| --- |
| 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](https://github.com/AutodeskAILab/Fusion360GalleryDataset/tree/master). |
| Please refer to [their publication](https://doi.org/10.48550/arXiv.2104.00706) for further information. Their licence statement can be found [here](https://github.com/AutodeskAILab/Fusion360GalleryDataset/blob/master/LICENSE.md). |
| 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](https://doi.org/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} |
| } |
| ``` |
| |
| |
