README.md

---
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- n<1K
task_categories:
- image-to-text
- visual-question-answering
- question-answering
dataset_info:
  features:
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: options
    sequence: string
  - name: answer
    dtype: string
  - name: category
    dtype: string
  - name: id
    dtype: int64
  - name: source
    dtype: string
  - name: url
    dtype: string
  splits:
  - name: train
    num_bytes: 145663.0
    num_examples: 4
  - name: test
    num_bytes: 15809151.0
    num_examples: 435
  - name: select
    num_bytes: 708434.0
    num_examples: 11
  download_size: 15059133
  dataset_size: 16663248.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: select
    path: data/select-*
---
# IllusionVQA: Optical Illusion Dataset

[Project Page](https://illusionvqa.github.io/) | 
[Paper](https://arxiv.org/abs/2403.15952) |
[Github](https://github.com/csebuetnlp/IllusionVQA/)

## TL;DR
IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively.

## Usage
```python
from datasets import load_dataset
import base64
from openai import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"

def encode_image(pil_image):
    temp_name = "temp.jpg"
    pil_image.save(temp_name)
    with open(temp_name, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def construct_mcq(options, correct_option):
    correct_option_letter = None
    i = "a"
    mcq = ""
    for option in options:
        if option == correct_option:
            correct_option_letter = i
        mcq += f"{i}. {option}\n"
        i = chr(ord(i) + 1)
    mcq = mcq[:-1]
    return mcq, correct_option_letter

def add_row(content, data, i, with_answer=False):  
    mcq, correct_option_letter = construct_mcq(data["options"], data["answer"])
    content.append({ "type": "text",
            "text": "Image " + str(i) + ": " + data["question"] + "\n" + mcq })
    content.append({ "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{encode_image(data['image'])}",
                "detail": "low"}})
    if with_answer:
        content.append({"type": "text", "text": "Answer {}: ".format(i) + correct_option_letter})
    else:
        content.append({"type": "text", "text": "Answer {}: ".format(i), })
    return content

dataset = load_dataset("csebuetnlp/illusionVQA-Comprehension")
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

content = [{
        "type": "text",
        "text": "You'll be given an image, an instruction and some choices. You have to select the correct one. Do not explain your reasoning. Answer with the option's letter from the given choices directly. Here are a few examples:",
    }]

### Add a few examples
for i, data in enumerate(dataset["train"], 1):
    content = add_row(content, data, i, with_answer=True)

content.append({"type": "text", "text": "Now you try it!",})

next_idx = i + 1

### Add the test data
test_data = dataset["test"][0]
content_t = add_row(content.copy(), test_data, next_idx, with_answer=False)

### Get the answer from GPT-4
response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[{"role": "user", "content": content_t,}],
    max_tokens=5,
)
gpt4_answer = response.choices[0].message.content
print(gpt4_answer)
```

## License
This dataset is made available for non-commercial research purposes only under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). The dataset may not be used for training models. The dataset contains images collected from the internet. While permission has been obtained from some of the images' creators, permission has not yet been received from all creators. If you believe any image in this dataset is used without proper permission and you are the copyright holder, please email <a href="mailto:[email protected]">Haz Sameen Shahgir</a> to request the removal of the image from the dataset.

The dataset creator makes no representations or warranties regarding the copyright status of the images in the dataset. The dataset creator shall not be held liable for any unauthorized use of copyrighted material that may be contained in the dataset.

You agree to the terms and conditions specified in this license by downloading or using this dataset. If you do not agree with these terms, do not download or use the dataset.

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>


### Citation
```
@article{shahgir2024illusionvqa,
      title={IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models}, 
      author={Haz Sameen Shahgir and Khondker Salman Sayeed and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yue Dong and Rifat Shahriyar},
      year={2024},
      url={https://arxiv.org/abs/2403.15952},
}
```