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CVAT
Label StudioNo subject matter listed
VideoI am a detail-oriented Data Annotation Specialist with hands-on experience in video labeling, structured data validation, and quality assurance for AI training data. My work spans projects in computer vision and robotics, where I have delivered high-accuracy annotations using tools like CVAT and applied advanced techniques such as bounding boxes and segmentation. I am skilled in Python, SQL, and pandas for data cleaning and validation, and I have mentored teams to improve annotation standards and data quality. My ability to detect edge cases, resolve technical annotation issues, and ensure ethical compliance has consistently improved the performance of machine learning models. I thrive in remote and collaborative environments, always striving for precision and efficiency in every dataset I handle.
the project is provided by atlas capture where they offer multiple videos of 1-5 minutes and you have to segment them and also label every activity done, point out each object visible and action
Focus was on crafting video annotations with exceptional precision, accomplishing over 100+ hours with 98% accuracy. High-level tagging of actions, objects, and sequences in hundreds of footage minutes was executed for robotics models. Applied advanced CVAT techniques, emphasizing bounding boxes and segmentation for precise labeling. Initiated collaborations for enhancing training datasets and optimizing machine learning model outputs. Played a key role in improving AI training datasets, ensuring high-quality outcomes.
Served as a lead specialist in data annotation, mentoring developers and enhancing data quality standards. Corrected significant dataset inconsistencies, which improved AI model accuracy significantly. Conducted QA reviews to ensure both ethical and technical compliance within AI-related projects. Played a pivotal role in AI evaluation tasks, contributing to significant accuracy improvements and quality boosts across multiple projects.
i annotated a soccer match using CVAT the output was 3000 labels and a 98% accuracy
Engaged in resolving over 20+ annotation issues daily, achieving a 98% accuracy rate. Improved understanding and clarity in workflow decision points, leading to a 35% reduction in rework. Special focus on structured data validation to ensure consistent schema compliance. Strategic contributions led to enhancements in data integrity and reliability of AI models.
Bachelor of Software Development, Software Development
Freelance Video Labeling & Data Annotation Specialist
Lead Data Annotation & AI Evaluation Specialist
