metadata

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:

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

Loading the Dataset

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

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].