Jasmine Denise Crawford

Key Skills

Software

Labelbox

CVAT

Supervisely

Top Subject Matter

Computer Vision

Speech Recognition

Surveillance Domain Expertise

Top Data Types

Image

Video

Audio

Text

Document

Top Task Types

Bounding Box

Segmentation

Tracking

Transcription

Freelancer Overview

AI Training Specialist. Core strengths include Labelbox, CVAT, and Supervisely. Education includes Bachelor of Science, University of Texas at Dallas (2021) and High School Diploma, Springfield High School (2017). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Bounding Box, Segmentation, and Tracking.

Labeling Experience

AI Training Specialist

LabelboxImageBounding Box

As an AI Training Specialist at DataVision AI Technologies, I led large-scale annotation projects in the computer vision and speech recognition domains. I performed bounding box, polygon, and keypoint annotations and managed multi-object tracking and audio transcription for model training. My work included dataset auditing and close collaboration with ML engineers to refine annotation standards. • Led diverse teams on image, video, and audio annotation projects. • Improved annotation accuracy through enhanced quality assurance workflows. • Conducted precise object detection and multi-object tracking tasks. • Utilized advanced tools, ensuring data compliance and high-quality datasets.

As an AI Training Specialist at DataVision AI Technologies, I led large-scale annotation projects in the computer vision and speech recognition domains. I performed bounding box, polygon, and keypoint annotations and managed multi-object tracking and audio transcription for model training. My work included dataset auditing and close collaboration with ML engineers to refine annotation standards. • Led diverse teams on image, video, and audio annotation projects. • Improved annotation accuracy through enhanced quality assurance workflows. • Conducted precise object detection and multi-object tracking tasks. • Utilized advanced tools, ensuring data compliance and high-quality datasets.

2023 - Present

Computer Vision Data Annotator

CVATImageSegmentation

As a Computer Vision Data Annotator at InnovateAI Labs, I annotated image and video frames for object detection, segmentation, and classification tasks. I structured datasets to align with YOLO training formats and supported dataset preprocessing and quality initiatives. My responsibilities contributed substantially to the precision and usability of computer vision datasets. • Labeled 500,000+ image and video frames for training datasets. • Executed segmentation, classification, and dataset cleanup. • Ensured dataset format compliance and metadata accuracy. • Supported large-scale annotation and ML workflow pipelines.

As a Computer Vision Data Annotator at InnovateAI Labs, I annotated image and video frames for object detection, segmentation, and classification tasks. I structured datasets to align with YOLO training formats and supported dataset preprocessing and quality initiatives. My responsibilities contributed substantially to the precision and usability of computer vision datasets. • Labeled 500,000+ image and video frames for training datasets. • Executed segmentation, classification, and dataset cleanup. • Ensured dataset format compliance and metadata accuracy. • Supported large-scale annotation and ML workflow pipelines.

2022 - 2023

AI Data Annotation Associate

SuperviselyVideoTracking

As an AI Data Annotation Associate at NextWave Analytics, I labeled surveillance and automation AI datasets involving real-time video annotation and object tracking. I performed motion and object tracking tasks, conducted rigorous quality control, and outlined workflow processes. These efforts directly improved AI system accuracy and operational efficiency. • Delivered accurate real-time video motion tracking. • Documented annotation guidelines for team use. • Ensured high annotation precision through meticulous QC. • Supported AI system deployment with quality dataset creation.

As an AI Data Annotation Associate at NextWave Analytics, I labeled surveillance and automation AI datasets involving real-time video annotation and object tracking. I performed motion and object tracking tasks, conducted rigorous quality control, and outlined workflow processes. These efforts directly improved AI system accuracy and operational efficiency. • Delivered accurate real-time video motion tracking. • Documented annotation guidelines for team use. • Ensured high annotation precision through meticulous QC. • Supported AI system deployment with quality dataset creation.

2021 - 2022

Project: Speech Recognition Dataset

AudioTranscription

On the Speech Recognition Dataset project, I transcribed and labeled multilingual audio data, with a focus on training robust speech recognition models. I annotated various speech patterns, tagged background noise, and supported model precision. This work ensured enhanced clarity and recognition accuracy for ML workflows. • Transcribed multilingual speech samples with detailed tags. • Labeled background noise and speech clarity metrics. • Provided precise data for speech model training. • Maintained consistent audio labeling standards.

On the Speech Recognition Dataset project, I transcribed and labeled multilingual audio data, with a focus on training robust speech recognition models. I annotated various speech patterns, tagged background noise, and supported model precision. This work ensured enhanced clarity and recognition accuracy for ML workflows. • Transcribed multilingual speech samples with detailed tags. • Labeled background noise and speech clarity metrics. • Provided precise data for speech model training. • Maintained consistent audio labeling standards.

2020 - 2020

Project: Autonomous Vehicle Object Detection Dataset

CVATImageSegmentation

For the Autonomous Vehicle Object Detection Dataset, I annotated vehicles, pedestrians, and lane markings using bounding box and segmentation techniques. My work improved detection accuracy and contributed to model validation for real-world applications. The project emphasized annotation consistency, standard adherence, and detailed dataset creation. • Labeled diverse traffic-related objects using high standards. • Applied segmentation strategies to challenging environments. • Collaborated with model validation teams to ensure accuracy. • Enhanced detection performance through quality labeling.

For the Autonomous Vehicle Object Detection Dataset, I annotated vehicles, pedestrians, and lane markings using bounding box and segmentation techniques. My work improved detection accuracy and contributed to model validation for real-world applications. The project emphasized annotation consistency, standard adherence, and detailed dataset creation. • Labeled diverse traffic-related objects using high standards. • Applied segmentation strategies to challenging environments. • Collaborated with model validation teams to ensure accuracy. • Enhanced detection performance through quality labeling.

2020 - 2020

Education

Work History