Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Emotion Recognition

Freelancer Overview

I am a detail-oriented AI Training Specialist with 4 years of hands-on experience in data labeling and annotation for computer vision and speech recognition projects. My expertise spans annotating and labeling image, video, and audio datasets using tools like Labelbox, CVAT, and SuperAnnotate, with a strong focus on object detection, tracking, segmentation, and keypoint annotation. I am proficient in using YOLO frameworks for object detection and tracking, and I have a solid foundation in TensorFlow and PyTorch. I excel at maintaining high annotation accuracy through rigorous quality assurance, optimizing labeling workflows, and collaborating with data scientists and ML engineers to ensure datasets meet the highest standards. My background in computer science and information technology, along with strong documentation and workflow optimization skills, allows me to consistently deliver high-quality training data that enhances machine learning model performance.

Labeling Experience

Multi-Modal AI Data Annotation for Object Detection & Audio Classification

LabelboxVideoBounding BoxPoint Key Point

Project Description Worked on a large-scale video annotation project designed to support the development of advanced computer vision models for object detection and tracking. The project focused on accurately labeling dynamic scenes to improve model performance in real-world environments. My responsibilities included frame-by-frame video annotation using bounding boxes and polygon segmentation, tracking multiple objects across sequences, and labeling over 15 object categories such as vehicles, pedestrians, and environmental elements. I also applied keypoint annotations for motion and pose estimation tasks where required. The project involved processing thousands of video clips and annotating over 100,000 frames. I followed strict annotation guidelines to ensure consistency and precision across sequences. Quality assurance measures included double-review checks, IoU (Intersection over Union) validation, temporal consistency verification for tracking tasks, and continuous feedback imp

Project Description Worked on a large-scale video annotation project designed to support the development of advanced computer vision models for object detection and tracking. The project focused on accurately labeling dynamic scenes to improve model performance in real-world environments. My responsibilities included frame-by-frame video annotation using bounding boxes and polygon segmentation, tracking multiple objects across sequences, and labeling over 15 object categories such as vehicles, pedestrians, and environmental elements. I also applied keypoint annotations for motion and pose estimation tasks where required. The project involved processing thousands of video clips and annotating over 100,000 frames. I followed strict annotation guidelines to ensure consistency and precision across sequences. Quality assurance measures included double-review checks, IoU (Intersection over Union) validation, temporal consistency verification for tracking tasks, and continuous feedback imp

2022

Multi-Modal AI Data Annotation for Object Detection & Audio Classification

LabelboxImageBounding BoxPoint Key Point

Project Description* Led a multi-modal data annotation project supporting the development of computer vision and audio recognition models. The project involved labeling over 250,000+ image and video frames and 5,000+ audio clips to train object detection, tracking, and classification algorithms. Key Responsibilities: Performed bounding box, polygon, and segmentation annotations for vehicles, pedestrians, and dynamic objects Conducted frame-by-frame video object tracking using YOLO-based detection pipelines Annotated keypoints for human pose estimation tasks Completed audio transcription and emotion recognition labeling for speech datasets Applied strict annotation guidelines to ensure 98%+ accuracy rate Conducted peer review and quality assurance checks before final dataset submission Assisted ML engineers in refining datasets to improve model precision and recall Project Scale: 250K+ images & video frames 5K+ annotated audio clips Multi-class object detection (20+ categor

Project Description* Led a multi-modal data annotation project supporting the development of computer vision and audio recognition models. The project involved labeling over 250,000+ image and video frames and 5,000+ audio clips to train object detection, tracking, and classification algorithms. Key Responsibilities: Performed bounding box, polygon, and segmentation annotations for vehicles, pedestrians, and dynamic objects Conducted frame-by-frame video object tracking using YOLO-based detection pipelines Annotated keypoints for human pose estimation tasks Completed audio transcription and emotion recognition labeling for speech datasets Applied strict annotation guidelines to ensure 98%+ accuracy rate Conducted peer review and quality assurance checks before final dataset submission Assisted ML engineers in refining datasets to improve model precision and recall Project Scale: 250K+ images & video frames 5K+ annotated audio clips Multi-class object detection (20+ categor

2020 - 2022

Education

Work History