README.md

---
tags:
- computer-vision
- visual-reasoning
- puzzle
language:
- en
license: cc-by-4.0
pretty_name: "CLEVR-Sudoku"
dataset_info:
  task_categories:
    - visual-reasoning
  task_ids:
    - puzzle-solving
---

# CLEVR-Sudoku

## Dataset Summary
CLEVR-Sudoku is a **challenging visual puzzle dataset** requiring both visual object perception and reasoning capabilities. Each sample contains:
- A partially filled Sudoku puzzle presented with **CLEVR-based** imagery.
- Separate “options” images that illustrate how specific **object properties** map to digits.
- Metadata specifying puzzle attributes and the ground-truth solution.

<img src="sudoku_example.png" alt="Sample CLEVR-Sudoku with Options" style="width:50%;" />

Designed to encourage **visual reasoning** and **pattern recognition**, this dataset provides 6 different subsets (no further train/val/test splits). Each subset contains 1,000 puzzles, leading to **6,000** total puzzles.

---

## Supported Tasks
- **Visual Reasoning**: Models must interpret compositional 3D object scenes to solve Sudoku logic.  
- **Pattern Recognition**: Identify how object attributes (shape, color, size, etc.) correlate with digit placements.

---

## Dataset Structure

### Data Instances

A typical data instance has the following structure:

```python
{
    "sudoku": 9x9 array of images or None,
    "options": 9x5 or 9x10 array of images,
    "attributes": dict that maps each digit to an attribute combination,
    "id": integer identifier for the puzzle,
    "solution": 9x9 array of integers (full sudoku)
}
```

## Data Splits / Subsets
There are 6 different subsets, 3 for CLEVR-Easy (CLEVR-Easy-K10, CLEVR-Easy-K30, CLEVR-Easy-K50) and 3 for CLEVR (CLEVR-4-K10, CLEVR-4-K30, CLEVR-4-K50). For CLEVR-Easy only color and shape are relevant for the digit mapping while for CLEVR also material and size relevant are. K indicates how many cells are empty in the sudoku, i.e. K10 means that there are 10 empty cells. Each subset contains 1,000 puzzles. Currently, there are no separate training/validation/test splits within each subset.

# Dataset Creation
## Curation Rationale

Goal: Combine the compositional “CLEVR”-style visual complexity with the logical constraints of Sudoku. This setup pushes models to perform both visual recognition (understanding shapes, colors, etc.) and abstract reasoning (solving Sudoku).

## Source Data
Synthetic Generation: The images are created in a CLEVR-like manner, programmatically generated with variations in shape, color, position, etc.
Sudoku Logic: Each puzzle is automatically generated and there exists exactly one solution to the puzzle.

## Annotations
Automatic Generation: Since the dataset is synthetic, the puzzle solutions are known programmatically. No human annotation is required for the puzzle solutions.
Attributes: Each digit (1–9) is associated with one or more visual properties (e.g., color = "red", shape = "cube"). These are also generated systematically.

## Personal and Sensitive Information
None: The dataset is purely synthetic, containing no personal or demographic data.

# Usage
## Loading the Dataset

Example usage:
```python
from datasets import load_dataset

dataset = load_dataset("AIML-TUDA/CLEVR-Sudoku", "CLEVR-Easy-K10")
print(dataset[0])
```

The second argument ("CLEVR-Easy-K10", "CLEVR-Easy-K30", etc.) corresponds to the 6 different subsets.
Each subset has 1,000 puzzles.

# Citation

If you use or reference this dataset in your work, please cite the following:
```
@article{stammer2024neural,
  title={Neural Concept Binder},
  author={Stammer, Wolfgang and W{\"u}st, Antonia and Steinmann, David and Kersting, Kristian},
  journal={Advances in Neural Information Processing Systems},
  year={2024}
}
```