---
language:
- en
pretty_name: ScrewCount
license: cc-by-nc-nd-4.0
task_categories:
- object-detection
- other
tags:
- object-counting
- few-shot-counting
- exemplar-based-counting
- text-guided-counting
- industrial-vision
- manufacturing
- density-estimation
- dense-object-counting
- small-object-counting
- screws
- nuts
---

# ScrewCount
**ScrewCount** is a dataset for **dense small-object counting in industrial inspection settings**, designed to benchmark both **exemplar-based few-shot counting** and **text-guided object counting**. It focuses on challenging manufacturing scenarios with **small, overlapping, densely packed, and visually similar objects**, specifically **screws** and **nuts**.

The dataset was introduced in the paper: [**ScrewCount: A Dataset and Benchmark for Exemplar Efficiency and Text-Guided Few-Shot Object Counting**](https://www.scitepress.org/PublicationsDetail.aspx?ID=vsx2VblEuWQ=)


## Supported Tasks and Leaderboards
This dataset can be used for:

- **Few-shot object counting**
- **Exemplar-based counting**
- **Text-guided counting**
- **Dense small-object counting**
- **Point-supervised counting**
- **Density map regression**
- **Industrial object detection** (limited, depending on annotation usage)

### Evaluation Metrics
The benchmark primarily uses:
- **MAE (Mean Absolute Error)**
- **RMSE (Root Mean Squared Error)**

These are standard metrics for object counting tasks.

## Dataset Structure

Each sample consists of a high-resolution image of densely packed industrial objects, along with annotations that can support multiple counting paradigms.

A typical sample may include:

- `image`: RGB image
- `points`: point annotations for object instances
- `density_map`: generated density map corresponding to point annotations
- `exemplar_boxes`: up to **6 exemplar bounding boxes** per image for few-shot counting
- `split`: train / val

## Dataset Summary

### Curation Rationale
Most existing counting datasets focus on crowds, cells, or natural scenes. These benchmarks do not fully capture the challenges of **industrial inspection**, where objects are often:

- very small,
- densely packed,
- heavily overlapping,
- partially occluded,
- and visually similar.

ScrewCount was created to address this gap and provide a benchmark tailored to **real-world industrial counting scenarios** where annotation is expensive and few-shot methods are especially relevant.

### Source Data
The dataset contains **high-resolution 3024×4032 images** of:
- **screws**
- **nuts**

The objects vary in:
- size: **small, medium, large**
- shape
- color: **white and black**

Images represent dense manufacturing-like scenes with object counts often in the **hundreds per image**.

### Categories

| Category | Training Samples | Test Samples |
|----------|------------------|--------------|
| Screw    | 500              | 100          |
| Nuts     | 500              | 100          |

### Summary

- **2 object categories**
- **1000 training images total**
- **200 test images total**
- **Image resolution:** 3024×4032
- **Objects per image:** typically a few hundred
- **Exemplars per image:** 6

## Contributions
Contributions, issue reports, and benchmark reproductions are welcome through the associated repository.

## Citation
If you use this dataset, please cite:

```bibtex
@conference{visapp26,
author={Farnaz Delirie and Afshin Dini and Amirmasoud Molaei and Leila Sadeghi},
title={ScrewCount: A Dataset and Benchmark for Exemplar Efficiency and Text-Guided Few-Shot Object Counting},
booktitle={Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP},
year={2026},
pages={306-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014236300004084},
isbn={978-989-758-804-4},
issn={2184-4321},
}
```

## License

This model is licensed under the Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).