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license: mit
pretty_name: MMB Counterfactual Dataset
task_categories:
- visual-question-answering
- multiple-choice
language:
- en
tags:
- vision
- language
- multimodal
- counterfactual
- question-answering
- synthetic
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: original_image
dtype: image
- name: counterfactual1_image
dtype: image
- name: counterfactual2_image
dtype: image
- name: counterfactual1_type
dtype: string
- name: counterfactual2_type
dtype: string
- name: counterfactual1_description
dtype: string
- name: counterfactual2_description
dtype: string
- name: original_question
dtype: string
- name: counterfactual1_question
dtype: string
- name: counterfactual2_question
dtype: string
- name: original_question_difficulty
dtype: string
- name: counterfactual1_question_difficulty
dtype: string
- name: counterfactual2_question_difficulty
dtype: string
- name: original_image_answer_to_original_question
dtype: string
- name: original_image_answer_to_cf1_question
dtype: string
- name: original_image_answer_to_cf2_question
dtype: string
- name: cf1_image_answer_to_original_question
dtype: string
- name: cf1_image_answer_to_cf1_question
dtype: string
- name: cf1_image_answer_to_cf2_question
dtype: string
- name: cf2_image_answer_to_original_question
dtype: string
- name: cf2_image_answer_to_cf1_question
dtype: string
- name: cf2_image_answer_to_cf2_question
dtype: string
splits:
- name: train
num_bytes: 29666931
num_examples: 100
download_size: 29653393
dataset_size: 29666931
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# MMB Counterfactual Dataset
A counterfactual VQA dataset constructed using the CLEVR blender assets to procedurally generate both negative and normal counter factual VQA images and questions for the Multimodal Benchmark paper.
## Dataset Structure
This repository contains counterfactual visual question answering data with:
- **Original images** and **counterfactual variants** (modifications to test reasoning)
- **Questions** for each image variant
- **Answer matrices** showing how each image answers each question (9 values per scene: 3 images × 3 questions)
### Loading from Python
After pushing this repository to the Hub, load it with:
```python
from datasets import load_dataset
ds = load_dataset("scholo/MMB_dataset", split="train")
print(ds[0])
```
No `trust_remote_code=True` needed since we use standard Parquet format!
## Directory Structure
```
MMB-Dataset/
├── README.md # This file
├── .gitattributes # Git LFS configuration for images
├── data/ # Dataset files (Parquet format)
│ └── train.parquet # Main dataset file
├── Dataset/ # Current dataset run
│ ├── images/ # All PNG images (referenced by Parquet)
│ ├── scenes/ # JSON scene descriptions (reference)
│ ├── image_mapping_with_questions.csv # Original CSV (source)
│ ├── checkpoint.json # Run metadata
│ └── run_metadata.json # Run metadata
```
## License
MIT
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