---
language:
- id
- sw
- ta
- tr
- zh
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
pretty_name: MaRVL
dataset_info:
  features:
  - name: id
    dtype: string
  - name: hypothesis
    dtype: string
  - name: hypo_en
    dtype: string
  - name: language
    dtype: string
  - name: label
    dtype: bool
  - name: chapter
    dtype: string
  - name: concept
    dtype: string
  - name: annotator_info
    struct:
    - name: age
      dtype: int64
    - name: annotator_id
      dtype: string
    - name: country_of_birth
      dtype: string
    - name: country_of_residence
      dtype: string
    - name: gender
      dtype: string
  - name: left_img_id
    dtype: string
  - name: right_img_id
    dtype: string
  - name: left_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  - name: right_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  - name: resized_left_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  - name: resized_right_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  - name: vertically_stacked_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  - name: horizontally_stacked_img
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  splits:
  - name: id
    num_bytes: 2079196646
    num_examples: 1128
  - name: sw
    num_bytes: 899838181
    num_examples: 1108
  - name: ta
    num_bytes: 801784098
    num_examples: 1242
  - name: tr
    num_bytes: 1373652829
    num_examples: 1180
  - name: zh
    num_bytes: 1193602152
    num_examples: 1012
  download_size: 6234764237
  dataset_size: 6348073906
configs:
- config_name: default
  data_files:
  - split: id
    path: data/id-*
  - split: sw
    path: data/sw-*
  - split: ta
    path: data/ta-*
  - split: tr
    path: data/tr-*
  - split: zh
    path: data/zh-*
---

# MaRVL
### This is a copy from the original repo: https://github.com/marvl-challenge/marvl-code

If you use this dataset, please cite the original authors:
```bibtex
@inproceedings{liu-etal-2021-visually,
    title = "Visually Grounded Reasoning across Languages and Cultures",
    author = "Liu, Fangyu  and
      Bugliarello, Emanuele  and
      Ponti, Edoardo Maria  and
      Reddy, Siva  and
      Collier, Nigel  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.818",
    pages = "10467--10485",
}
```
### Additional data
In addition to the data available in the original repo, this dataset contains the following columns
* `en_translation` --> English translation of the `hypothesis` created using Bing Translate
* `left_img` --> PIL Image
* `right_img`--> PIL Image
* `resized_left_img` --> PIL Image resized
* `resized_right_img` --> PIL Image resized
* `vertically_stacked_img` --> PIL image that contains the left and right resized images stacked vertically with a black gutter of `10px`
* `horizontally_stacked_img` --> PIL image that contains the left and right resized images stacked horizontally with a black gutter of `10px`

The images were resized using [`img2dataset`](https://github.com/rom1504/img2dataset/blob/main/img2dataset/resizer.py):
<details>
  <summary>Show code snippet</summary>
  
  ```python
  Resizer(
    image_size=640,
    resize_mode=ResizeMode.keep_ratio,
    resize_only_if_bigger=True,
  )
  ```

</details>

### How to read the images
Due to a [bug](https://github.com/huggingface/datasets/issues/4796), the images cannot be stored as PIL.Image.Images directly but need to be converted to dataset.Images-. Hence, to load them, this additional step is required:

```python
from datasets import Image, load_dataset

ds = load_dataset("floschne/marvl", split="sw")
ds.map(
    lambda sample: {
        "left_img_t": [Image().decode_example(img) for img in sample["left_img"]],
        "right_img_t": [Image().decode_example(img) for img in sample["right_img"]],
        "resized_left_img_t": [
            Image().decode_example(img) for img in sample["resized_left_img"]
        ],
        "resized_right_img_t": [
            Image().decode_example(img) for img in sample["resized_right_img"]
        ],
        "vertically_stacked_img_t": [
            Image().decode_example(img) for img in sample["vertically_stacked_img"]
        ],
        "horizontally_stacked_img_t": [
            Image().decode_example(img) for img in sample["horizontally_stacked_img"]
        ],
    },
    remove_columns=[
        "left_img",
        "right_img",
        "resized_left_img",
        "resized_right_img",
        "vertically_stacked_img",
        "horizontally_stacked_img",
    ],
).rename_columns(
    {
        "left_img_t": "left_img",
        "right_img_t": "right_img",
        "resized_left_img_t": "resized_left_img",
        "resized_right_img_t": "resized_right_img",
        "vertically_stacked_img_t": "vertically_stacked_img",
        "horizontally_stacked_img_t": "horizontally_stacked_img",
    }
)

```