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ImageI have hands-on experience in data labeling and AI training, specializing in creating high-quality annotated datasets for computer vision models. I excel at tasks like bounding boxes, image segmentation, and quality assurance to ensure datasets meet rigorous standards. My meticulous attention to detail and ability to analyze edge cases have consistently contributed to improved model performance and reduced bias in training data. Additionally, I bring a strong understanding of AI workflows, including preprocessing, dataset management, and integrating model feedback to refine annotations. I’ve collaborated with cross-functional teams to develop efficient labeling workflows, demonstrating strong communication and problem-solving skills. My ability to adapt to new tools and emerging AI trends, combined with a commitment to accuracy and efficiency, sets me apart in delivering impactful results for AI-driven projects.
Autonomous waste sortation using bounding boxes in a platform like Supahand involves leveraging annotated data to train machine learning models for efficient waste identification and categorization.
Bachelor Degree in Industrial Engineering, Industrial Engineering
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