Image Annotation & Classification Project – Annotator/Data Labeler
I completed a dedicated image annotation project focused on urban street scenes using YOLO-format bounding boxes. My task involved labeling 60 images with a total of 195 bounding boxes covering six object classes relevant to autonomous driving and scene analysis. The labeling was carried out in two concentrated sessions, with meticulous attention to bounding box consistency and YOLO formatting standards. • Annotated and classified objects including vehicles, pedestrians, cyclists, traffic signs, traffic lights, and road obstacles. • Ensured all labels followed center-x, center-y, width, height normalized coordinates per YOLO requirements. • Managed dataset imbalance issues and documented rare classes needing additional data collection. • Generated a comprehensive portfolio report analyzing dataset quality, class balance, and annotation density.