Caleb Mcmurray

Key Skills

Software

Appen

Labelbox

Mighty AI

Snorkel AI

SuperAnnotate

Surge AI

CVAT

Top Subject Matter

Medical equipment detection

linguistics

Healthcare

Top Data Types

Computer Code Programming

Geospatial Tiled Imagery

Medical Dicom

Top Task Types

Computer Programming Coding

Data Collection

Geocoding

Object Detection

Prompt Response Writing SFT

Freelancer Overview

With extensive experience in data labeling and AI training data management, I have honed skills in data annotation, quality assurance, and dataset curation for diverse AI applications. I have worked on various projects involving image, text, and audio labeling, ensuring accuracy and consistency to support model training and evaluation. My ability to interpret nuanced data labeling requirements and develop guidelines that maintain data integrity has led to improved model performance and reliable outcomes. My attention to detail, combined with hands-on experience with industry-standard annotation tools, sets me apart. I am skilled in managing large datasets, implementing data quality checks, and collaborating closely with data scientists to tailor datasets to specific model requirements. This experience enables me to deliver high-quality, precise training data that contributes to the effectiveness and robustness of AI models.

Labeling Experience

Autonomous Vehicle Pedestrian and Traffic Object Detection

CVATImageBounding BoxSegmentation

For an autonomous vehicle project, I led a detailed data labeling initiative focused on pedestrian and traffic object detection. Using Labelbox and CVAT, I created and managed large datasets by applying bounding boxes and pixel-level segmentation for accurate object identification and classification, crucial for real-time environment perception. My work included rigorous quality assurance processes, identifying edge cases, and refining annotation guidelines to ensure consistency and reliability across high-resolution image and video data. This project enhanced the vehicle's object detection accuracy, improving safety and reliability in various driving scenarios, making it a robust solution for autonomous navigation systems.

For an autonomous vehicle project, I led a detailed data labeling initiative focused on pedestrian and traffic object detection. Using Labelbox and CVAT, I created and managed large datasets by applying bounding boxes and pixel-level segmentation for accurate object identification and classification, crucial for real-time environment perception. My work included rigorous quality assurance processes, identifying edge cases, and refining annotation guidelines to ensure consistency and reliability across high-resolution image and video data. This project enhanced the vehicle's object detection accuracy, improving safety and reliability in various driving scenarios, making it a robust solution for autonomous navigation systems.

2023 - 2023

Education

Work History