Computer Vision Dataset Curation & Quality Assurance
Project Scope: Supported a Neural Architecture Search (NAS) research initiative focused on optimizing CNN architectures for edge vision applications. The project involved curating and annotating subsets of the ImageNet dataset to train reinforcement learning agents for architecture discovery. Specific Data Labeling Tasks Performed: Performed multi-class image classification labeling across 1,000+ categories with strict adherence to ImageNet ontology standards Created and validated bounding box annotations for object localization tasks used in model evaluation Conducted quality assurance audits on existing labeled datasets, identifying and correcting approximately 200+ mislabeled samples that were impacting model convergence Developed Python scripts to automate consistency checks across label sets, flagging edge cases and ambiguous annotations for human review Project Size: Managed annotation workflows for approximately 50,000 images across training, validation, and test splits Collaborated with a team of 3 researchers to align labeling guidelines with model performance goals Quality Measures Adhered To: Maintained inter-annotator agreement (IAA) standards with 95%+ consistency across reviewers Implemented multi-pass review process: each sample was reviewed by at least two annotators, with discrepancies escalated for senior review Used randomized spot-checking (10% of batches) to verify label accuracy before integration into training pipelines Documented labeling guidelines and edge case definitions to ensure consistency across sessions This experience demonstrates a strong understanding of how annotation precision directly influences model performance, combining hands-on labeling expertise with a technical background in AI/ML development.