Multi-Modal Data Annotation & AI Training (Computer Vision & NLP)

Executed high-precision data annotation projects using Label Studio to support the development of Computer Vision and NLP models.

Key Contributions:

Computer Vision (Polygon Segmentation): Applied advanced polygon masks for autonomous vehicle datasets, achieving pixel-perfect tracing of complex instances including pedestrians, cyclists, and vehicles.

Object Detection: Utilized bounding boxes to accurately identify and classify traffic infrastructure, such as cars and signposts, across diverse urban perspectives.

Named Entity Recognition (NER): Performed high-accuracy text labeling to categorize entities including Person, Location, Organization, and Time within various datasets.

Sentiment Analysis: Classified large-scale customer feedback into Positive, Negative, and Neutral categories to train emotional intelligence in Large Language Models (LLMs).

Dedicated to providing high-fidelity ‘Ground Truth’ data with a focus on 99%+ accuracy and strict adherence to complex labeling guidelines.