Timothy Cooper

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Segmentation

Classification

Tracking

Freelancer Overview

I am an AI Training Specialist with 3 years of hands-on experience in data labeling, annotation, and preparing high-quality datasets for machine learning, particularly in computer vision domains. My expertise includes working with image, video, and audio data using industry-standard tools like Labelbox for bounding boxes, semantic segmentation, and classification, as well as preparing YOLO-formatted datasets for object detection and multi-object tracking. I have successfully contributed to large-scale projects such as annotating over 50,000 images for traffic object detection and performing frame-level tracking across thousands of video frames for surveillance applications. My strong attention to detail, commitment to annotation quality, and ability to collaborate with machine learning engineers have enabled me to deliver consistent and accurate training data that improves model performance.

Labeling Experience

Autonomous Vehicle Multi-Object Detection & Tracking Dataset

LabelboxImageBounding BoxSegmentation

Led data annotation for a large-scale autonomous vehicle dataset involving over 50,000 images and 12,000+ video frames. Responsible for labeling and tracking multiple objects including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure elements. Performed precise bounding box annotations and multi-object tracking across sequential video frames. Converted datasets into YOLO-compatible formats for object detection model training and validation. Maintained annotation accuracy above 98% through structured quality assurance processes, including: Frame-by-frame consistency checks Class verification and taxonomy compliance Peer review validation Error correction and dataset refinement Collaborated closely with machine learning engineers to refine labeling guidelines and improve dataset performance, contributing to improved object detection model accuracy and reduced false positives.

Led data annotation for a large-scale autonomous vehicle dataset involving over 50,000 images and 12,000+ video frames. Responsible for labeling and tracking multiple objects including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure elements. Performed precise bounding box annotations and multi-object tracking across sequential video frames. Converted datasets into YOLO-compatible formats for object detection model training and validation. Maintained annotation accuracy above 98% through structured quality assurance processes, including: Frame-by-frame consistency checks Class verification and taxonomy compliance Peer review validation Error correction and dataset refinement Collaborated closely with machine learning engineers to refine labeling guidelines and improve dataset performance, contributing to improved object detection model accuracy and reduced false positives.

2022 - 2023

Education

Work History