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<p>In modern laboratories, automation and safety rely heavily on accurately detecting and identifying laboratory equipment. To address this need, we introduce a comprehensive and well-curated dataset designed to detect 25 commonly used chemistry lab apparatuses. The dataset comprises 4,599 JPG-format images captured under diverse real-world conditions, including varying lighting, backgrounds, angles, overlaps, and distances - factors that enhance the robustness and generalizability of model training. It is split into training (70%), validation (20%), and testing (10%) subsets. This resource is particularly valuable for developing laboratory automation systems, with potential applications in safety monitoring, inventory management, and real-time tracking of lab tools. We evaluated the dataset using seven state-of-the-art object detection models, all achieving impressive performance with mAP@50 scores exceeding 0.9: RF-DETR (0.992), YOLOv11 (0.987), YOLOv9 (0.986), YOLOv5 (0.985), YOLOv8 (0.983), YOLOv7 (0.947), and YOLOv12 (0.92). To the best of our knowledge, this is the most extensive publicly available dataset of its kind, covering 25 categories of chemistry laboratory apparatuses and establishing a strong foundation for future research in laboratory automation.</p>

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Real-world chemistry lab image dataset for equipment recognition across 25 apparatus categories

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月5日

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  • Md Sakhawat Hossain,
  • Md Sadman Haque,
  • Md Mostafizur Rahman,
  • Md Mosaddik Mashrafi Mousum,
  • Zobaer Ibn Razzaque,
  • Robiul Awoul Robin,
  • Raiyan Rahman,
  • Jannatun Noor

摘要

In modern laboratories, automation and safety rely heavily on accurately detecting and identifying laboratory equipment. To address this need, we introduce a comprehensive and well-curated dataset designed to detect 25 commonly used chemistry lab apparatuses. The dataset comprises 4,599 JPG-format images captured under diverse real-world conditions, including varying lighting, backgrounds, angles, overlaps, and distances - factors that enhance the robustness and generalizability of model training. It is split into training (70%), validation (20%), and testing (10%) subsets. This resource is particularly valuable for developing laboratory automation systems, with potential applications in safety monitoring, inventory management, and real-time tracking of lab tools. We evaluated the dataset using seven state-of-the-art object detection models, all achieving impressive performance with mAP@50 scores exceeding 0.9: RF-DETR (0.992), YOLOv11 (0.987), YOLOv9 (0.986), YOLOv5 (0.985), YOLOv8 (0.983), YOLOv7 (0.947), and YOLOv12 (0.92). To the best of our knowledge, this is the most extensive publicly available dataset of its kind, covering 25 categories of chemistry laboratory apparatuses and establishing a strong foundation for future research in laboratory automation.