Popuri Sai Vishnu Vardhan

Key Skills

Software

CVAT

LabelImg

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Label Types

Bounding Box

Polygon

Entity Ner Classification

Segmentation

Freelancer Overview

I am passionate about AI and data science, with hands-on experience in data labeling, annotation, and AI training data evaluation across NLP and computer vision domains. My work includes developing sentiment analysis systems using TextBlob, VADER, and BERT for text classification, as well as building object detection models with TensorFlow to identify and classify images. I have evaluated AI-generated responses for accuracy and quality as a prompt engineer, ensuring precise and reliable training data. My technical skills span Python, SQL, machine learning, NLP, data visualization, and tools like Pandas, NumPy, Scikit-learn, Power BI, and Git. I am dedicated to delivering high-quality annotated datasets that drive model accuracy and effectiveness in real-world applications.

Labeling Experience

Multimodal Data Annotation for Computer Vision and NLP Tasks using Label Studio

LabelimgImageBounding BoxPolygon

Worked on multiple data annotation and labeling projects involving image, text, and audio datasets using Label Studio, CVAT, and LabelImg. Key responsibilities included: Annotating images with bounding boxes, polygons, and segmentation masks for object detection and image segmentation models. Performing Named Entity Recognition (NER) and text classification for NLP and LLM training datasets. Labeling audio data with transcription and speech segmentation for ASR model training. Ensuring high-quality annotations by following strict labeling guidelines and performing quality assurance checks. Handling large-scale datasets (5,000+ samples) while maintaining >98% annotation accuracy. The project focused on building high-quality training datasets for machine learning, deep learning, and large language models (LLMs).

Worked on multiple data annotation and labeling projects involving image, text, and audio datasets using Label Studio, CVAT, and LabelImg. Key responsibilities included: Annotating images with bounding boxes, polygons, and segmentation masks for object detection and image segmentation models. Performing Named Entity Recognition (NER) and text classification for NLP and LLM training datasets. Labeling audio data with transcription and speech segmentation for ASR model training. Ensuring high-quality annotations by following strict labeling guidelines and performing quality assurance checks. Handling large-scale datasets (5,000+ samples) while maintaining >98% annotation accuracy. The project focused on building high-quality training datasets for machine learning, deep learning, and large language models (LLMs).

2025 - 2025

Education

Work History