---
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- counting
- spatial-reasoning
- object-counting
- vqa
- benchmark
- vision-language
- multimodal
- evaluation
size_categories:
- 10K<n<100K
pretty_name: "VisQuant: Can Your VLM Actually Count?"
---

<div align="center">

# 🔢 VisQuant — The Object Counting Benchmark for Vision-Language Models

### *LLaVA-7B scores 30%. GPT-4V struggles. Can yours do better?*

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset Size](https://img.shields.io/badge/Images-67%2C093-blue)](https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1)
[![QA Pairs](https://img.shields.io/badge/QA%20Pairs-369%2C612-green)](https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1)
[![Object Types](https://img.shields.io/badge/Object%20Types-88-orange)](https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1)

</div>

---

## 💡 Why VisQuant Exists

Counting is supposedly trivial. You learned it at age 3.

Your frontier VLM didn't.

We tested LLaVA-7B on images with 1–20 everyday objects and **it scored 30% overall** — worse on hard scenes, barely better than random on expert-level images. VisQuant is a structured, difficulty-graded benchmark with **zero label noise** (counts are set by code, not annotators) designed to expose exactly where and why vision-language models fail at quantitative reasoning.

Every label in this dataset is **mathematically guaranteed correct.** No crowdworkers. No ambiguity. No hallucinated annotations.

---

## 📊 Dataset at a Glance

| | |
|---|---|
| 🖼️ **Total Images** | 67,093 |
| ❓ **QA Pairs** | 369,612 |
| 🏷️ **Object Categories** | 88 everyday objects |
| 🎯 **Count Range** | 1 – 20 objects per image |
| 📐 **Difficulty Levels** | Easy → Medium → Hard → Expert |
| ✅ **Label Accuracy** | 100% (programmatically guaranteed) |
| 📄 **License** | CC BY 4.0 |

---

## 🏆 Leaderboard

> **Submit your model's results** — open a [discussion](https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1/discussions) with your scores and we'll add you to the board.

| Rank | Model | Size | Easy | Medium | Hard | Expert | **Overall** |
|------|-------|------|------|--------|------|--------|-------------|
| 🥇 | *Submit yours!* | — | — | — | — | — | — |
| — | LLaVA-7B | 7B | **45.0%** | **28.3%** | **28.3%** | **16.7%** | **29.6%** |
| — | moondream | 1.8B | 26.7% | 18.3% | 13.3% | 5.0% | 15.8% |
| — | Random chance (est.) | — | ~25% | ~20% | ~15% | ~12% | **~18%** |

*Metrics: exact-match accuracy on `exact_count` questions. 240 images tested per model (60/difficulty), temperature=0. LLaVA-13B results coming soon.*

---

## 🎯 Difficulty Breakdown

```
Easy    ████░░░░░░░░░░░░  7,183 images  — 1–3 objects, simple spatial layouts
Medium  ████████████░░░░  25,857 images — 2–6 objects, mixed arrangements, 1 type
Hard    ████████████░░░░  21,390 images — 3–12 objects, dense scenes, 2–3 types  
Expert  ████████░░░░░░░░  12,663 images — 5–20 objects, multi-type chaos
```

**Easy is a warm-up. Expert is a wall.**

---

## ❓ Question Types

Each image comes with up to 6 question types:

| Type | Example | Count |
|------|---------|-------|
| `exact_count` | *"How many feathers are in this image?"* → `"7"` | 140,894 |
| `threshold` | *"Are there more than 5 rocks?"* → `"Yes"` | 67,093 |
| `comparison` | *"Are there more shells or buttons?"* → `"Shells"` | 50,620 |
| `total_count` | *"How many objects are there in total?"* → `"11"` | 46,403 |
| `spatial` | *"How are the objects arranged?"* | 48,129 |
| `background` | *"What surface are the objects on?"* | 16,473 |

---

## 🚀 Quick Start

```python
from datasets import load_dataset

ds = load_dataset("Anas-Mohiuddin-Syed/visquant-v1")
```

Or load a specific difficulty split:

```python
import json

# Stream easy examples only
with open("data/dataset.jsonl") as f:
    easy = [json.loads(l) for l in f 
            if json.loads(l)[list(json.loads(l).keys())[0]]["difficulty"] == "easy"]
```

**Evaluate your model:**

```python
from PIL import Image

sample_id = "visquant_00101"
image = Image.open(f"data/images/{sample_id}.png")

# Ask your VLM the first question
entry  = ds[sample_id]
q      = entry["questions"][0]["q"]   # "How many twigs are in this image?"
gt     = entry["questions"][0]["a"]   # "4"

prediction = your_vlm(image, q)
correct = prediction.strip() == gt
```

