README.md

---
dataset_info:
  features:
    - name: image
      dtype: image
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: split
      dtype: string
    - name: sample_id
      dtype: string
    - name: image_height
      dtype: int32
    - name: image_width
      dtype: int32
  num_examples: 2480
license: cc-by-4.0
task_categories:
  - visual-question-answering
tags:
  - counting
  - object-counting
  - benchmark
  - multimodal
---

# HoloCount: A Holistic Visual Counting Benchmark for MLLMs

<p align="center">
  <a href="TODO_ARXIV_LINK">
    <img src="https://img.shields.io/badge/arXiv-Paper-red" alt="arXiv">
  </a>
  <a href="https://huggingface.co/datasets/MM-MVR/HoloCount">
    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow" alt="HuggingFace">
  </a>
  <a href="https://kinredon.github.io/HoloCount/">
    <img src="https://img.shields.io/badge/Project-Page-blue" alt="Project Page">
  </a>
  <a href="https://github.com/kinredon/HoloCount">
    <img src="https://img.shields.io/badge/GitHub-Code-black" alt="GitHub">
  </a>
</p>

## Abstract

Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning. While Multimodal Large Language Models (MLLMs) have achieved remarkable success in qualitative scene understanding, their quantitative precision remains a significant bottleneck, often characterized by persistent numerical hallucinations. Existing counting benchmarks primarily focus on basic perception in simplified contexts, failing to capture the complex failure modes that emerge under logical constraints or adversarial conditions. To address these limitations, we introduce HoloCount, a holistic and diagnostically rich benchmark structured around a three-level hierarchical taxonomy. HoloCount evaluates MLLMs across: (1) Semantic Counting, focusing on atomic and property-based enumeration; (2) Analytical Counting, assessing logical composition through spatial and set-based reasoning; and (3) Robustness Testing, probing model integrity against adverse scenarios and grounded counter-priors, such as high-density scenes and linguistic biases. Through an exhaustive evaluation of over 20 state-of-the-art MLLMs, we reveal a critical performance gap: even top-tier models degrade significantly as tasks transition from perception to complex analytical reasoning and adverse scenarios. Our findings provide a systematic landscape of current MLLM counting capabilities and offer a roadmap for developing more grounded and reliable multimodal systems.

## Overview

- **2,480** QA pairs
- **20** fine-grained subsets
- **1,481** visual concepts
- **30+** evaluated MLLMs

## Taxonomy

The benchmark features a three-level hierarchical taxonomy:

- **Semantic Counting** — Atomic and property-based enumeration (6 subsets)
- **Analytical Counting** — Logical composition through spatial and set-based reasoning (7 subsets)
- **Robustness Testing** — Model integrity against adverse scenarios (7 subsets)

## Dataset Composition

| Split | Count |
|-------|-------|
| Action & State Attribution | 102 |
| Aerial Perspective Shift | 109 |
| Atomic Counting | 182 |
| Categorical Cardinality | 101 |
| Chromatic Attribution | 202 |
| Complementary Exclusion | 101 |
| Coordinate-Prompt Region Grounding | 84 |
| Differential Comparison | 100 |
| General Semantic Attribution | 101 |
| High-Density Enumeration | 100 |
| Joint-Set Aggregation | 100 |
| Linguistic Prior Conflict | 163 |
| Material Attribution | 101 |
| Null-Target Prompting | 250 |
| Partial Object Occlusion | 103 |
| Relative Canonical Orientation | 78 |
| Scale Attribution | 100 |
| Small-Scale Enumeration | 101 |
| Visual Distractor Illusion | 200 |
| Visual-Prompt Region Grounding | 102 |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("MM-MVR/HoloCount")
```

## Fields

- `image`: the image file
- `question`: counting question
- `answer`: ground truth count (string)
- `split`: capability category
- `sample_id`: unique identifier
- `image_height`: original image height in pixels
- `image_width`: original image width in pixels

## Citation

```bibtex
@article{deng2026holocount,
  title={HoloCount: A Holistic Visual Counting Benchmark for MLLMs},
  author={Deng, Jinhong and Qiao, Limeng and Wan, Guanglu},
  year={2026}
}
```

## License

This dataset is released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).

## Contact

- Jinhong Deng (dengjinhong@meituan.com)