Infusion Stand Scene Classification Image Dataset in Ward Environment

In the current medical industry, monitoring infusion stand scenes in ward environments faces various challenges, such as high work pressure and complex environmental changes, leading to poor monitoring and management of infusion stands. Existing solutions often rely on manual monitoring and cannot accurately identify the status of infusion stands in real-time, which affects the quality of medical services. This dataset aims to help machine learning algorithms better achieve classification of infusion stands in ward environments by providing high-quality image data, meeting the needs of hospitals for equipment management. Data is collected using high-resolution cameras at different times and lighting conditions to ensure image diversity. To ensure data quality, multiple rounds of annotation and expert review are employed to ensure the accuracy and consistency of annotation information. The data is stored in JPEG format and organized in category folders for easy subsequent processing and use.