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Ayomide

Agency

Founder & Lead Data Annotation Specialist — LabelFast (AI training data)

Lagos, Nigeria

labelfastt.netlify.app/

Key Skills

Software

CVAT

Labelbox

Top Subject Matter

Computer vision for e-commerce/retail product recognition and object detection

Computer vision segmentation labeling for AI training

Multilingual NLP training data (African languages) for NER and related tasks

Top Data Types

Image

Text

Audio

Document

Top Task Types

Bounding Box

Polygon

Segmentation

Evaluation/Rating

Fine-tuning

Company Overview

Founder & Lead Data Annotation Specialist — LabelFast (AI training data). Core strengths include CVAT. Education includes Bachelor of Science, Afe Babalola University. AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, Segmentation, and Entity (NER).

Security

Security Overview

Label Fast takes data security and client confidentiality seriously. We implement the following measures to protect your data: - All annotators sign a Non-Disclosure Agreement (NDA) before any work begins. - Client data is stored securely in private Google Drive folders with restricted access. - Access to client data is limited only to annotators actively working on that project. - We do not share, publish, or reproduce any client data. - All labeled data is delivered via secure file transfer and is not retained after delivery unless requested. - Annotators are trained on data privacy best practices. We understand that your training data is proprietary and valuable. We treat it as such. If a client requires specific security protocols (e.g., VPN access, encrypted file transfer, specific NDAs), we are happy to accommodate their requirements. We are a small, focused team, which means we have full visibility and control over who accesses client data. There are no third-party sub-processors involved in our workflow.

Labeling Experience

Founder & Lead Annotator — Multilingual African Language NLP Labeling (LabelFast)

CVAT

Text

Supports multilingual AI training data for African language NLP projects using native-context text annotation. Offers labeling capabilities for tasks such as NER, sentiment analysis, intent classification, and hate speech detection across Yoruba, Hausa, Igbo, and Nigerian Pidgin. Produces model-ready labeled text for multilingual AI deployments targeting African markets.• Native-speaker annotation in Yoruba, Hausa, Igbo, and Nigerian Pidgin.• Text labeling for NER and other classification-style NLP tasks.• Domain-focused guidance for under-resourced African language datasets.• Multilingual labeling service delivered as part of AI training data projects.

2025 - Present

Data Annotation Specialist — Polygon Segmentation (LabelFast)

CVAT

Image

Segmentation

Provides polygon segmentation labeling support for computer vision model training through CVAT. Produces annotation outputs suitable for segmentation workflows used by ML teams. Ensures quality through batch spot-check QC and instruction-driven consistency.• Polygon segmentation annotations using CVAT polygon tools.• Batch-level QC via 10% spot-check before delivery.• Consistency enforcement through per-project labeling instruction guides.• Output compatibility through COCO JSON export alongside other formats.

2025 - Present

Founder & Lead Data Annotation Specialist — LabelFast (AI training data)

CVAT

Image

Bounding Box

Founded and leads LabelFast, delivering high-accuracy computer vision training datasets for e-commerce and retail product recognition. Personally performs tight bounding box object detection annotations in CVAT while maintaining labeling consistency across projects. Exports client-ready results in common ML formats for downstream training and evaluation.• Performed 500+ image bounding box annotation projects with 99%+ accuracy.• Managed sub-pixel box tightness standards and internal QC checks.• Delivered datasets in COCO JSON and YOLO formats via CVAT.• Created and maintained labeling instruction guides with class rules and edge cases.

2025 - Present

Product Bounding Box Annotation for E-Commerce AI Model

CVAT

Image

Bounding Box

Label Fast completed a product detection project for an e-commerce AI training dataset. The goal was to provide tight bounding box annotations around products in retail images to train a computer vision model. Project Scope: - 500 product images annotated with tight bounding boxes - Single-class labeling (product detection) - Images varied in background complexity, lighting, and product orientation Quality Measures: - All annotations were completed using CVAT - 100% of annotations were manually reviewed by a dedicated QC lead - 10% spot-check with a 99% accuracy target - Every box was drawn tight to the object edges with zero gaps Deliverables: - Labeled data exported in COCO JSON format - QC report provided to client Outcome: The client received clean, production-ready training data with zero errors identified in the QC process. The dataset contributed to a successful model deployment.

2026 - 2026