Data labeling
Project Scope: The project focused on building high-quality AI training datasets through precise image annotation and structured quality evaluation to enhance model accuracy and performance. Data Labeling Tasks Performed: Performed bounding box annotation, polygon annotation, point/keypoint labeling, and semantic/instance segmentation. Additionally conducted object detection, attribute tagging, segmentation refinement, prompt alignment checks, and consistency validation across datasets. Project Size: Contributed to large-scale datasets comprising thousands of images across multiple annotation workflows and cross-functional teams. Quality Measures Adhered To: Followed strict annotation guidelines, maintained high accuracy thresholds, conducted multi-level quality assurance reviews, resolved edge cases, ensured inter-annotator consistency, and met turnaround time and precision benchmarks.