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Data Annotation Tech
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Polygon
Object Detection
PolylineA standout project involved leading a 6-person team to annotate 50,000+ images for a pedestrian-detection model. I created style guides, resolved ambiguous scenarios (e.g., occluded objects, low-light frames), and introduced a two-pass review system that cut rework by 40%. My qualifications include a certification in AI data preparation (e.g., Udacity’s Data Labeling for ML) and familiarity with active learning pipelines—skills that help me prioritize uncertain samples to accelerate model improvement. I don’t just label; I partner with data scientists to ensure labels directly improve model performance.
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Data CollectionBuild a 100k image dataset with class labels and basic metadata for supervised learning. Image acquisition, validity flagging, metadata tagging (lighting, angle, source), deduplication with the project size of 100k images, 20 classes, 25 annotators, 4 weeks, ~$8k budget. Gold‑set qualification, Fleiss’ Kappa (≥0.8), stratified diversity, 10% senior audit, compliance checks.
certificate upon completion, Data Engineering for AI Training
Internship
