Beard Jason Michael

Key Skills

Software

Labelbox

CVAT

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Audio

Video

Top Label Types

Bounding Box

Point Key Point

Classification

Tracking

Translation Localization

Audio Recording

Transcription

Cuboid

Freelancer Overview

I am an AI training specialist and data annotation expert with over seven years of hands-on experience delivering high-quality datasets for computer vision, NLP, and audio-based machine learning systems. My background includes leading large-scale annotation projects—such as labeling over 750,000 images and 15,000 hours of video—and managing remote teams to optimize workflow and ensure top-tier data quality. I am highly proficient with tools like YOLO (v3–v8), Labelbox, CVAT, Supervisely, Roboflow, Amazon SageMaker Ground Truth, and Prodigy, and skilled in preparing datasets in formats such as COCO, Pascal VOC, and custom JSON. My expertise spans bounding box, polygon, and segmentation annotation, video and audio labeling, NLP tasks like NER and sentiment analysis, and rigorous quality assurance processes. I have contributed to projects in diverse domains, including autonomous driving, medical imaging, retail analytics, and speech recognition, consistently improving model accuracy and efficiency. I am passionate about optimizing AI training pipelines, reducing labeling errors, and collaborating with machine learning teams to build robust, production-ready datasets.

Labeling Experience

Autonomous Vehicle Multi-Class Object Detection & Segmentation Project

AppenVideoBounding BoxPoint Key Point

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

2025

Autonomous Vehicle Multi-Class Object Detection & Segmentation Project

CVATAudioPoint Key PointTracking

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

2023 - 2024

Autonomous Vehicle Multi-Class Object Detection & Segmentation Project

LabelboxImageBounding BoxPoint Key Point

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

Led a large-scale computer vision data labeling project focused on autonomous vehicle perception systems. The project involved annotating over 750,000 high-resolution images and 15,000+ hours of dashcam video footage for real-time object detection and road scene understanding. Specific tasks performed included: Bounding box annotation for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and obstacles Polygon and semantic segmentation for lane markings, road boundaries, and sidewalks Multi-object tracking across video frames Class balancing and dataset refinement for YOLO model training Annotation conversion to YOLO, COCO, and Pascal VOC formats Edge case identification (night driving, rain, occlusion scenarios) Dataset validation and correction cycles Implemented a multi-layer quality assurance framework including: Double-blind annotation review 10% random sampling audits Automated script-based validation checks Inter-annotator agreement scoring (IAA above 9

2019 - 2023

Education

Work History