feat data

README.md

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 ---
-license: mit
+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].
+
 ---

data/test-000000.parquet

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