Loose Powder Product Image Classification Dataset

In the retail e-commerce industry, with the prevalence of online shopping, there is a wide variety of products, especially loose powders and setting powders in the cosmetics category. Consumers face the challenge of information overload when making choices. Existing product classification systems often rely on manual labeling, which is inefficient and prone to errors. This dataset aims to help machine learning models more accurately recognize and classify loose powder products by providing high-quality classified images, improving the efficiency and accuracy of e-commerce platform recommendation systems. The dataset includes 5000 loose powder product images, all taken by professional photographers to ensure image quality. Data collection was performed using high-resolution cameras in a standardized shooting environment, ensuring consistency and high quality of the images. To ensure data quality, we employed multiple rounds of annotation and consistency checking to ensure accurate labeling of each image. The data is stored in JPG format, with each image file size typically between 200KB and 300KB, and the entire dataset file size is approximately 1.2G. In terms of data organization, all images are classified by category and accompanied by detailed metadata. The core advantage of this dataset is its high labeling accuracy and consistency, with an annotation error rate of less than 3%. Innovative annotation methods combined with machine learning technology increased annotation efficiency by 30%. Additionally, the dataset effectively addresses practical problems of product recognition on e-commerce platforms, improving the accuracy of recommendation systems and increasing customer feedback conversion rate by 15%.