Datasets:

WSKL
/

techmb

license: cc-by-4.0
task_categories:
  - visual-question-answering
language:
  - en
  - de
tags:
  - engineering
  - drawing
  - CAD
pretty_name: Technical drawings for Manufacturability Benchmark
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: techmb.tsv

Dataset Card for TechMB

Dataset Details

The Technical drawing for Manufacturability Benchmark (TechMB) gives a domain specific benchmark for the task of manufacturability evaluations based on technical drawings. This task is described as a Visual Question Answering (VQA) task targeted at Vision Language Models (VLM) consisting of 947 question-answer pairs on 180 distinct techical drawings. The objects, the technical drawings are developed from, represent a selection of parts of the Fusion 360 Gallery Segmentation Dataset. Please refer to their publication for further information. Their licence statement can be found here. The IDs of the parts from the f360 segmentation dataset also declare the corresponding technical drawings for better association.

Curated by: Leonhard Kunz
Funded by: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer (543073350)
Language(s) (NLP): English, German
License: CC-BY-4.0

Dataset Structure

The dataset consists contains the following fields:

task_id: ID of the specific question.
eval_type: Classifier for the expected answer type (answer matching or multiple choice).
drw_id: ID of the part and the corresponding drawing.
image: Bit64 encoded image of the exported technical drawing.
drw_complexity: Numeric complexity of the drawing. Calculated with the following formula: $complexity=(faces+dimensionings+\frac{annotation characters}{4.6})*views$
question: The question text.
answer: The expected answer corresponding to the answer type.
label_confidence: The confidence of the assorted labels in manual labelling (low, medium, high).

Citation:

Please refer to our dataset using the following DOI: doi:10.57967/hf/6214

For more information, refer to our publication:

@inproceedings{kunz2025techmb,
  title={TechMB: Exploring the Potential of Vision Language Models for Interpreting Technical Drawings},
  author={Kunz, Leonhard and Klostermeier, Mario and Thanabalan, Kokulan and Legler, Tatjana and Ruskowski, Martin and others},
  booktitle={DS 140: Proceedings of the 36th Symposium Design for X (DFX2025)},
  pages={1--10},
  year={2025},
  doi={https://doi.org/10.35199/dfx2025.19}
}