---
license: cc-by-4.0
task_categories:
  - image-classification
  - visual-question-answering
tags:
  - document-understanding
  - resolution-selection
  - multi-resolution
  - vision-language
  - document-vqa
language:
  - en
size_categories:
  - 100K<n<1M
source_datasets:
  - textvqa
  - docvqa
  - chartqa
  - infographicvqa
  - hme100k
---

# Hardness Data Mix - Resolution Sufficiency Dataset

A large-scale dataset of document images with labels indicating the minimum resolution required to accurately answer questions about those documents.

## Dataset Description

This dataset contains 81,924 document image-question pairs labeled with resolution sufficiency information. Each sample is annotated with a "hardness" label indicating the minimum resolution level needed to answer questions about that document accurately.

### Dataset Summary

- **Total Samples**: 81,924
- **Image Formats**: JPEG, PNG
- **Resolutions Available**: Low (384×384), Medium (512×512), High (768×768+)
- **Features**: Multi-path image storage (low, mid, high resolution versions)
- **Languages**: English
- **Domains**: Mixed document types (text, charts, infographics, documents)

### Key Statistics

```
Class Distribution:
  Class 0 (Low res sufficient):     38,537 samples (47.0%)
  Class 1 (Medium res needed):      19,929 samples (24.3%)
  Class 2 (High res required):      23,458 samples (28.6%)

Total Size: ~4.92 MB (parquet format)
Average Sample Size: ~60 KB
```

## Dataset Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique sample identifier |
| `question` | string | Question about the document |
| `low_path` | string | Path to low-resolution image (384×384) |
| `mid_path` | string | Path to medium-resolution image (512×512) |
| `high_path` | string | Path to high-resolution image (768×768+) |
| `hard` | int | Label: 0=low res enough, 1=medium needed, 2=high needed |

## Data Sources

The dataset is a curated mix from multiple established VQA and document understanding benchmarks:

### Source Datasets

1. **TextVQA** (~25%)
   - Text-rich images from scenes and documents
   - Focus on reading and understanding text in images
   
2. **DocVQA** (~30%)
   - Document-focused question answering
   - Scanned document images
   
3. **ChartQA** (~15%)
   - Charts and figure understanding
   - Questions about data visualization
   
4. **InfographicVQA** (~20%)
   - Complex infographic understanding
   - Multi-element visual reasoning
   
5. **HME100K** (~10%)
   - Handwritten mathematical expressions
   - Document analysis

## Labeling Strategy

Each sample was labeled based on:

1. **Resolution Effectiveness Analysis**: Performance of VLMs at each resolution level
2. **Question Complexity**: Type and difficulty of the question
3. **Image Content**: Visual elements requiring high resolution
4. **Error Analysis**: Where models fail at lower resolutions

### Class Definitions

- **Class 0 (Low - 384×384)**: VLM achieves ≥95% accuracy at low resolution
- **Class 1 (Medium - 512×512)**: VLM needs medium resolution for adequate performance
- **Class 2 (High - 768×768+)**: VLM requires high resolution for accurate answers

## Usage

### Loading with Hugging Face Datasets

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("Kimhi/hardness_data_mix")

# Access splits
train_split = dataset["train"]  # If available
full_data = dataset["hardness_data_mix"]

# Display sample
sample = full_data[0]
print(sample)
```

### Loading with Pandas

```python
import pandas as pd

# Load parquet file
df = pd.read_parquet("hardness_data_mix.parquet")

# Inspect
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(df.head())

# Get class distribution
print(df['hard'].value_counts().sort_index())
```

### Use in Training

```python
import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_parquet("hardness_data_mix.parquet")

# Split
train_df, val_df = train_test_split(
    df,
    test_size=0.1,
    stratify=df['hard'],
    random_state=42
)

# Use with training scripts
train_df.to_parquet("train_data.parquet")
val_df.to_parquet("val_data.parquet")
```

## Dataset Applications

This dataset is designed for:

1. **Resolution Selection Research**
   - Training classifiers to predict required resolution
   - Understanding resolution vs. accuracy tradeoffs
   
2. **Efficient VLM Inference**
   - Optimizing multi-resolution inference
   - Reducing computational costs
   - Adaptive resolution selection
   
3. **Model Benchmarking**
   - Evaluating VLM robustness at different resolutions
   - Comparing resolution handling strategies
   
4. **Academic Research**
   - Understanding visual information requirements
   - Document understanding challenges

## Related Models

This dataset is used to train the CARES (Context-Aware Resolution Selection) models:

### SmolVLM Resolution Gate
- **Model**: [Kimhi/smolvlm-res-gate](https://huggingface.co/Kimhi/smolvlm-res-gate)
- **Approach**: Lightweight classifier on frozen features
- **Use Case**: Fast, on-device inference

### Granite-Docling Resolution Gate
- **Model**: [Kimhi/granite-docling-res-gate-lora](https://huggingface.co/Kimhi/granite-docling-res-gate-lora)
- **Approach**: Autoregressive SFT with LoRA
- **Use Case**: Production deployment

## Ethical Considerations

### Intended Use
- Academic research and development
- Industrial document understanding applications
- Model benchmarking and evaluation
- Responsible AI research

### Potential Risks
- Dataset reflects biases in source datasets
- May not generalize to specific document domains
- Quality varies based on document type
- Labels are proxy measures of resolution necessity

### Mitigation
- Stratified sampling ensures class balance
- Multi-source composition reduces single-domain bias
- Regular validation against real-world tasks
- Transparent documentation of limitations

## Limitations

1. **Domain Specificity**: Primarily document-focused
2. **Language**: Primarily English
3. **Quality Variation**: Mixed-quality source data
4. **Labeling**: Labels based on model performance, not human judgment
5. **Representation**: May not include all document types equally

## Citation

If you use this dataset, please cite:

```bibtex
@misc{kimhi2025carescontextawareresolutionselector,
      title={CARES: Context-Aware Resolution Selector for VLMs}, 
      author={Moshe Kimhi and Nimrod Shabtay and Raja Giryes and Chaim Baskin and Eli Schwartz},
      year={2025},
      eprint={2510.19496},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
}
```

## Acknowledgements

- Dataset sources: TextVQA, DocVQA, ChartQA, InfographicVQA, HME100K communities
- Infrastructure: Hugging Face Hub
- Hosting: Hugging Face Datasets

## License

CC BY 4.0 - See LICENSE for details

## Contact

For questions about this dataset, please open an issue on the [CARES GitHub repository](https://github.com/mkimhi/CARES).

---

**Dataset Version**: 1.0
**Last Updated**: 2024
**Recommended Citation**: hardness_data_mix, Kimhi (2024)