---
language:
- ar
pretty_name: "COCO-ARVQA: Arabic Visual Question Answering over COCO 2017"
license: cc-by-4.0
task_categories:
- visual-question-answering
- image-to-text
tags:
- arabic
- vqa
- visual-question-answering
- coco
- coco2017
- multimodal
- vision-language
- question-answering
- low-resource-language
- arabic-nlp
size_categories:
- 100K<n<1M
configs:
- config_name: default
  default: true
  data_files:
  - split: train
    path: data/train.jsonl
  - split: validation
    path: data/validation.jsonl
---

# COCO-ARVQA: Arabic Visual Question Answering over COCO 2017

## Dataset Summary

**COCO-ARVQA** is an Arabic Visual Question Answering dataset built over images from **MS COCO 2017 train2017**.  
It provides Arabic questions, Arabic answers, answer lists, question identifiers, image identifiers, and COCO image file names.

This repository does **not** redistribute COCO images. Both the training and validation splits reference images from the official COCO 2017 `train2017.zip` archive.

Official COCO image archive used by this dataset:

`http://images.cocodataset.org/zips/train2017.zip`

## Dataset Structure

```text
coco-arvqa/
├── README.md
├── CITATION.cff
├── LICENSE
├── dataset_stats.json
├── data/
│   ├── train.jsonl
│   └── validation.jsonl
└── assets/
    ├── split_examples.png
    ├── unique_images.png
    ├── answer_type_distribution.png
    └── top_question_types_train.png
```

## Splits

| Split | Examples | Unique Images | Image Source |
|---|---:|---:|---|
| Train | 93,645 | 49,894 | COCO 2017 train2017 |
| Validation | 10,361 | 5,544 | COCO 2017 train2017 |
| Total | 104,006 | 55,438 | COCO 2017 train2017 |

## Dataset Visualizations

### Examples per Split

![Examples per Split](assets/split_examples.png)

### Unique Images per Split

![Unique Images per Split](assets/unique_images.png)

### Answer Type Distribution

![Answer Type Distribution](assets/answer_type_distribution.png)

### Top Question Types in the Train Split

![Top Question Types](assets/top_question_types_train.png)

## Answer Type Distribution

| Answer Type | Train | Validation | Total |
|---|---:|---:|---:|
| yes/no | 36,157 | 3,925 | 40,082 |
| number | 26,591 | 2,921 | 29,512 |
| other | 30,897 | 3,515 | 34,412 |

<!-- DATASET_EXAMPLES_START -->

## Dataset Examples

The following examples illustrate the structure of COCO-ARVQA. Images are not redistributed in this repository. Each example can be matched with its COCO image using the image_file_name field after downloading the official COCO 2017 train2017.zip archive.

Official COCO image archive:

http://images.cocodataset.org/zips/train2017.zip

### Training Examples

| Split | Image File | Arabic Question | Arabic Answer | Answer Type |
|---|---|---|---|---|
| train | 000000458752.jpg | ما لون قميص اللاعبين؟ | برتقالي | other |
| train | 000000262146.jpg | ما لون الثلج؟ | أبيض | other |
| train | 000000262146.jpg | ماذا يفعل الشخص؟ | التزلج | other |
| train | 000000262146.jpg | ما لون غطاء رأس الشخص؟ | أحمر | other |
| train | 000000524291.jpg | ماذا يحمل الشخص في يده؟ | قرص طائر | other |
| train | 000000524291.jpg | هل الكلب ينتظر؟ | نعم | yes/no |

### Validation Examples

| Split | Image File | Arabic Question | Arabic Answer | Answer Type |
|---|---|---|---|---|
| validation | 000000393227.jpg | هل لدى الرجل وشم؟ | نعم | yes/no |
| validation | 000000393227.jpg | على ماذا يركب هذا الرجل؟ | لوح تزلج | other |
| validation | 000000393227.jpg | كم وشمًا يمكن رؤيته على جسم هذا الرجل؟ | 1 | number |
| validation | 000000131127.jpg | هل الرجل غير سعيد؟ | لا | yes/no |

### Example JSON Record

{
  "task": "vqa_ar",
  "question_id": 458752002,
  "image_id": 458752,
  "image_file_name": "000000458752.jpg",
  "coco_split": "train2017",
  "coco_images_zip": "http://images.cocodataset.org/zips/train2017.zip",
  "question_ar": "ما لون قميص اللاعبين؟",
  "prompt": "أجب عن السؤال بالعربية: ما لون قميص اللاعبين؟",
  "answer_ar": "برتقالي",
  "multiple_choice_answer_ar": "برتقالي",
  "answers_ar": [
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي",
    "برتقالي"
  ],
  "question_type_en": "what color is the",
  "answer_type": "other",
  "source_dataset": "MS COCO 2017 train2017",
  "dataset_name": "COCO-ARVQA"
}

