---
license: cc-by-nc-nd-4.0
task_categories:
- visual-question-answering
language:
- en
- zh
tags:
- food
- culture
- multilingual
size_categories:
- n<1K
pretty_name: Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
---


# FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
![](foodie-img.jpeg)


## Github Repo
😋 We release all tools and code used to create the dataset at https://github.com/lyan62/FoodieQA.

## Paper
For more details about the dataset, please refer to 📄 [FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture](https://arxiv.org/abs/2406.11030)

## Dataset Download
**!!Note!!** **The Json files are in the FoodieQA.zip (click on the Files and Versions tab to download), or download the dataset directly with git clone.**

## Terms and Conditions for Data Usage

By downloading and using the data, you acknowledge that you have read, understood, and agreed to the following terms and conditions.

1. **Research Purpose**: The data is provided solely for research purposes and must not be used for any commercial activities.

2. **Evaluation Only**: The data may only be used for evaluation purposes and not for training models or systems.

3. **Compliance**: Users must comply with all applicable laws and regulations when using the data.

4. **Attribution**: Proper attribution must be given in any publications or presentations resulting from the use of this data.

5. **License**: The data is released under the CC BY-NC-ND 4.0 license. Users must adhere to the terms of this license.

## Data Structure
- `/images`: contains all images needed for multi-image VQA and single-image VQA task.
- `mivqa_tidy.json` questions for Multi-image VQA task.
    - data format
        ```
        {
            "question": "哪一道菜适合喜欢吃肠的人？",
            "choices": "",
            "answer": "0",
            "question_type": "ingredients",
            "question_id": qid,
            "ann_group": "闽",
            "images": [
                img1_path, img2_path, img3_path, img4_path
            ],
            "question_en": "Which dish is for people who like intestine?"
        }
        ```
- `sivqa_tidy.json` question for Single-image VQA task.
    - data format
        ```
        {
            "question": "图片中的食物是哪个地区的特色美食?",
            "choices": [
                ...
            ],
            "answer": "3",
            "question_type": "region-2",
            "food_name": "梅菜扣肉",
            "question_id": "vqa-34",
            "food_meta": {
                "main_ingredient": [
                    "肉"
                ],
                "id": 253,
                "food_name": "梅菜扣肉",
                "food_type": "客家菜",
                "food_location": "餐馆",
                "food_file": img_path
            },
            "question_en": translated_question,
            "choices_en": [
                translated_choices1,
                ...
            ]
        }
        ```

- `textqa_tidy.json`
    - data format
        ```
        {
            "question": "酒酿圆子属于哪个菜系?",
            "choices": [
                ...
            ],
            "answer": "1",
            "question_type": "cuisine_type",
            "food_name": "酒酿圆子",
            "cuisine_type": "苏菜",
            "question_id": "textqa-101"
        },
        ```

### Models and results for the VQA tasks
| Evaluation          | Multi-image VQA (ZH) | Multi-image VQA (EN) | Single-image VQA (ZH) | Single-image VQA (EN) |
|---------------------|:--------------------:|:--------------------:|:---------------------:|:---------------------:|
| **Human**           | 91.69                | 77.22†               | 74.41                 | 46.53†                |
| **Phi-3-vision-4.2B** | 29.03               | 33.75                | 42.58                 | 44.53                 |
| **Idefics2-8B**     | **50.87**            | 41.69                | 46.87                 | **52.73**             |
| **Mantis-8B**       | 46.65                | **43.67**            | 41.80                 | 47.66                 |
| **Qwen-VL-12B**     | 32.26                | 27.54                | 48.83                 | 42.97                 |
| **Yi-VL-6B**        | -                    | -                    | **49.61**             | 41.41                 |
| **Yi-VL-34B**       | -                    | -                    | 52.73                 | 48.05                 |
| **GPT-4V**          | 78.92                | 69.23                | 63.67                 | 60.16                 |
| **GPT-4o**          | **86.35**            | **80.64**            | **72.66**             | **67.97**             |

### Models and results for the TextQA task

| Model               | Best Accuracy | Prompt |
|---------------------|:-------------:|:------:|
| Phi-3-medium        | 41.28         | 1      |
| Mistral-7B-instruct | 35.18         | 1      |
| Llama3-8B-Chinese   | 47.38         | 1      |
| YI-6B               | 25.53         | 3      |
| YI-34B              | 46.38         | 3      |
| Qwen2-7B-instruct   | 68.23         | 3      |
| GPT-4               | 60.99         | 1      |


## BibTeX Citation

```
@article{li2024foodieqa,
  title={FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture},
  author={Li, Wenyan and Zhang, Xinyu and Li, Jiaang and Peng, Qiwei and Tang, Raphael and Zhou, Li and Zhang, Weijia and Hu, Guimin and Yuan, Yifei and S{\o}gaard, Anders and others},
  journal={arXiv preprint arXiv:2406.11030},
  year={2024}
}
```