---

## 🗂️ Data Format

Each record in `data/dataset.jsonl`:

```json
{
  "visquant_00101": {
    "objects": { "twig": 4 },
    "total_objects": 4,
    "difficulty": "medium",
    "background": "a marble surface",
    "spatial_info": "exactly 4 twigs arranged in a semi-circle on a marble surface",
    "questions": [
      { "type": "exact_count", "q": "How many twigs are in this image?",       "a": "4"   },
      { "type": "threshold",   "q": "Are there more than 3 twigs?",             "a": "Yes" },
      { "type": "spatial",     "q": "How are the objects arranged?",            "a": "Twigs arranged in a semi-circle" },
      { "type": "background",  "q": "What surface are the objects on?",         "a": "Marble surface" }
    ]
  }
}
```

Images live at `data/images/{image_id}.png` — 512×512px PNG, transparent-composited sprites on photorealistic backgrounds.

---

## 🔬 How Images Are Built (No Annotation Needed)

```
Twemoji SVG sprites  ──►  PIL compositor  ──►  Exact N objects placed
     (88 types)                │                 at computed positions
                               ▼
                    40+ background textures
                    (marble, wood, concrete…)
                               │
                               ▼
                    Labels written by the same
                    code that placed the objects
                    ──► Zero annotation error
```

Every count label equals the loop variable `N` that placed those objects. There is no human in the loop, and therefore no human error.

---

## 📦 What's Inside

```
visquant-v1/
├── data/
│   ├── dataset.jsonl       # 67,093 records, 369,612 QA pairs
│   └── images/
│       ├── visquant_00101.png
│       ├── visquant_00102.png
│       └── ... (67,093 total, 512×512px)
└── README.md
```

---

## 🧩 Object Categories (88 total)

`feather` · `shell` · `button` · `leaf` · `acorn` · `ring` · `twig` · `bead` · `earring` · `bracelet` · `rock` · `pinecone` · `sock` · `hair clip` · `ribbon` · `bow` · `domino` · `remote control` · `flower` · `pebble` · `marble` · `coin` · `candle` · `eraser` · `chess piece` · `apple` · `banana` · `cup` · `dice` · `USB drive` · `pencil` · `stapler` · `binder clip` · `plate` · `mouse` · `cylinder` · `glass` · `puzzle piece` · *and 50 more…*

---

## 📉 What the Numbers Reveal

Running LLaVA-7B across 240 VisQuant images surfaces a crisp, reproducible pattern:

**The subitizing cliff at 3:**
```
1 object  → 75.0% accuracy   ████████████████████████████████████████
2 objects → 81.3% accuracy   ████████████████████████████████████████
─────────────────────────────────── cliff ───────────────────────────
3 objects → 18.4% accuracy   ████████
4 objects → 46.2% accuracy   ███████████████████
5 objects → 25.0% accuracy   ██████████
6 objects → 15.0% accuracy   ██████
7 objects → 47.4% accuracy   ███████████████████  ← grid layout boost
8+ objects → collapses toward 0%
12+ objects → 0.0% (absolute failure)
```

**Systematic undercounting bias:** 20% of predictions are off by −1, vs 15.8% off by +1. The model consistently underestimates.

**Expert ceiling:** 16.7% — barely above a random guess for high-count, multi-type scenes.

VisQuant isolates these failure modes in a controlled, reproducible benchmark so you can **measure, not guess** how your model handles counting.

---

## 📋 Reproduce / Extend

The full generation pipeline is available. To regenerate or extend the dataset:

1. Twemoji sprites → rasterized PNG (96×96px)
2. PIL compositor places exactly N sprites per layout type
3. Backgrounds: 40+ PIL-generated textures
4. QA pairs generated rule-based from metadata
5. Images filtered to remove layout overflow (stacked/row >4 objects)

---

## 🔖 Citation

If you use VisQuant in your research, please cite:

```bibtex
@dataset{visquant_v1_2025,
  title     = {VisQuant: A Programmatic Object Counting Benchmark for Vision-Language Models},
  author    = {Syed, Anas Mohiuddin},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1},
  license   = {CC-BY-4.0},
  note      = {67,093 images · 369,612 QA pairs · 88 object categories}
}
```

---

## 📬 Contact & Contributions

- **Submit benchmark results**: Open a [Discussion](https://huggingface.co/datasets/Anas-Mohiuddin-Syed/visquant-v1/discussions)
- **Report issues**: Found a bad image? Open an issue in Discussions
- **Extend the dataset**: PRs welcome — add new object types, layouts, or difficulty tiers

---

<div align="center">
<sub>Built with 🔢 and zero annotation error · CC BY 4.0 · 2025</sub>
</div>