
- The dataset focuses on mechanical engineering drawings and should not be
  treated as representative of general multimodal reasoning.
- Reasoning traces may teach stylistic artifacts in addition to useful domain
  reasoning. Users should evaluate both final-answer quality and trace quality.
- The release audit targets data quality, schema, paths, duplication, and
  benchmark overlap; it is not a comprehensive privacy or legal review.

Users should retain human expert review for safety-critical applications and
report suspected data issues through the
[project issue tracker](https://github.com/xiaofengShi/MechVQA/issues).

## Personal and Sensitive Information

The dataset is designed around technical drawings rather than personal data,
and no personal or sensitive attributes were intentionally collected. Because
the audit was not a comprehensive privacy review, users who identify accidental
sensitive content should report it through the project issue tracker.

## License

The release is distributed under the Apache License 2.0. See `LICENSE` in this
dataset repository for the complete terms.

## Citation

If you use this dataset, please cite the accompanying MechVQA paper:

```bibtex
@misc{kou2026mechvqabenchmarkingenhancingmultimodal,
  title        = {MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding},
  author       = {Qian Kou and Xiaofeng Shi and Yulin Li and Xiaosong Qiu and Xinyang Wang and Hua Zhou and Dongxing Cao},
  year         = {2026},
  eprint       = {2605.30794},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2605.30794}
}
```

APA:

> Kou, Q., Shi, X., Li, Y., Qiu, X., Wang, X., Zhou, H., & Cao, D. (2026).
> *MechVQA: Benchmarking and enhancing multimodal LLMs on comprehensive
> mechanical drawing understanding*. arXiv. https://arxiv.org/abs/2605.30794

## Contact

- ModelScope dataset: [xiaofengalg/MechVQA](https://modelscope.cn/datasets/xiaofengalg/MechVQA)
- GitHub issues: [xiaofengShi/MechVQA](https://github.com/xiaofengShi/MechVQA/issues)