README.md

---
dataset_info:
  features:
  - name: qid
    dtype: uint32
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: q_lang
    dtype:
      class_label:
        names:
          '0': en
          '1': zh
  - name: img_id
    dtype: uint32
  - name: location
    dtype:
      class_label:
        names:
          '0': Abdomen
          '1': Lung
          '2': Chest_heart
          '3': Chest_lung
          '4': Brain_Tissue
          '5': Brain_Face
          '6': Brain
          '7': Neck
          '8': Chest_mediastinal
          '9': Pelvic Cavity
  - name: modality
    dtype:
      class_label:
        names:
          '0': MRI
          '1': CT
          '2': X-Ray
  - name: base_type
    dtype:
      class_label:
        names:
          '0': vqa
          '1': kvqa
  - name: answer_type
    dtype:
      class_label:
        names:
          '0': OPEN
          '1': CLOSED
  - name: content_type
    dtype:
      class_label:
        names:
          '0': Modality
          '1': Position
          '2': Organ
          '3': Size
          '4': Abnormality
          '5': Quantity
          '6': Plane
          '7': Shape
          '8': Color
          '9': KG
  - name: triple
    list: string
  splits:
  - name: test
    num_bytes: 215442470
    num_examples: 2094
  - name: train
    num_bytes: 1331237033
    num_examples: 9835
  - name: validation
    num_bytes: 195808761
    num_examples: 2099
  download_size: 1324949790
  dataset_size: 1742488264
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
- zh
tags:
- medical
---

Fork of [BoKelvin/SLAKE](https://huggingface.co/datasets/BoKelvin/SLAKE) converted to:

1. Wrap images as binary object
2. Classify categorical information into class labels

## Metadata

| Name  | #train | #val  | #test | img#train | img#val | img#test |
| :---: | :----: | :---: | :---: | :-------: | :-----: | :------: |
| SLAKE | 9,835  | 2,099 | 2,094 |    586    |   174   |   180    |

### Conversion script

```py
from pathlib import Path

from datasets import ClassLabel, Dataset, Features, Image, Sequence, Value

SLAKE_FEAT = {
    "qid": Value("uint32"),
    "image": Image(decode=True),
    "question": Value("string"),
    "answer": Value("string"),
    "q_lang": ClassLabel(names=["en", "zh"]),
    "img_id": Value("uint32"),
    "location": ClassLabel(
        names=[
            "Abdomen",
            "Lung",
            "Chest_heart",
            "Chest_lung",
            "Brain_Tissue",
            "Brain_Face",
            "Brain",
            "Neck",
            "Chest_mediastinal",
            "Pelvic Cavity",
        ]
    ),
    "modality": ClassLabel(names=["MRI", "CT", "X-Ray"]),
    "base_type": ClassLabel(names=["vqa", "kvqa"]),
    "answer_type": ClassLabel(names=["OPEN", "CLOSED"]),
    "content_type": ClassLabel(
        names=[
            "Modality",
            "Position",
            "Organ",
            "Size",
            "Abnormality",
            "Quantity",
            "Plane",
            "Shape",
            "Color",
            "KG",
        ]
    ),
    "triple": Sequence(Value("string")),
}


def reformat(
    jsonl_path: Path,
    upload_to: str | None = None,
    image_dir: str = "images",
):
    split = jsonl_path.stem
    d = Dataset.from_json(jsonl_path.as_posix())
    d = d.map(
        lambda e: {
            "image": {"path": f"{image_dir}/{e['img_name']}"},
        },
        num_proc=16,
        features=Features(SLAKE_FEAT),
        remove_columns=["img_name"]
    )

    if upload_to:
        d.push_to_hub(upload_to, split=split)
    else:
        print(d)
        print(d[0])
        print(f"Would upload to split={split} on the hub.")
```