Harrison Greg

Key Skills

Software

Labelbox

Supervisely

OpenCV AI Kit (OAK)

Top Subject Matter

Autonomous driving

retail analytics

object detection

Top Data Types

Image

3D Sensor

Top Task Types

Bounding Box

Segmentation

Object Detection

Classification

Freelancer Overview

Data Annotation Specialist. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Supervisely, and OpenCV AI Kit (OAK). Education includes Bachelor of Science, University of Central Florida (2022). AI-training focus includes data types such as Image and 3D Sensor and labeling workflows including Bounding Box, Segmentation, and Object Detection.

Labeling Experience

Data Annotation Specialist

LabelboxImageBounding Box

As a Data Annotation Specialist at Outlier, I annotated and verified image, video, and LiDAR datasets to support AI model training. I automated annotation tasks using Python to increase team efficiency and conducted regular quality assurance audits to maintain high data accuracy. I collaborated closely with data engineers and mentored new team members on best practices. • Labeled and verified 2D images, video frames, and 3D LiDAR data using multiple annotation tools. • Maintained consistent labeling standards and ensured 98%+ data accuracy across projects. • Utilized software such as Labelbox, CVAT, Supervisely, V7, Segments.ai, and Scale AI for diverse labeling needs. • Improved annotation workflow automation, boosting team efficiency by 15%.

As a Data Annotation Specialist at Outlier, I annotated and verified image, video, and LiDAR datasets to support AI model training. I automated annotation tasks using Python to increase team efficiency and conducted regular quality assurance audits to maintain high data accuracy. I collaborated closely with data engineers and mentored new team members on best practices. • Labeled and verified 2D images, video frames, and 3D LiDAR data using multiple annotation tools. • Maintained consistent labeling standards and ensured 98%+ data accuracy across projects. • Utilized software such as Labelbox, CVAT, Supervisely, V7, Segments.ai, and Scale AI for diverse labeling needs. • Improved annotation workflow automation, boosting team efficiency by 15%.

2023 - Present

Junior Data Analyst (Intern)

LabelboxImageSegmentation

As a Junior Data Analyst intern at Accenture, I supported the preparation and validation of computer vision datasets for AI initiatives. I created Python scripts to detect labeling inconsistencies and assisted in developing annotation guidelines. I worked as part of a multidisciplinary team to ensure data integrity and precision in annotations. • Prepared and validated datasets for automated model training and validation. • Detected and resolved annotation inconsistencies, reducing manual review times significantly. • Collaborated with teams to draft workflow documentation and best practices. • Contributed to the maintenance of accurate computer vision data pipelines.

As a Junior Data Analyst intern at Accenture, I supported the preparation and validation of computer vision datasets for AI initiatives. I created Python scripts to detect labeling inconsistencies and assisted in developing annotation guidelines. I worked as part of a multidisciplinary team to ensure data integrity and precision in annotations. • Prepared and validated datasets for automated model training and validation. • Detected and resolved annotation inconsistencies, reducing manual review times significantly. • Collaborated with teams to draft workflow documentation and best practices. • Contributed to the maintenance of accurate computer vision data pipelines.

2022 - 2022

Retail Product Image Classification Dataset Annotator

Opencv AI Kit OakImageClassification

I labeled and segmented over 10,000 retail product images to create a comprehensive classification dataset for AI-driven retail analytics. I implemented data augmentation techniques using OpenCV to boost model diversity and robustness. I maintained strict quality control throughout the labeling phases to support high-accuracy AI classification models. • Performed image segmentation and category labeling for product identification. • Deployed OpenCV pipelines to expand training data. • Contributed to annotation guidelines to standardize labeling. • Supported rapid iteration and scaling of the dataset for better learning.

I labeled and segmented over 10,000 retail product images to create a comprehensive classification dataset for AI-driven retail analytics. I implemented data augmentation techniques using OpenCV to boost model diversity and robustness. I maintained strict quality control throughout the labeling phases to support high-accuracy AI classification models. • Performed image segmentation and category labeling for product identification. • Deployed OpenCV pipelines to expand training data. • Contributed to annotation guidelines to standardize labeling. • Supported rapid iteration and scaling of the dataset for better learning.

2021 - 2021

Autonomous Vehicle Object Detection Dataset Annotator

Supervisely3D SensorObject Detection

I annotated thousands of 3D LiDAR frames for a multi-class object detection dataset in the context of autonomous vehicles. I supported model training using YOLO-based frameworks and ensured high-performance detection metrics. I focused on accuracy and consistency in labeling to optimize AI-driven vehicle perception. • Labeled vehicles, pedestrians, and cyclists in 3D LiDAR point cloud data. • Applied object detection, cuboid, and segmentation techniques as needed. • Ensured data suitability for downstream machine learning tasks. • Enabled reliable training of object detection models for autonomy.

I annotated thousands of 3D LiDAR frames for a multi-class object detection dataset in the context of autonomous vehicles. I supported model training using YOLO-based frameworks and ensured high-performance detection metrics. I focused on accuracy and consistency in labeling to optimize AI-driven vehicle perception. • Labeled vehicles, pedestrians, and cyclists in 3D LiDAR point cloud data. • Applied object detection, cuboid, and segmentation techniques as needed. • Ensured data suitability for downstream machine learning tasks. • Enabled reliable training of object detection models for autonomy.

2021 - 2021

Education

Work History