A small preview file is also provided at:

examples/sample_examples.jsonl

<!-- DATASET_EXAMPLES_END -->

## Data Fields

Each JSONL example contains the following fields:

| Field | Type | Description |
|---|---|---|
| `task` | string | Task name, usually `vqa_ar`. |
| `question_id` | integer/string | Question identifier. |
| `image_id` | integer/string | COCO image identifier. |
| `image_file_name` | string | File name of the image inside COCO `train2017`. |
| `coco_split` | string | COCO image split used by the dataset, here `train2017`. |
| `coco_images_zip` | string | Official COCO archive URL for the images. |
| `question_ar` | string | Arabic visual question. |
| `prompt` | string | Instruction-style Arabic prompt. |
| `answer_ar` | string | Main Arabic answer. |
| `multiple_choice_answer_ar` | string | Arabic multiple-choice-style answer, when available. |
| `answers_ar` | list[string] | List of Arabic answer annotations. |
| `question_type_en` | string | Original or inherited English question type category. |
| `answer_type` | string | Answer category: `yes/no`, `number`, or `other`. |
| `source_dataset` | string | Image source dataset. |
| `dataset_name` | string | Dataset name. |

## Example

```json
{
  "task": "vqa_ar",
  "question_id": 458752002,
  "image_id": 458752,
  "image_file_name": "000000458752.jpg",
  "coco_split": "train2017",
  "coco_images_zip": "http://images.cocodataset.org/zips/train2017.zip",
  "question_ar": "ما لون قميص اللاعبين؟",
  "prompt": "أجب عن السؤال بالعربية: ما لون قميص اللاعبين؟",
  "answer_ar": "برتقالي",
  "multiple_choice_answer_ar": "برتقالي",
  "answers_ar": ["برتقالي", "برتقالي"],
  "question_type_en": "what color is the",
  "answer_type": "other"
}
```

## How to Use

### Load the annotations from Hugging Face

```python
from datasets import load_dataset

ds = load_dataset("MouaffakAyoub/coco-arvqa")
print(ds)
print(ds["train"][0])
```

### Download and match COCO images

Download and extract the official COCO 2017 train images:

```bash
wget http://images.cocodataset.org/zips/train2017.zip
unzip train2017.zip
```

Then match each example with its image:

```python
from pathlib import Path

image_root = Path("train2017")
example = ds["train"][0]
image_path = image_root / example["image_file_name"]

print(image_path)
```

## Dataset Creation

The dataset was created for Arabic Visual Question Answering research.  
It links Arabic question-answer annotations to COCO 2017 image file names. The training and validation splits are internal splits of COCO-ARVQA; both reference images from COCO 2017 `train2017`.

## Intended Uses

This dataset is intended for:

- Arabic Visual Question Answering.
- Multilingual and Arabic vision-language modeling.
- Parameter-efficient adaptation of multimodal models.
- Low-resource multimodal learning research.
- Evaluation of Arabic VQA systems.

## Out-of-Scope Uses

This dataset is not intended for:

- Biometric identification or surveillance.
- Face recognition or person identification.
- Legal, medical, or safety-critical decision-making.
- Commercial use of COCO images without checking the original image licenses.
- Any use that violates COCO, Flickr, or original image-owner terms.

## Limitations

- Images are not included in this repository.
- Both train and validation examples reference COCO 2017 `train2017`.
- Arabic questions and answers may contain translation or generation noise.
- The dataset may inherit visual, cultural, and social biases from COCO images and from the automatic generation/translation process.
- Some answers are short, such as colors, numbers, or yes/no responses.

## Licensing and Copyright

### Arabic VQA Annotations

The Arabic question-answer annotations in this repository are released under **CC BY 4.0**, unless otherwise specified by the dataset authors. Users should cite the dataset and preserve attribution.

### COCO Images

COCO images are **not redistributed** in this repository. Users must download the images from the official COCO archive and comply with the original image licenses and Flickr terms.

### COCO Annotations

The original COCO annotation set belongs to the COCO Consortium and is commonly distributed under Creative Commons Attribution 4.0. COCO images themselves follow Flickr/image-owner licensing terms.

## Citation

If you use this dataset, please cite it as:

```bibtex
@dataset{mouaffak2026cocoarvqa,
  title  = {COCO-ARVQA: Arabic Visual Question Answering over COCO 2017},
  author = {Mouaffak, Ayoub},
  year   = {2026},
  url    = {https://huggingface.co/datasets/MouaffakAyoub/coco-arvqa},
  note   = {Arabic Visual Question Answering annotations over COCO 2017 train2017 images}
}
```

## Acknowledgements

This dataset builds on MS COCO 2017 image identifiers and image file names. Users should also cite the Microsoft COCO paper when using the associated images.

## Contact

For questions, corrections, or citation updates, contact the dataset maintainer: **ayoubmouaffak@gmail.com**.