Massage Device Usage Area Recognition Dataset

The retail e-commerce industry is currently facing challenges in accurately classifying massage devices according to their intended usage areas, which can lead to customer dissatisfaction and increased return rates. Existing solutions often fall short due to insufficient data diversity and poor annotation quality. This dataset aims to address the specific technical issue of identifying and classifying massage devices based on their designated body areas, fulfilling the business need for more accurate product categorization. The data was collected using high-resolution cameras in controlled lighting conditions, focusing on images of massage devices in various angles and backgrounds. Quality control measures included multi-round annotations, consistency checks, and expert reviews to ensure high accuracy. The data is stored in JPG format, organized in folders by usage area, facilitating easy access and processing. The core advantages of this dataset include high-quality annotations with over 95% accuracy in labeling, significantly improving classification performance compared to existing datasets. The innovative labeling method employed allows for quick adjustments and enhancements, while data augmentation techniques have been applied to increase the dataset's diversity, pushing the dataset's effective size to around 20,000 images. Moreover, the dataset addresses real-world problems by enabling better product recommendations, potentially reducing return rates by up to 30%.