| ---
|
| language:
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| - en
|
| pretty_name: ScrewCount
|
| license: cc-by-nc-nd-4.0
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| task_categories:
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| - object-detection
|
| - other
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| tags:
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| - object-counting
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| - few-shot-counting
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| - exemplar-based-counting
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| - text-guided-counting
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| - industrial-vision
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| - manufacturing
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| - density-estimation
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| - dense-object-counting
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| - small-object-counting
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| - screws
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| - 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=)
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|
|
|
|
| ## Supported Tasks and Leaderboards
|
| This dataset can be used for:
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|
|
| - **Few-shot object counting**
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| - **Exemplar-based counting**
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| - **Text-guided counting**
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| - **Dense small-object counting**
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| - **Point-supervised counting**
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| - **Density map regression**
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| - **Industrial object detection** (limited, depending on annotation usage)
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|
|
| ### Evaluation Metrics
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| The benchmark primarily uses:
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| - **MAE (Mean Absolute Error)**
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| - **RMSE (Root Mean Squared Error)**
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|
|
| These are standard metrics for object counting tasks.
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|
|
| ## Dataset Structure
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|
|
| Each sample consists of a high-resolution image of densely packed industrial objects, along with annotations that can support multiple counting paradigms.
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|
|
| A typical sample may include:
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|
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| - `image`: RGB image
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| - `points`: point annotations for object instances
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| - `density_map`: generated density map corresponding to point annotations
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| - `exemplar_boxes`: up to **6 exemplar bounding boxes** per image for few-shot counting
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| - `split`: train / val
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|
|
| ## Dataset Summary
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|
|
| ### Curation Rationale
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| 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:
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|
|
| - very small,
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| - densely packed,
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| - heavily overlapping,
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| - partially occluded,
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| - and visually similar.
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|
|
| 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.
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|
|
| ### Source Data
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| The dataset contains **high-resolution 3024×4032 images** of:
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| - **screws**
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| - **nuts**
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|
|
| The objects vary in:
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| - size: **small, medium, large**
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| - shape
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| - color: **white and black**
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|
|
| Images represent dense manufacturing-like scenes with object counts often in the **hundreds per image**.
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|
|
| ### Categories
|
|
|
| | Category | Training Samples | Test Samples |
|
| |----------|------------------|--------------|
|
| | Screw | 500 | 100 |
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| | Nuts | 500 | 100 |
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|
|
| ### Summary
|
|
|
| - **2 object categories**
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| - **1000 training images total**
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| - **200 test images total**
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| - **Image resolution:** 3024×4032
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| - **Objects per image:** typically a few hundred
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| - **Exemplars per image:** 6
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|
|
| ## Contributions
|
| Contributions, issue reports, and benchmark reproductions are welcome through the associated repository.
|
|
|
| ## Citation
|
| If you use this dataset, please cite:
|
|
|
| ```bibtex
|
| @conference{visapp26,
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| author={Farnaz Delirie and Afshin Dini and Amirmasoud Molaei and Leila Sadeghi},
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| title={ScrewCount: A Dataset and Benchmark for Exemplar Efficiency and Text-Guided Few-Shot Object Counting},
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| booktitle={Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP},
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| year={2026},
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| pages={306-313},
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| publisher={SciTePress},
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| organization={INSTICC},
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| doi={10.5220/0014236300004084},
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| isbn={978-989-758-804-4},
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| issn={2184-4321},
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| }
|
| ```
|
|
|
| ## License
|
|
|
| This model is licensed under the Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). |
