Datasets:
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license: mit
language:
- en
pretty_name: TactileEval
size_categories:
- 10K<n<100K
---
# TactileEval Dataset
TactileEval decomposes tactile-graphic quality into five BANA-aligned quality
dimensions (view, parts, background, texture, line quality) across six object
families, yielding 30 task families and 14,095 option-level annotations. Each
record corresponds to a natural photo / tactile drawing pair and a specific
quality option with majority-vote label, vote counts, and provenance metadata.
## Repository layout
```
images/
<Family>/<Object>/<Natural|Tactile>/*.{jpg,png}
processed/
records_full.jsonl
splits/{train,val,test}.jsonl
family_splits/F{1..6}/{train,val,test}.jsonl
dataset_summary.{csv,json}
```
JSONL fields:
| Field | Description |
|-------|-------------|
| `pair_id` | `NaturalRel::TactileRel` identifier |
| `task_family` | Task code (e.g., F1QL) |
| `option_id` | Option identifier (e.g., `too_thick`) |
| `option_description` | Plain-language description |
| `natural_image`, `tactile_image` | Paths relative to `images/` |
| `votes_total`, `positives`, `negatives` | Vote stats |
| `label` | Majority label (0/1) |
| `vote_fraction` | positives / total |
| `status_counts` | counts of approved/submitted ballots |
## Splits
- `splits/train.jsonl` (11,348 records)
- `splits/val.jsonl` (1,341 records)
- `splits/test.jsonl` (1,406 records)
`family_splits/` mirrors these splits per object family.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("Adnank1998/TactileEval", name="full", split="train")
family = load_dataset("Adnank1998/TactileEval", name="family_f1", split="train")
example = ds[0]
print(example["natural_image"], example["label"])
```
Images are stored under `images/`; join the relative path returned in
`natural_image`/`tactile_image` with the local dataset root to load the files.
Available configurations:
- `full`: All families (default).
- `family_f1` through `family_f6`: Per-family subsets matching the paper splits.
Each split lives in `processed/`, enabling the Hugging Face dataset viewer via
the bundled `dataset_infos.json`.
Available configurations:
- `full`: All families (default).
- `family_f1` … `family_f6`: Per-family subsets matching the paper splits.
Each split lives in `processed/`, so the Hugging Face dataset viewer can load the files directly via the bundled `dataset_infos.json`.
## Citation
```
@misc{khan2026tactileevalstepautomatedfinegrained,
title={TactileEval: A Step Towards Automated Fine-Grained Evaluation and Editing of Tactile Graphics},
author={Adnan Khan and Abbas Akkasi and Majid Komeili},
year={2026},
eprint={2604.19829},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2604.19829}
}
```
## Contact
Questions? Open an issue or email adnankhan5@cmail.carleton.ca.
## Acknowledgements
This work was supported in part by MITACS and the Digital Alliance of Canada.
We thank the student volunteers at the Intelligent Machines Lab (iML), Carleton
University, for their contributions, and Joshua Olojede and Hoda Vafaeesefat
for their help with the AMT annotation environment.
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