Appen
Scope: The project involved annotating a large dataset of images containing various electronic components to train a machine learning model for automated object detection and classification. Tasks Performed: Specific tasks included bounding box annotation for components like resistors, capacitors, and integrated circuits, as well as polygon segmentation for complex shapes. The work required adherence to detailed guidelines for labeling accuracy and consistency. Project Size: The dataset comprised approximately 50,000 high-resolution images, with an average of 15 objects per image. Quality Measures: Quality assurance was maintained through a multi-tier review process, including peer review and senior annotation specialist audits, ensuring a minimum accuracy rate of 98% was achieved across all labeled data.