Upload README.md with huggingface_hub

README.md

@@ -22,13 +22,47 @@ task_categories:
 tags:
   - counting
   - object-counting
+  - benchmark
+  - multimodal
 ---
 
-# HoloCount
+# HoloCount: A Holistic Visual Counting Benchmark for MLLMs
 
-A counting QA benchmark dataset with 2480 samples across 20 fine-grained counting capabilities.
+<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>
 
-## Split Distribution
+## 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 |
 |-------|-------|
@@ -58,7 +92,7 @@ A counting QA benchmark dataset with 2480 samples across 20 fine-grained countin
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("imagefolder", data_dir="data")
+dataset = load_dataset("MM-MVR/HoloCount")
 ```
 
 ## Fields
@@ -70,3 +104,21 @@ ds = load_dataset("imagefolder", data_dir="data")
 - `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)