Eyeshadow Product Image Classification Dataset

The current retail e-commerce industry faces challenges such as a wide variety of products and diverse consumer demand. Traditional product recognition methods struggle to quickly and accurately classify products, leading to inefficiencies in inventory management and recommendation systems. Existing solutions often rely on manual labeling, which is slow and error-prone, unable to meet the rapidly changing market demands. This dataset aims to improve the accuracy and efficiency of image classification models by providing high-quality eyeshadow product image data. Data collection used professional photography equipment under standardized lighting conditions to ensure each image is clear and distinguishable. In terms of quality control, a three-round labeling and consistency check were adopted to ensure labeling accuracy, and expert review was conducted to improve data quality. Data is stored in JPG format and organized by category folders for easy retrieval and use.