SortScrews: A Dataset and Baseline for Real-time Screw Classification

2026-03-13

AI Summary

This study addresses the lack of publicly available datasets for single-screw classification in industrial automation, which has hindered the development of automated sorting systems. To bridge this gap, the authors introduce a high-quality screw image dataset captured under controlled conditions, encompassing six screw categories and a background class, with systematic variations in lighting and viewing angles. A low-cost hardware setup and reusable data acquisition scripts are also open-sourced to facilitate reproducibility. Transfer learning models based on EfficientNet-B0 and ResNet-18 achieve high classification accuracy even with limited training data, demonstrating the significant performance gains enabled by standardized image acquisition. All code, datasets, and training pipelines are publicly released to support further customization and extension.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Automatic identification of screw types is important for industrial automation, robotics, and inventory management. However, publicly available datasets for screw classification are scarce, particularly for controlled single-object scenarios commonly encountered in automated sorting systems. In this work, we introduce $\textbf{SortScrews}$, a dataset for casewise visual classification of screws. The dataset contains 560 RGB images at $512\times512$ resolution covering six screw types and a background class. Images are captured using a standardized acquisition setup and include mild variations in lighting and camera perspective across four capture settings. To facilitate reproducible research and dataset expansion, we also provide a reusable data collection script that allows users to easily construct similar datasets for custom hardware components using inexpensive camera setups. We establish baseline results using transfer learning with EfficientNet-B0 and ResNet-18 classifiers pretrained on ImageNet. In addition, we conduct a well-explored failure analysis. Despite the limited dataset size, these lightweight models achieve strong classification accuracy, demonstrating that controlled acquisition conditions enable effective learning even with relatively small datasets. The dataset, collection pipeline, and baseline training code are publicly available at https://github.com/ATATC/SortScrews.

Problem

• screw classification
• industrial automation
• dataset scarcity
• real-time identification
• automated sorting

Innovation

• screw classification
• industrial vision dataset
• controlled image acquisition
• lightweight CNN
• reproducible data collection