Infusion Bottle Liquid Type Classification Image Dataset

The current medical industry faces challenges in liquid infusion management, such as inaccurate classification and low processing efficiency. Traditional manual classification methods often rely on experience, which can lead to errors. Existing solutions lack sufficient training data, resulting in inadequate accuracy of intelligent classification models. This dataset aims to provide a rich collection of liquid type image data to help researchers and developers train efficient image classification models, thereby enhancing the accuracy and efficiency of automated diagnosis. The dataset includes images collected using high-resolution camera equipment in standard medical environments, subjected to multiple rounds of labeling and consistency checks to ensure the accuracy and consistency of the annotations. The data is stored in JPG format and organized by categories. The data collection process used professional photography equipment and lighting environments to ensure image quality, and it underwent expert review and multiple rounds of labeling to ensure high standards. The dataset is organized with classification labels and image file links for easy subsequent deep learning tasks.