Anaiya Hollingsworth

Key Skills

Software

CVAT

LabelImg

Roboflow

Top Subject Matter

Computer Vision

Natural Language Processing

Machine Learning

Top Data Types

Image

Video

Text

Top Task Types

Bounding Box

Polygon

Point/Key Point

Segmentation

Object Detection

Classification

Entity (NER) Classification

Polyline

Diagnosis

Freelancer Overview

AI Labeler / Data Annotator, Outlier. Core strengths include CVAT, LabelImg, and Roboflow. Education includes Bachelor of Science, University of Nairobi (2022). AI-training focus includes data types such as Image, Video, and Text and labeling workflows including Bounding Box, Polygon, and Point.

Labeling Experience

AI Labeler / Data Annotator, Outlier

CVATImageBounding BoxPolygon

As an AI Labeler/Data Annotator at Outlier, I annotated images and videos for computer vision projects and labeled text data for NLP tasks. My work involved creating bounding boxes, polygons, and keypoints, and ensuring the quality and consistency of annotations. I used tools such as CVAT, Labelimg, and Roboflow to meet productivity and quality targets while maintaining high accuracy (95%+). • Annotated various data types including images, videos, and text. • Used annotation tools: CVAT, Labelimg, and Roboflow. • Tasks included object detection, segmentation, classification, NER, and sentiment analysis for NLP. • Ensured high-quality and consistent labels following project guidelines.

As an AI Labeler/Data Annotator at Outlier, I annotated images and videos for computer vision projects and labeled text data for NLP tasks. My work involved creating bounding boxes, polygons, and keypoints, and ensuring the quality and consistency of annotations. I used tools such as CVAT, Labelimg, and Roboflow to meet productivity and quality targets while maintaining high accuracy (95%+). • Annotated various data types including images, videos, and text. • Used annotation tools: CVAT, Labelimg, and Roboflow. • Tasks included object detection, segmentation, classification, NER, and sentiment analysis for NLP. • Ensured high-quality and consistent labels following project guidelines.

2023 - Present

Data Annotation Intern, Data annotation.ai

ImageBounding BoxClassification

During my Data Annotation Intern role at Data annotation.ai, I assisted in labeling image datasets for machine learning projects. My tasks included image classification and bounding box annotation to support supervised learning models. I participated in quality checks and data validation while adhering to annotation guidelines. • Labeled image datasets for machine learning projects. • Performed bounding box annotation and image classification. • Participated in data validation and quality checks. • Learned and strictly followed annotation guidelines.

During my Data Annotation Intern role at Data annotation.ai, I assisted in labeling image datasets for machine learning projects. My tasks included image classification and bounding box annotation to support supervised learning models. I participated in quality checks and data validation while adhering to annotation guidelines. • Labeled image datasets for machine learning projects. • Performed bounding box annotation and image classification. • Participated in data validation and quality checks. • Learned and strictly followed annotation guidelines.

2022 - 2022

Medical Image Segmentation (Project)

PolygonSegmentation

In the Medical Image Segmentation project, I annotated MRI images using polygon and segmentation masks. The goal was to support the development of medical AI models for diagnostic purposes. My annotations provided critical labeled data for training and evaluating medical AI algorithms. • Annotated MRI images for medical AI training. • Used polygons and segmentation masks for precise delineation. • Aided in building labeled datasets for diagnosis models. • Focused on accuracy and compliance with medical annotation standards.

In the Medical Image Segmentation project, I annotated MRI images using polygon and segmentation masks. The goal was to support the development of medical AI models for diagnostic purposes. My annotations provided critical labeled data for training and evaluating medical AI algorithms. • Annotated MRI images for medical AI training. • Used polygons and segmentation masks for precise delineation. • Aided in building labeled datasets for diagnosis models. • Focused on accuracy and compliance with medical annotation standards.

Not specified

Autonomous Driving Dataset Annotation (Project)

ImageBounding BoxPolyline

For the Autonomous Driving Dataset Annotation project, I annotated over 10,000 images using bounding boxes and lane markings. The project required precise object detection and semantic understanding annotations to build training data for autonomous vehicle models. My annotations contributed to improving model accuracy in real-world driving scenarios. • Labeled 10,000+ images for object detection and lane marking. • Used bounding boxes and polylines for detailed annotations. • Supported the development of AI for autonomous driving. • Ensured data quality through thorough review and consistency checks.

For the Autonomous Driving Dataset Annotation project, I annotated over 10,000 images using bounding boxes and lane markings. The project required precise object detection and semantic understanding annotations to build training data for autonomous vehicle models. My annotations contributed to improving model accuracy in real-world driving scenarios. • Labeled 10,000+ images for object detection and lane marking. • Used bounding boxes and polylines for detailed annotations. • Supported the development of AI for autonomous driving. • Ensured data quality through thorough review and consistency checks.

Not specified

Education

Work History