Datasets:
File size: 3,646 Bytes
94992a3 35b4c57 94992a3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
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].
---
|