Comprehensive image dataset for enhancing object detection in chemical experiments

Ryosuke Sasaki, Mikito Fujinami, Hiromi Nakai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The application of image recognition in chemical experiments has the potential to enhance experiment recording and risk management. However, the current scarcity of suitable benchmarking datasets restricts the applications of machine vision techniques in chemical experiments. This data article presents an image dataset featuring common chemical apparatuses and experimenter's hands. The images have been meticulously annotated, providing detailed information for precise object detection through deep learning methods. The images were captured from videos filmed in organic chemistry laboratories. This dataset comprises a total of 5078 images including diverse backgrounds and situations surrounding the objects. Detailed annotations are provided in accompanying text files. The dataset is organized into training, validation, and test subsets. Each subset is stored within independent folders for easy access and utilization.

Original languageEnglish
Article number110054
JournalData in Brief
Volume52
DOIs
Publication statusPublished - 2024 Feb

Keywords

  • Chemical apparatus
  • Deep learning
  • Image classification
  • Image recognition

ASJC Scopus subject areas

  • General

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