Dishwasher Installation Type Recognition Dataset

In the current retail e-commerce industry, identifying the type of dishwasher installation is crucial for accurate product categorization and customer satisfaction. However, existing solutions often rely on manual classification, which is time-consuming and error-prone. This dataset aims to address the challenge of automating the recognition of freestanding and built-in dishwashers using images. The dataset is constructed by collecting images from various online retail platforms, ensuring a diverse representation of installation types. Quality control measures include multiple rounds of annotation, consistency checks, and expert reviews to maintain high accuracy. The images are stored in JPG format, organized by installation type for easy access and training purposes. The core advantages of this dataset include high data quality with over 95% annotation accuracy, achieved through rigorous quality control processes. Innovative labeling techniques and data augmentation methods are employed to enhance model training. The dataset significantly improves performance metrics, with expected increases of 20% in classification accuracy compared to existing datasets. Moreover, it provides a comprehensive resource for training machine learning models, thereby streamlining the product categorization process in the e-commerce sector.