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Datasets:
Harisundar
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PALL-VLM-data

Tasks:
Image-Text-to-Text
Visual Question Answering
Image Classification
Modalities:
Image
Text
Formats:
json
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
dental
medical
multimodal
vision-language
vqa
pall
Libraries:
Datasets
pandas
Polars
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PALL-VLM-data / README.md
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metadata 
license: cc-by-nc-4.0
language:
  - en
task_categories:
  - image-text-to-text
  - visual-question-answering
  - image-classification
tags:
  - dental
  - medical
  - multimodal
  - vision-language
  - vqa
  - pall
size_categories:
  - 10K<n<100K

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.

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:

{
  "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 

@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 

@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}},

}