Irregular Mechanical Parts Automatic Classification Detection Dataset

The current industrial sector faces challenges such as the manual inspection of irregular parts, which is time-consuming and prone to human error. Existing solutions often lack the accuracy and speed needed for efficient production lines. This dataset aims to address the technical problem of automating the classification and detection of irregular mechanical parts, fulfilling the business need for enhanced operational efficiency. The dataset includes images captured using high-resolution cameras in controlled environments, ensuring consistent lighting and focus. Quality control measures involve multiple rounds of annotation, consistency checks among different annotators, and expert reviews to validate the accuracy of classifications. Data is stored in JPG format, organized by categories of mechanical parts and associated metadata.