Comprehensive image dataset for enhancing object detection in chemical experiments

Ryosuke Sasaki, Mikito Fujinami, Hiromi Nakai*

*この研究の対応する著者

研究成果: Article査読

5 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号110054
ジャーナルData in Brief
52
DOI
出版ステータスPublished - 2024 2月

ASJC Scopus subject areas

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