Dariia M.

Key Skills

Software

CVAT

Label Studio

Top Subject Matter

Healthcare & Medical Data Annotation

E-commerce & Product Data Categorization

Computer Vision & Image Labeling

Top Data Types

Image

Video

Text

Top Task Types

Bounding Box

Polygon

Segmentation

Object Detection

Text Generation

Freelancer Overview

My experience in data annotation is rooted in the precise processing and structuring of large volumes of data for training AI models. I've successfully completed thousands of tasks on crowdsourcing platforms preparing training datasets for computer vision (bounding box mapping, polygon manipulation) and natural language processing (text classification, sentiment analysis, and content moderation). The main advantage that sets me apart from other candidates is maintaining a consistently high annotation accuracy of 98%. I can quickly understand complex and extensive technical instructions, easily master professional annotation software (such as CVAT or Label Studio), and effectively work with diverse data types (text, images, audio), ensuring the quality engineers need to successfully train neural networks.

Labeling Experience

Label Your Data

ImageObject Detection

High-precision pixel-by-pixel labeling and annotation of graphic data (images and videos) for training neural networks in the fields of autonomous vehicles and object recognition. Key tasks: - Complex semantic segmentation of urban and road scenes at the pixel level. - Labeling of irregularly shaped objects using multipoint polygons and contour lines. - Keypoint tracking and marking for gesture and pose recognition systems. Project size: Labeling and validation of over 8,000 highly detailed images. Quality assurance: Strict adherence to pixel accuracy, successful completion of rigorous engineering checks, and maintaining a final project QA score of 98%.

2025 - Present

Mindy Teams

ImageSegmentation

Labeling and annotating large-scale multimodal datasets for training computer vision models and AI applications. Key tasks: - Accurate superposition of 2D/3D bounding boxes, working with multipoint polygons, and semantic segmentation of objects in images. - Classification, tagging, and structuring of visual content according to strict technical guidelines. Project size: processing over 10,000 objects. Quality assurance: strict adherence to engineers' instructions, verified labeling accuracy of 98%, minimization of errors in training dataset preparation.

2024 - 2025

Education

Work History