Farida Ashraf

Key Skills

Software

Argilla

CVAT

Data Annotation Tech

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Image

Top Label Types

Audio Recording

Bounding Box

Segmentation

Text Generation

Text Summarization

Transcription

Freelancer Overview

I am a dedicated data annotation specialist with hands-on experience in labeling and curating high-quality datasets for AI and machine learning projects. My background includes working remotely with international companies such as Transperfect UAE and iMerit (Roboflow), where I annotated large volumes of text, images, and audio data, ensuring accuracy and consistency to support model training. I have a strong foundation in GIS and surveying, with proficiency in tools like ArcGIS and QGIS, and I am skilled in data entry, database management, and quality assurance. My attention to detail, ability to meet deadlines, and commitment to adopting best practices allow me to deliver precise and reliable training data for a variety of domains. I am fluent in English, Arabic, and German, and I thrive in collaborative, fast-paced environments.

Labeling Experience

Retail Product Detection Roboflow

RoboflowImageBounding Box

Managed a specialized data labeling project on Roboflow aimed at automating inventory tracking in retail environments. Specifically, I performed precise Bounding Box annotation for various product categories. I also utilized Roboflow’s augmentation tools (like rotation and brightness adjustments) to enhance dataset diversity. The project included labeling over 1,000 images, maintaining a high accuracy rate through consistent self-QA and adherence to 'tight-fitting' box standards to ensure the model distinguishes products from shelf backgrounds.

Managed a specialized data labeling project on Roboflow aimed at automating inventory tracking in retail environments. Specifically, I performed precise Bounding Box annotation for various product categories. I also utilized Roboflow’s augmentation tools (like rotation and brightness adjustments) to enhance dataset diversity. The project included labeling over 1,000 images, maintaining a high accuracy rate through consistent self-QA and adherence to 'tight-fitting' box standards to ensure the model distinguishes products from shelf backgrounds.

2025 - 2025

Traffic Signs

CVATImageBounding Box

This project involved the meticulous annotation of traffic signs to support the development of an autonomous driving perception system using the YOLO framework. Project Scope & Tasks: Annotated various road signs (Speed limits, Stop signs, Yield, etc.) across diverse environments. Performed 2D Bounding Box annotation with precise localization for each object. Project size Successfully processed a dataset of [2000+ high-resolution images. Quality measures Adhered to strict 'pixel-perfect' tight-fitting box standards to ensure high model accuracy. Conducted regular self-QA (Quality Assurance) to maintain over 98% label precision

This project involved the meticulous annotation of traffic signs to support the development of an autonomous driving perception system using the YOLO framework. Project Scope & Tasks: Annotated various road signs (Speed limits, Stop signs, Yield, etc.) across diverse environments. Performed 2D Bounding Box annotation with precise localization for each object. Project size Successfully processed a dataset of [2000+ high-resolution images. Quality measures Adhered to strict 'pixel-perfect' tight-fitting box standards to ensure high model accuracy. Conducted regular self-QA (Quality Assurance) to maintain over 98% label precision

2025 - 2025

Education

Work History