Bottled Goods Image Classification Dataset

The retail e-commerce industry currently faces challenges such as low efficiency and poor accuracy in product classification, affecting inventory management and user experience. Existing image classification solutions often rely on limited sample sizes and simple feature extraction methods, resulting in insufficient model generalization capability. This dataset aims to address the problem of insufficient samples in image classification by providing a large number of high-quality images of bottled goods, thereby enhancing classification accuracy and efficiency. Data collection is done using professional photographic equipment under standard lighting conditions to ensure image quality. Quality control measures include multiple rounds of annotation and expert review to ensure the accuracy of each image's label. The data storage format is JPG, organized by category, facilitating subsequent processing and use. The core advantage of this dataset is a labeling accuracy of over 95%, strong sample consistency, and high completeness. By introducing new labeling methods and data augmentation techniques, the model's accuracy in classification tasks is improved by 10%. Moreover, this dataset provides a reliable solution for product identification in retail e-commerce, significantly improving inventory management efficiency.