File size: 4,066 Bytes
a0c6910 f66c456 06cad10 f66c456 06cad10 f66c456 a0c6910 f66c456 f8e2d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | ---
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). |