You will get expert AI annotation systems with high-quality training data
Project details
This project goes beyond simple labeling.
I design the entire annotation system, Defining the taxonomy, deciding what to label and how to label it, and selecting the optimal annotation method (bounding boxes, polygons, segmentation, nested classes) based on the model’s end use.
Each class is structured to reduce ambiguity, ensure class balance, and improve downstream OCR and detection accuracy.
I create clear annotation rules, handle edge-case definitions, and maintain high consistency across the dataset through multiple QA passes. The final dataset is fully optimized for training: clean, standardized, and exported in the correct format for YOLO, COCO, or custom pipelines.
By combining strategy, structure, and precise labeling, this project delivers training data that significantly boosts model performance and reduces error rates in real-world applications.
I design the entire annotation system, Defining the taxonomy, deciding what to label and how to label it, and selecting the optimal annotation method (bounding boxes, polygons, segmentation, nested classes) based on the model’s end use.
Each class is structured to reduce ambiguity, ensure class balance, and improve downstream OCR and detection accuracy.
I create clear annotation rules, handle edge-case definitions, and maintain high consistency across the dataset through multiple QA passes. The final dataset is fully optimized for training: clean, standardized, and exported in the correct format for YOLO, COCO, or custom pipelines.
By combining strategy, structure, and precise labeling, this project delivers training data that significantly boosts model performance and reduces error rates in real-world applications.
What's included
| Service Tiers |
Starter
$1,000
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 2 | 1 | 1 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Yasir
Fractional CMO | AI-Powered Marketing, Growth & Demand Generation
Kuala Lumpur, Malaysia - 5:37 am local time
With 13+ years across Asia at DHL, I’ve led large-scale marketing transformation initiatives — increasing Share of Voice by 150%, growing leads by 73% while reducing budget by 25%, and building content ecosystems generating millions of monthly impressions.
I specialize in building scalable growth systems — combining SEO, content, automation, and AI workflows to drive consistent demand, improve customer journeys, and reduce dependency on paid channels.
Steps for completing your project
After purchasing the project, send requirements so Yasir can start the project.
Delivery time starts when Yasir receives requirements from you.
Yasir works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Validation & Taxonomy Design
We clarify the use case, define the class list, outline annotation rules, and choose the best labeling method (boxes, polygons, segmentation).
Pilot Annotation & Test Case Approval
A small batch is annotated to validate edge cases, class balance, labeling logic, and overall annotation quality.