Virginia Shirley

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Classification

Tracking

Point Key Point

Freelancer Overview

I am a detail-oriented AI training specialist with over three years of hands-on experience in data labeling, annotation, and quality control across image, video, and audio formats. My expertise includes using industry-leading tools like Labelbox, YOLO, and advanced tracking systems to deliver high-quality datasets for computer vision and speech recognition models. I am skilled in managing large-scale projects, ensuring accuracy, and collaborating closely with machine learning engineers to optimize training data for model performance. My background also includes annotating complex audio datasets for speech-to-text applications and improving object recognition through precise labeling and tracking, as well as contributing to projects that enhanced transcription accuracy and real-time object detection. I bring a strong foundation in Python, basic ML frameworks, and a commitment to maintaining the highest data standards for reliable AI solutions.

Labeling Experience

AI Training Specialist – Data Labeling & Annotation

LabelboxVideoBounding BoxPoint Key Point

In this ongoing video annotation project, I am leading the data labeling efforts for training AI models in video-based applications, primarily focusing on object detection, action recognition, and tracking tasks. Using tools such as Labelbox and YOLO, I annotate complex video datasets with bounding boxes, polygons, and key points to identify and track objects across frames. Additionally, I apply advanced tracking algorithms like DeepSORT to accurately follow objects in motion, enhancing the robustness of object detection models. My responsibilities also include validating annotations, ensuring consistency, and cross-referencing annotations for high accuracy. This project contributes to the development of models used in autonomous systems, surveillance, and real-time video analysis.

In this ongoing video annotation project, I am leading the data labeling efforts for training AI models in video-based applications, primarily focusing on object detection, action recognition, and tracking tasks. Using tools such as Labelbox and YOLO, I annotate complex video datasets with bounding boxes, polygons, and key points to identify and track objects across frames. Additionally, I apply advanced tracking algorithms like DeepSORT to accurately follow objects in motion, enhancing the robustness of object detection models. My responsibilities also include validating annotations, ensuring consistency, and cross-referencing annotations for high accuracy. This project contributes to the development of models used in autonomous systems, surveillance, and real-time video analysis.

2024

AI Training Specialist – Data Labeling & Annotation

LabelboxImageBounding BoxClassification

In this project, I am responsible for leading data labeling and annotation tasks for a variety of datasets used in training machine learning models. This includes image, video, and audio data, focusing on object detection, classification, and audio tagging. I utilize tools like Labelbox for annotating images and videos, while YOLO and CVAT are employed for object detection tasks in large video datasets. Audio files are annotated using tools like Audacity and Praat to support speech-to-text and voice recognition models. The project ensures high-quality data through robust quality control measures, including cross-checking and validation of annotations. The project involves working with large-scale datasets and has resulted in improved model accuracy and performance, particularly in object recognition and speech transcription tasks.

In this project, I am responsible for leading data labeling and annotation tasks for a variety of datasets used in training machine learning models. This includes image, video, and audio data, focusing on object detection, classification, and audio tagging. I utilize tools like Labelbox for annotating images and videos, while YOLO and CVAT are employed for object detection tasks in large video datasets. Audio files are annotated using tools like Audacity and Praat to support speech-to-text and voice recognition models. The project ensures high-quality data through robust quality control measures, including cross-checking and validation of annotations. The project involves working with large-scale datasets and has resulted in improved model accuracy and performance, particularly in object recognition and speech transcription tasks.

2021 - 2023

Education

Work History