Sleep Monitoring Device Model Classification Image Dataset

Currently, the sleep monitoring industry faces challenges due to the diversity of device models and poor user experience, causing confusion for healthcare providers in selecting the appropriate equipment. Existing solutions often lack adequate image classification capability, failing to accurately identify different device models, which affects medical decision-making. This dataset aims to solve the technical difficulties of device model recognition by providing high-quality images of sleep monitoring devices to meet the demand for high-accuracy classification in the medical industry. Data collection was conducted using professional photography equipment in a laboratory environment to ensure image quality. Additionally, multiple rounds of annotation and consistency checks were implemented to ensure the accuracy and reliability of the data. The data is stored in JPG format and organized according to device model for easy subsequent processing and use.