Blender Accessories Recognition Dataset

The current retail e-commerce sector faces challenges in accurately identifying various blender accessories, which can lead to inefficiencies in inventory management and customer service. Existing solutions often rely on manual categorization, which is time-consuming and prone to errors. This dataset aims to address the technical challenges of accessory recognition and improve the automation of inventory processes. The data collection involved capturing high-resolution images of blender accessories in diverse lighting conditions and angles, using DSLR cameras. Quality control measures included multi-round annotations, consistency checks among annotators, and expert reviews to ensure high accuracy. The dataset is stored in JPG format, organized by accessory type, and includes metadata for each image.