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---
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language:
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|
- en
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|
license: [mit]
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annotations_creators:
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- no-annotation
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|
language_creators:
|
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- machine-generated
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|
pretty_name: GridTallyBench
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|
size_categories:
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|
- n<1k
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|
source_datasets:
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- original
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|
task_categories:
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- image-classification
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- object-detection
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task_ids:
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- multi-class-image-classification
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|
- object-counting
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|
dataset_info:
|
|
features:
|
|
- name: block_pixel
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|
dtype: int32
|
|
- name: grid_size
|
|
dtype: int32
|
|
- name: first_block
|
|
dtype: string
|
|
- name: image
|
|
dtype: image
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|
splits:
|
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- name: test
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num_examples: 960
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configs:
|
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- config_name: default
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data_files:
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- split: test
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path: data/test-*
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---
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# GridTallyBench: Checkerboard Image Dataset for MLLM Benchmarking
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## Overview
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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.
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## Dataset Details
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- **Name**: GridTallyBench
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- **Version**: 1.0.0
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- **Task**: Image classification and object counting
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- **Size**: 960 images
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- **Format**: Parquet file containing image data and metadata
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- **License**: MIT
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## Content
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The dataset consists of checkerboard images with the following variations:
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- **Block sizes**: 1x1 to 24x24 pixels
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- **Grid sizes**: 1x1 to 20x20 blocks
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- **Starting colors**: Black-first and white-first patterns
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Each image in the dataset is accompanied by metadata including:
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- `block_pixel`: Size of each square in pixels (1 to 24)
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- `grid_size`: Number of squares in each row/column (1 to 20)
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- `first_block`: Color of the top-left square ('black' or 'white')
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- `image`: Binary data of the PNG image
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## Use Cases
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This dataset is particularly useful for:
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1. Testing MLLM's ability to count objects in images
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2. Evaluating pattern recognition capabilities
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3. Assessing color differentiation in simple scenarios
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4. Benchmarking performance on controlled, synthetic images
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## Loading the Dataset
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To load and use this dataset with the Hugging Face `datasets` library:
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```python
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from datasets import load_dataset
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dataset = load_dataset("MoonTideF/GridTallyBench")
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# Access the first item
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first_item = dataset['test'][0]
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print(f"Block size: {first_item['block_pixel']}x{first_item['block_pixel']} pixels")
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print(f"Grid size: {first_item['grid_size']}x{first_item['grid_size']} blocks")
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print(f"First block color: {first_item['first_block']}")
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dataset['test'][0]['image'].show()
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```
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## Dataset Creation
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This dataset was generated using a custom Python script. The images are synthetic and do not contain any real-world content or personal information.
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## Limitations
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- The dataset is limited to black and white colors only
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- Images are synthetic and may not represent real-world complexity
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- The largest image size is 480x480 pixels (20x20 grid with 24x24 pixel blocks)
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## Citation
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If you use this dataset in your research, please cite it as follows:
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```
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@misc{gridtallybench,
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author = {MoonTideF},
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title = {GridTallyBench: Checkerboard Image Dataset for MLLM Benchmarking},
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year = {2024},
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publisher = {Hugging Face},
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journal = {Hugging Face Datasets},
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howpublished = {\url{https://huggingface.co/datasets/MoonTideF/GridTallyBench}}
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}
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```
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## Contact
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For any questions or feedback regarding this dataset, please contact [Your Contact Information].
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---
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