Datasetpublic

Harisundar/PALL-VLM-data

PALL-VLM-data — Dental Vision-Language Dataset The training dataset for Harisundar/PALL-VLM, a dental vision-language model. It contains 32,884 records over 52,461 images, formatted as image+text conversations for LLaVA-style instruction tuning. Curated by: Harisundar R Used by: Harisundar/PALL-VLM · PALL on GitHub Language: English Layout vlm_train/ ├── images/ # 52,461 dental images ├── train.jsonl # 29,667 records ├── val.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/Harisundar/PALL-VLM-data.

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Dataset Card

PALL-VLM-data — Dental Vision-Language Dataset

The training dataset for `Harisundar/PALL-VLM`, a dental vision-language model. It contains 32,884 records over 52,461 images, formatted as image+text conversations for LLaVA-style instruction tuning.

—Curated by: Harisundar R
—Used by: `Harisundar/PALL-VLM` · PALL on GitHub
—Language: English

Layout

vlm_train/
├── images/                  # 52,461 dental images
├── train.jsonl              # 29,667 records
├── val.jsonl                #  1,640 records
├── test.jsonl              #  1,577 records
└── manifest.json            # provenance / split & source distribution

Schema

Each JSONL row is a conversation referencing one or more images:

json

{
  "id": "...",
  "source": "oral_cancer_photos_pq",
  "task_type": "vqa",
  "messages": [
    {"role": "user", "content": "<image>\n<question>"},
    {"role": "assistant", "content": "<answer>"}
  ],
  "images": ["images/rel/path.jpg"]
}

The number of <image> tokens in the user turn always equals len(images) (single- and multi-image rows; 7,228 records are multi-image).

Composition

Splits: train 29,667 · val 1,640 · test 1,577.

Task subtypes: classification 23,728 · detection 2,564 · caption 1,231 · report 998 · segmentation 562 · (other 3,801).

Source Attribution & Citations

This dataset was assembled from multiple publicly available dental image datasets and sources. We gratefully acknowledge the original creators.

Source	Records	Task(s)	Attribution
Oral cancer clinical photos (PQ)	10,002	classification	Kaggle oral cancer image dataset contributors
CODE oral classification	7,546	classification	CODE oral lesion classification dataset
Oral cancer histopathology	5,127	classification	Community histopathology datasets
Dental textbook figures	3,221	VQA, caption	Various textbook authors (see PALL-Text card)
Radiograph caries (ICDAS)	1,431	classification, detection	ICDAS Foundation; Ismail, A.I. et al. (2007). The International Caries Detection and Assessment System (ICDAS). Community Dentistry and Oral Epidemiology, 35(3), 170–178
Dental samples	1,082	mixed	Community dental image datasets
SMART oral photos	1,071	classification	SMART oral lesion dataset contributors
Tufts Dental Database	998	report generation	Panetta, K., Rajendran, R., Ramesh, A., Rao, S., & Agaian, S. (2022). Tufts Dental Database. IEEE J. Biomed. Health Inform., 26(4), 1650–1659
DENTEX — quadrant detection	676	detection	Hamamci, I.E. et al. (2023). DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays. arXiv:2305.19112
Dental radiology	580	classification	Community dental radiology datasets
Oral cancer clinical photos (2)	544	classification	Kaggle oral cancer datasets
DENTEX — disease classification	407	classification	Hamamci, I.E. et al. (2023) (same as above)
Dental jaw captions	144	captioning	Community dental datasets
DENTEX — enumeration	50	enumeration	Hamamci, I.E. et al. (2023) (same as above)
Dental image dataset	5	mixed	Community contribution

BibTeX citations for key image dataset sources

bibtex

@article{panetta2022tufts,
  title={Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems},
  author={Panetta, Karen and Rajendran, Rahul and Ramesh, Aruna and Rao, Shishir and Agaian, Sos},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={26}, number={4}, pages={1650--1659}, year={2022}, doi={10.1109/JBHI.2021.3117575}
}
@article{hamamci2023dentex,
  title={DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays},
  author={Hamamci, Ibrahim Ethem and Er, Sezgin and Simsar, Enis and Sekuboyina, Anjany
          and Gundogar, Mustafa and Stadlinger, Bernd and Mehl, Albert and Menze, Bjoern},
  journal={arXiv preprint arXiv:2305.19112}, year={2023}
}
@article{ismail2007icdas,
  title={The International Caries Detection and Assessment System (ICDAS): an integrated system for measuring dental caries},
  author={Ismail, Amid I. and Sohn, Woosung and Tellez, Marisol and Amaya, Ashley
          and Sen, Ananda and Hasson, Hana and Pitts, Nigel B.},
  journal={Community Dentistry and Oral Epidemiology}, volume={35}, number={3}, pages={170--178},
  year={2007}, doi={10.1111/j.1600-0528.2007.00347.x}
}

Intended use & limitations

—Intended: training/evaluating dental vision-language models for education and clinical-decision support.
—The set is classification-heavy; evaluation should include an image-shuffle control to guard against modality collapse.
—Wide panoramic radiographs may need tiling/AnyRes for best results (v1 uses square resize).
—Not for autonomous diagnosis. Respect the source licenses of the constituent datasets.

Citation

bibtex

@misc{rajendran2026pallvlmdata,
  title        = {PALL-VLM-data: A Dental Vision-Language Instruction Dataset},
  author       = {Rajendran, Harisundar},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Harisundar/PALL-VLM-data}},

}