Mercor
The project involved performing large-scale image labeling tasks for a machine learning dataset through Mercor. The primary objective was to accurately annotate visual elements within images to support the training and improvement of computer vision models. Tasks included identifying and labeling objects, drawing bounding boxes around specific items, categorizing images based on predefined classes, and verifying annotations for accuracy and consistency. The project covered a dataset consisting of several thousand images across multiple categories. Strict labeling guidelines were followed to ensure consistency, including adherence to annotation protocols, class definitions, and formatting requirements. Quality assurance was maintained through regular self-review, guideline checks, and platform-based validation processes to minimize labeling errors.