size_categories:1K<n<10K format:parquet modality:image modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: filename
    dtype: string
  - name: id
    dtype: int64
  - name: lan1
    dtype: string
  - name: image1
    dtype: string
  - name: coords1
    sequence: int64
  - name: available1
    dtype: bool
  - name: lan2
    dtype: string
  - name: image2
    dtype: string
  - name: coords2
    sequence: int64
  - name: available2
    dtype: bool
  splits:
  - name: test
    num_bytes: 842457
    num_examples: 1119
  download_size: 197184
  dataset_size: 842457
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
# Dataset Card for "MuST-Bench"

## MuST-Bench
This repository is the official implementation of MuST-Bench dataset reconstruction.

>📋  Once all the steps are completed, the final results will be saved in the './must-bench' directory.

### Requirements

To install requirements:

```setup
pip install -r requirements.txt
```


### Download Multilingual Poster Data

To get the multilingual poster, run this command:

```
python get_posters.py data.json 
```

>📋  Once the execution is complete, the data will be saved in the './data' directory.


### Create Multilingual Pair Image Patch

To get the image patch, run this command:

```
python get_stylesubject.py data.json ./data
```
>📋  Once the execution is complete, the data will be saved in the './must-bench' directory.


Make multilingual pair:
```
python merge_language.py US CN
python merge_language.py US KR
python merge_language.py US RU
python merge_language.py US AR
python merge_language.py US TH
```
📋  Once the execution is complete, the pair data will be saved in the './must-bench' directory.


## Dataset Summary

This study introduces Multimodal Style Translation (MuST-Bench) to evaluate generative models' ability to translate visual text design across languages.

### Language

English, Chinese, Korean, Arabic, Thailand, Russian

## Dataset Structure

Here's an example instance of the dataset:

```
{
  "1421.jpg": {
                "US": {
                    "coords": [
                        143,
                        226,
                        203,
                        276,
                        ...
                    ],
                    "available": true,
                    "link": "https://media-cache.cinematerial.com/p/500x/jumpyg9b/dolphins-and-whales-3d-tribes-of-the-ocean-movie-poster.jpg?v=1456324064"
                },
                "RU": {
                    "coords": [
                        148,
                        229,
                        195,
                        283,
                        ...
                    ],
                    "available": true,
                    "link": "https://media-cache.cinematerial.com/p/500x/g0ylqdmy/dolphins-and-whales-3d-tribes-of-the-ocean-russian-movie-poster.jpg?v=1456279804"
                }
            }
},
```

Please note that we only provide URLs to the images in compliance with their licenses.

### Data Instances

See above

### Data Fields

See above

### Curation Rationale

Full details are in the paper.

### Source Data

We obtain our images from this source: [Cinematerial](https://www.cinematerial.com/). Please note that we only provide URLs to these images to comply with their usage terms.

### Initial Data Collection

Full details are in the paper.

### Annotations

Full details are in the paper.

#### Annotation Process

Full details are in the paper.

#### Who are the annotators?

Authors of the paper.

### Licencing Information

The annotations we provide are licensed under CC-BY-NC. The images are governed by their respective rights.