Washing Machine Appearance Recognition Dataset

The washing machine industry faces challenges in accurately identifying different types of washing machines, such as drum, pulsator, and mini types, which complicates inventory management and enhances the risk of misclassification. Current solutions often rely on manual classification, which is time-consuming and error-prone. This dataset aims to provide a robust framework for training machine learning models that can automate the classification of washing machine appearances, thereby improving efficiency and accuracy in e-commerce applications. The dataset consists of images collected from various online retail platforms, ensuring a diverse representation of washing machines. Quality control measures include multiple rounds of annotation, consistency checks among annotators, and expert reviews to ensure high-quality labels. The images are stored in JPG format and organized in a directory structure by class labels.