Data labeler
The project involved performing large-scale image data labeling to support the development and improvement of machine learning and computer vision models. Key activities included: Annotating images according to predefined labeling guidelines. Classifying and tagging objects, scenes, or features within images. Drawing bounding boxes, polygons, or segmentation masks where required. Ensuring labeling consistency and quality across datasets. Reviewing and correcting annotations to meet accuracy standards. Managing datasets and maintaining proper labeling documentation. Collaborating with quality assurance teams to improve annotation accuracy. Meeting productivity and quality targets within project timelines. The labeled datasets contributed to training and validating AI models for automated image recognition and analysis tasks.