Serum/Ampoule Image Classification Dataset

In the retail e-commerce industry, there are numerous types of skincare products like serums and ampoules, making it challenging for consumers to make quick choices when faced with a wide selection. Although current image recognition technology has advanced, it still faces problems of insufficient accuracy and low classification efficiency. This dataset aims to provide high-quality image data to help train more accurate image classification models, meeting the business needs of e-commerce platforms for automated recommendation and classification. The dataset includes 5000 high-quality images of serums and ampoules, collected from multiple e-commerce platforms to ensure a diverse range of products. The data collection process uses high-resolution cameras under standardized lighting conditions to ensure the clarity and consistency of the images. In terms of quality control, multiple rounds of annotation are implemented, and verification by a professional review team is conducted to improve annotation accuracy. Data is stored in JPG format, organized into a standardized folder structure for ease of subsequent use.