Datasets:

Kimhi
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hardness_data_mix

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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

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

Labeling Strategy

Each sample was labeled based on:

Resolution Effectiveness Analysis: Performance of VLMs at each resolution level
Question Complexity: Type and difficulty of the question
Image Content: Visual elements requiring high resolution
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

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

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

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:

Resolution Selection Research
- Training classifiers to predict required resolution
- Understanding resolution vs. accuracy tradeoffs
Efficient VLM Inference
- Optimizing multi-resolution inference
- Reducing computational costs
- Adaptive resolution selection
Model Benchmarking
- Evaluating VLM robustness at different resolutions
- Comparing resolution handling strategies
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
Approach: Lightweight classifier on frozen features
Use Case: Fast, on-device inference

Granite-Docling Resolution Gate

Model: 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

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

Citation

If you use this dataset, please cite:

@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},
}