Inductor and Transformer Detection Dataset

The current industrial sector faces significant challenges in the accurate inspection of power modules and magnetic components, which directly impacts product quality and reliability. Existing solutions often rely on manual inspection methods that are time-consuming and prone to human error. This dataset aims to address the technical issue of automated defect detection in inductors and transformers by providing a rich set of annotated images that can be used to train machine learning models. The data is collected using high-resolution cameras in controlled environments to ensure consistency and quality. Quality control measures include multiple rounds of annotation, consistency checks, and expert reviews to maintain high accuracy. The dataset is organized in JPG format, each image accompanied by its corresponding labels and bounding box information, facilitating straightforward integration into machine learning workflows.