Julia Sermuksnis

Key Skills

Software

CVAT

Labelbox

LabelImg

Roboflow

Scale AI

SuperAnnotate

Supervisely

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

Image

Medical Dicom

Video

Top Task Types

Bounding Box

Object Detection

Point Key Point

Polygon

Segmentation

Freelancer Overview

I am a Data Labeling Specialist with experience in annotating images and videos for AI and computer vision applications. My work includes object identification, bounding box creation, segmentation, and visual categorization to support high-quality training datasets. I am skilled in tools such as Labelbox, CVAT, SuperAnnotate, and Roboflow, and I bring strong attention to detail, consistency, and accuracy to every project. I excel in visual understanding, pattern recognition, and analytical thinking, which allows me to contribute effectively to AI training workflows. My background in technology and software engineering enhances my ability to understand model requirements and adapt quickly to new annotation guidelines. I am committed to delivering precise, high-quality labeled data that improves model performance and supports reliable machine learning development.

Labeling Experience

Image and Video Annotation for Computer Vision Models

LabelboxImage

I worked on an image annotation project focused on supporting computer vision training datasets. My tasks included creating bounding boxes, polygon outlines, segmentation masks, and identifying multiple objects within images. I followed detailed guidelines to ensure accuracy, consistency, and high-quality annotations suitable for machine learning models. The project required strong attention to detail and precise labeling to improve model performance.

I worked on an image annotation project focused on supporting computer vision training datasets. My tasks included creating bounding boxes, polygon outlines, segmentation masks, and identifying multiple objects within images. I followed detailed guidelines to ensure accuracy, consistency, and high-quality annotations suitable for machine learning models. The project required strong attention to detail and precise labeling to improve model performance.

2023 - 2025

Education

Work History