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I am a reviewer evaluating prompts and responses. The prompts are written based on four categories, and the responses follow a specified format.
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Role: Image labeling and training support for a traffic sign recognition system (FPGA-based YOLOv3-Tiny). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include LabelImg, Supervisely, and Roboflow. Education includes Bachelor's Degree, University of Information Technology (2024) and Bachelor's Degree, N/A (2026). AI-training focus includes data types such as Image and labeling workflows including Object Detection.
I am a reviewer evaluating prompts and responses. The prompts are written based on four categories, and the responses follow a specified format.
You labeled images for a traffic sign recognition dataset and supported YOLOv3-Tiny model training. You annotated multiple traffic sign categories with precise bounding boxes and verified class identification. You contributed to dataset preparation by managing storage and improving consistency across annotations.• Labeled 44 traffic sign classes across three groups: prohibition, warning, and guide signs. • Annotated nearly 10,000 images with accurate bounding boxes and class IDs. • Used LabelImg and Supervisely to perform image annotation. • Collaborated with an ML team to optimize annotation quality and consistency.
Bachelor's Degree, Computer Engineering
Bachelor's Degree, Computer Engineering
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