Mercury Thermometer Temperature Classification Dataset in Home Scenarios

In the current medical industry, home care and health monitoring are increasingly emphasized, especially since the accuracy of temperature monitoring directly affects the early diagnosis and treatment of diseases. However, due to users' different understanding of how to use thermometers, the reliability of measurement results has been insufficient. Existing solutions rely heavily on manual annotation and traditional machine learning methods, lacking specialized datasets for home scenarios. To address this issue, this dataset focuses on image classification of mercury thermometers in home scenarios, aiming to enhance the automation and intelligence of temperature monitoring. High-resolution cameras are used to capture images in various home settings, ensuring diversity and authenticity. The annotation process involves multiple rounds of annotation and expert review to ensure consistency and accuracy. Data is stored in JPEG format for quick access and processing. The core advantages of the dataset lie in data quality and annotation precision, with an annotation consistency of up to 95%, representing a 20% improvement over traditional datasets. Innovative annotation methods and data augmentation techniques have improved model performance metrics by over 15%, providing good practical application value.