Semi-Automated Data Pipeline (CVAT + YOLOv8)
The Blueprint (Goal): Creating a high-precision dataset for detecting objects in a manufacturing environment. The goal was to reduce the time and cost of manual annotation without sacrificing accuracy. The Process (How I solved it): Instead of manually drawing every box, I architected a "Human-in-the-Loop" workflow—think of it like an assembly line where a robot does the heavy lifting and I act as the Quality Control Inspector. Auto-Labeling (The Robot): I ran raw images through a custom YOLOv8 model I trained to generate the initial bounding boxes automatically. Verification (The Inspector): I imported these pre-labeled images into CVAT. My role shifted to verifying the AI's work and fixing edge cases, rather than drawing from scratch. Final Output: Cleaned and exported the validated dataset in YOLO format for final training. The Result: This semi-automated approach increased annotation speed by 5-6x compared to standard manual labeling, delivering a production-ready