language:
- en
size_categories:
- n<1K
task_categories:
- image-to-text
- visual-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: reasoning
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 50339
num_examples: 4
- name: test
num_bytes: 24579079
num_examples: 1000
download_size: 24495650
dataset_size: 24629418
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: cc-by-nc-sa-4.0
IllusionVQA: Optical Illusion Dataset
Project Page | Paper | Github
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
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). 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 Haz Sameen Shahgir 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.
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},
}