---
language:
- en
license: [mit]
annotations_creators:
- no-annotation
language_creators:
- machine-generated
pretty_name: GridTallyBench
size_categories:
- n<1k
source_datasets:
- original
task_categories:
- image-classification
- object-detection
task_ids:
- multi-class-image-classification
- object-counting
dataset_info:
  features:
  - name: block_pixel
    dtype: int32
  - name: grid_size
    dtype: int32
  - name: first_block
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: test
    num_examples: 960
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# GridTallyBench: Checkerboard Image Dataset for MLLM Benchmarking

## Overview

GridTallyBench is a collection of synthetic checkerboard images designed to test and benchmark Multi-modal Large Language Models (MLLMs) on tasks involving visual pattern recognition and counting. This dataset offers a controlled environment for evaluating model performance on basic visual tasks, particularly useful for assessing an MLLM's ability to count and describe simple geometric patterns.

## Dataset Details

- **Name**: GridTallyBench
- **Version**: 1.0.0
- **Task**: Image classification and object counting
- **Size**: 960 images
- **Format**: Parquet file containing image data and metadata
- **License**: MIT

## Content

The dataset consists of checkerboard images with the following variations:

- **Block sizes**: 1x1 to 24x24 pixels
- **Grid sizes**: 1x1 to 20x20 blocks
- **Starting colors**: Black-first and white-first patterns

Each image in the dataset is accompanied by metadata including:

- `block_pixel`: Size of each square in pixels (1 to 24)
- `grid_size`: Number of squares in each row/column (1 to 20)
- `first_block`: Color of the top-left square ('black' or 'white')
- `image`: Binary data of the PNG image

## Use Cases

This dataset is particularly useful for:

1. Testing MLLM's ability to count objects in images
2. Evaluating pattern recognition capabilities
3. Assessing color differentiation in simple scenarios
4. Benchmarking performance on controlled, synthetic images

## Loading the Dataset

To load and use this dataset with the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("MoonTideF/GridTallyBench")

# Access the first item
first_item = dataset['test'][0]
print(f"Block size: {first_item['block_pixel']}x{first_item['block_pixel']} pixels")
print(f"Grid size: {first_item['grid_size']}x{first_item['grid_size']} blocks")
print(f"First block color: {first_item['first_block']}")
dataset['test'][0]['image'].show()
```

## Dataset Creation

This dataset was generated using a custom Python script. The images are synthetic and do not contain any real-world content or personal information.

## Limitations

- The dataset is limited to black and white colors only
- Images are synthetic and may not represent real-world complexity
- The largest image size is 480x480 pixels (20x20 grid with 24x24 pixel blocks)

## Citation

If you use this dataset in your research, please cite it as follows:

```
@misc{gridtallybench,
  author = {MoonTideF},
  title = {GridTallyBench: Checkerboard Image Dataset for MLLM Benchmarking},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Datasets},
  howpublished = {\url{https://huggingface.co/datasets/MoonTideF/GridTallyBench}}
}
```

## Contact

For any questions or feedback regarding this dataset, please contact [Your Contact Information].

---