Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+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.
+
+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.
+