Anthony Seran

Key Skills

Software

CrowdSource

CVAT

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Geospatial Tiled Imagery

Image

Medical Dicom

Text

Video

Top Label Types

Bounding Box

Classification

Computer Programming Coding

Data Collection

Object Detection

Polygon

Prompt Response Writing SFT

RLHF

Segmentation

Text Generation

Freelancer Overview

I am a data annotation specialist with over three years of hands-on experience working on image, video, and point cloud labeling for computer vision applications. My expertise includes using CVAT for bounding boxes, polygon segmentation, object detection, image classification, and video annotation, as well as performing quality control reviews to ensure annotation accuracy and consistency. I have collaborated closely with data scientists and machine learning engineers, contributed to the development of clear annotation guidelines, and maintained high inter-annotator agreement rates on large-scale projects. My background as a Computer Science student and research assistant has given me a strong foundation in computer vision concepts, data preprocessing, and model evaluation. I am committed to delivering precise, high-quality datasets that support effective AI and machine learning solutions.

Labeling Experience

PROJECT 1 — Image Annotation (Bounding Boxes & Polygon Segmentation)

CVATImageBounding BoxPolygon

• Annotated 3,000+ images using CVAT with bounding boxes and polygon segmentation for object detection model training • Conducted quality control reviews of fellow annotators work, ensuring 95%+ accuracy rate across all annotated datasets • Documented comprehensive annotation guidelines adopted by the full annotation team • Performed data preprocessing including image resizing and format standardization for ML pipeline compatibility • Upheld consistency across all annotations by strictly following established project rules and guidelines

• Annotated 3,000+ images using CVAT with bounding boxes and polygon segmentation for object detection model training • Conducted quality control reviews of fellow annotators work, ensuring 95%+ accuracy rate across all annotated datasets • Documented comprehensive annotation guidelines adopted by the full annotation team • Performed data preprocessing including image resizing and format standardization for ML pipeline compatibility • Upheld consistency across all annotations by strictly following established project rules and guidelines

2023

Education

Work History