Joshua Turner

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Emotion Recognition

Freelancer Overview

I am an experienced AI training specialist with over three years of hands-on expertise in data annotation and labeling for high-impact machine learning projects. My work spans image, video, and audio datasets, where I excel at producing precise bounding boxes, polygons, keypoint annotations, and multi-object tracking for computer vision applications. I am highly skilled with tools such as Labelbox, CVAT, Roboflow, Supervisely, and YOLO-based pipelines, ensuring top-tier annotation quality and consistency. My commitment to accuracy (maintaining 95%+ on all projects), strong interpretation of complex guidelines, and reliable remote collaboration have enabled me to deliver production-ready datasets on tight deadlines. I am passionate about supporting AI teams with quality training data and am flexible for remote, long-term opportunities.

Labeling Experience

High-Precision Multimodal Data Annotation for Computer Vision Models

LabelboxVideoBounding BoxPoint Key Point

Performed high-precision video annotation for computer vision model training across diverse real-world scenarios. The project involved frame-by-frame labeling of moving objects using bounding boxes, polygons, and keypoints, with a strong emphasis on multi-object tracking consistency across long video sequences. Responsibilities included identifying and labeling dynamic entities such as people, vehicles, and activities while preserving object IDs throughout occlusions, motion blur, and scene transitions. Applied temporal consistency checks and strict ontology rules to ensure annotations aligned with model training requirements. Processed large batches of video data using tools including Labelbox and CVAT, preparing datasets optimized for YOLO-based detection and tracking pipelines. Maintained 95%+ accuracy through structured QA workflows, self-review, and guideline compliance. Collaborated with remote AI teams to resolve edge cases, refine labeling taxonomies, and improve dataset qua

Performed high-precision video annotation for computer vision model training across diverse real-world scenarios. The project involved frame-by-frame labeling of moving objects using bounding boxes, polygons, and keypoints, with a strong emphasis on multi-object tracking consistency across long video sequences. Responsibilities included identifying and labeling dynamic entities such as people, vehicles, and activities while preserving object IDs throughout occlusions, motion blur, and scene transitions. Applied temporal consistency checks and strict ontology rules to ensure annotations aligned with model training requirements. Processed large batches of video data using tools including Labelbox and CVAT, preparing datasets optimized for YOLO-based detection and tracking pipelines. Maintained 95%+ accuracy through structured QA workflows, self-review, and guideline compliance. Collaborated with remote AI teams to resolve edge cases, refine labeling taxonomies, and improve dataset qua

2024

Advanced Video Annotation & Multi-Object Tracking for Computer Vision

LabelboxVideoPoint Key PointObject Detection

Executed large-scale video annotation projects supporting computer vision and action recognition models. Performed frame-by-frame labeling of dynamic scenes using bounding boxes, polygons, and keypoints while maintaining persistent object IDs for multi-object tracking tasks. Handled complex scenarios including occlusions, motion blur, dense traffic scenes, and rapid object movement. Applied strict ontology rules and temporal consistency checks to ensure annotations met production-level AI training standards. Processed thousands of video frames across diverse environments, preparing datasets optimized for YOLO-based object detection and tracking pipelines. Maintained 95%+ annotation accuracy through structured QA workflows, guideline adherence, and systematic self-review. Collaborated with remote AI teams to resolve edge cases, improve labeling taxonomies, and deliver high-quality datasets within tight deadlines.

Executed large-scale video annotation projects supporting computer vision and action recognition models. Performed frame-by-frame labeling of dynamic scenes using bounding boxes, polygons, and keypoints while maintaining persistent object IDs for multi-object tracking tasks. Handled complex scenarios including occlusions, motion blur, dense traffic scenes, and rapid object movement. Applied strict ontology rules and temporal consistency checks to ensure annotations met production-level AI training standards. Processed thousands of video frames across diverse environments, preparing datasets optimized for YOLO-based object detection and tracking pipelines. Maintained 95%+ annotation accuracy through structured QA workflows, guideline adherence, and systematic self-review. Collaborated with remote AI teams to resolve edge cases, improve labeling taxonomies, and deliver high-quality datasets within tight deadlines.

2023 - 2025

High-Precision Multimodal Data Annotation for Computer Vision Models

LabelboxImageBounding BoxPoint Key Point

Project Description Executed high-accuracy data labeling and annotation for large-scale multimodal datasets supporting computer vision and audio intelligence models. Responsibilities included creating bounding boxes, polygons, and keypoints for object detection tasks; performing frame-by-frame multi-object tracking in video sequences; and classifying as well as transcribing audio data. Worked with diverse datasets including street scenes, human activities, and general object categories. Ensured strict adherence to client ontologies and annotation guidelines while maintaining 95%+ labeling accuracy through structured QA workflows. Processed thousands of image frames and extended video sequences, optimizing annotations for YOLO-based detection models. Conducted self-review and peer QA checks to minimize relabeling rates and improve dataset consistency. Collaborated remotely with AI engineers and project managers to resolve edge cases and refine labeling taxonomies.

Project Description Executed high-accuracy data labeling and annotation for large-scale multimodal datasets supporting computer vision and audio intelligence models. Responsibilities included creating bounding boxes, polygons, and keypoints for object detection tasks; performing frame-by-frame multi-object tracking in video sequences; and classifying as well as transcribing audio data. Worked with diverse datasets including street scenes, human activities, and general object categories. Ensured strict adherence to client ontologies and annotation guidelines while maintaining 95%+ labeling accuracy through structured QA workflows. Processed thousands of image frames and extended video sequences, optimizing annotations for YOLO-based detection models. Conducted self-review and peer QA checks to minimize relabeling rates and improve dataset consistency. Collaborated remotely with AI engineers and project managers to resolve edge cases and refine labeling taxonomies.

2022 - 2024

Education

Work History