John Pool

Key Skills

Software

Labelbox

Other

CVAT

Top Subject Matter

AI Art Generation

Image Quality Assessment

Autonomous Systems

Top Data Types

Image

Video

Audio

Top Label Types

Bounding Box

Point Key Point

Object Detection

Tracking

Emotion Recognition

Entity Ner Classification

Audio Recording

Transcription

Segmentation

Classification

Freelancer Overview

Midjourney AI Image Evaluation Specialist. Core strengths include Other and CVAT. AI-training focus includes data types such as Image and labeling workflows including Evaluation, Rating, and Object Detection.

Labeling Experience

Multi-Object Video Tracking for Smart Surveillance System

LabelboxVideoBounding BoxPoint Key Point

Managed advanced video data labeling and annotation projects involving real-time multi-object tracking. Annotated and tracked pedestrians, vehicles, and dynamic objects across thousands of video frames. Optimized labeling workflows to support high-accuracy object detection models. Ensured dataset scalability and precision through structured QA validation processes.

Managed advanced video data labeling and annotation projects involving real-time multi-object tracking. Annotated and tracked pedestrians, vehicles, and dynamic objects across thousands of video frames. Optimized labeling workflows to support high-accuracy object detection models. Ensured dataset scalability and precision through structured QA validation processes.

2023

Advanced Object Detection & Tracking Dataset Labeling

LabelboxImageBounding BoxPoint Key Point

Led the data labeling and annotation of large-scale image and video datasets for AI model training in autonomous systems. Developed comprehensive labeling guidelines for object detection, tracking, and segmentation tasks, ensuring high-quality and consistent annotations. Worked closely with AI engineers to optimize data pipelines, validate dataset integrity, and improve model performance. Trained and supervised a team of junior annotators to maintain dataset accuracy at scale.

Led the data labeling and annotation of large-scale image and video datasets for AI model training in autonomous systems. Developed comprehensive labeling guidelines for object detection, tracking, and segmentation tasks, ensuring high-quality and consistent annotations. Worked closely with AI engineers to optimize data pipelines, validate dataset integrity, and improve model performance. Trained and supervised a team of junior annotators to maintain dataset accuracy at scale.

2023 - Present

Large-Scale Image Classification & Segmentation for Retail AI

LabelboxImageBounding BoxSegmentation

Executed large-scale data labeling and annotation for retail AI systems focused on product recognition and shelf analytics. Annotated thousands of product images using polygon segmentation and bounding boxes to improve object detection accuracy. Designed detailed annotation protocols to ensure consistent labeling across diverse product categories. Collaborated with data scientists to refine dataset quality and improve model precision for product classification tasks.

Executed large-scale data labeling and annotation for retail AI systems focused on product recognition and shelf analytics. Annotated thousands of product images using polygon segmentation and bounding boxes to improve object detection accuracy. Designed detailed annotation protocols to ensure consistent labeling across diverse product categories. Collaborated with data scientists to refine dataset quality and improve model precision for product classification tasks.

2022 - 2022

Audio Transcription & Intent Labeling for Conversational AI

LabelboxAudioEntity Ner ClassificationTracking

Performed detailed audio data labeling and annotation for conversational AI training datasets. Transcribed and tagged thousands of voice recordings, identifying user intent and key entities to support NLP model development. Applied strict quality control standards to ensure linguistic accuracy and contextual consistency. Assisted in refining annotation taxonomies for better model training outcomes.

Performed detailed audio data labeling and annotation for conversational AI training datasets. Transcribed and tagged thousands of voice recordings, identifying user intent and key entities to support NLP model development. Applied strict quality control standards to ensure linguistic accuracy and contextual consistency. Assisted in refining annotation taxonomies for better model training outcomes.

2021 - 2022

Education

Work History