Foundation Liquid Product Image Classification Dataset

The current retail e-commerce industry faces challenges in product image recognition and classification, especially against a backdrop of rapid updates of diverse products and changing consumer demands, where traditional methods fall short in meeting accurate classification needs. Existing solutions often rely on manual annotation, resulting in inefficiency and inadequacy in annotation accuracy. This dataset aims to address the inconsistencies in image classification annotation and insufficient data volume by providing high-quality foundation liquid product images, fulfilling the needs of e-commerce platforms for product classification and recommendation systems. Data collection involves web crawling to obtain foundation liquid product images from major e-commerce platforms, ensuring data diversity and authenticity. A multi-round review and consistency check during the annotation process ensures accuracy and consistency, with a final review by an expert team to guarantee high-quality data. The data is stored in JPG format and organized by category for ease of subsequent training and application.