Cotton Coat Image Classification Dataset

The current retail e-commerce industry faces challenges such as low product recognition efficiency and chaotic classification management. Existing image classification solutions often rely on general models that cannot effectively recognize this specific category of cotton coats, leading to poor inventory management and diminished customer experience. This dataset aims to provide high-quality cotton coat images to address accurate recognition and classification issues. Data collection is conducted with professional photographic equipment in a standardized shooting environment to ensure each image has high resolution and good lighting. In terms of quality control, multiple rounds of annotation and expert reviews are implemented to ensure consistency and accuracy of annotations. Data is stored in JPG format, organized so that each category corresponds to a folder, facilitating subsequent applications and processing. This dataset offers labeling accuracy of up to 95% and achieved a pass rate of 98% in consistency checks, which is a 15% improvement in labeling accuracy compared to existing datasets. Innovative data augmentation techniques are employed to allow models to achieve better generalization during training, improving recognition accuracy by over 20% in practical applications and significantly enhancing user experience.