Mercury Thermometer Usage Posture Correctness Classification Image Dataset

In the current medical industry, temperature monitoring is one of the important health metrics, especially during pandemics, where the frequency of using thermometers has greatly increased. However, improper posture while using a mercury thermometer may lead to inaccurate measurement results, affecting health assessment. Existing solutions often rely on manual monitoring, which is inefficient and prone to errors. This dataset aims to use image classification technology to automatically recognize the usage posture of mercury thermometers, enhancing measurement accuracy. The dataset includes 5000 images of mercury thermometers in different postures, captured with high-definition cameras in well-lit environments to ensure image quality. After data collection, the images underwent multiple rounds of annotation and consistency checks to ensure the accuracy of each image's label, and they are finally stored in JPEG format for ease of subsequent processing and use.