You will get accurate image annotation for computer vision datasets
Project details
I will provide accurate image annotation for computer vision datasets using clear labeling rules and a structured quality-control process.
I can annotate images with bounding boxes, image-level classification, or polygon segmentation based on the agreed package and project scope. Your dataset can be delivered in YOLO, COCO JSON, Pascal VOC, CSV, or another agreed format.
My process includes reviewing your sample images and class definitions, confirming difficult cases, annotating the dataset consistently, checking for missing or incorrect labels, and organizing the final files for easy use in model training.
Package limits are based on the number of images, classes, and objects per image listed in each tier. Crowded scenes, unclear images, keypoints, video frames, complex segmentation, or major guideline changes may require a custom quote.
You will receive the annotated images, label files, class list, and quality-review notes included in your selected package.
I can annotate images with bounding boxes, image-level classification, or polygon segmentation based on the agreed package and project scope. Your dataset can be delivered in YOLO, COCO JSON, Pascal VOC, CSV, or another agreed format.
My process includes reviewing your sample images and class definitions, confirming difficult cases, annotating the dataset consistently, checking for missing or incorrect labels, and organizing the final files for easy use in model training.
Package limits are based on the number of images, classes, and objects per image listed in each tier. Crowded scenes, unclear images, keypoints, video frames, complex segmentation, or major guideline changes may require a custom quote.
You will receive the annotated images, label files, class list, and quality-review notes included in your selected package.
Machine Learning Tools
NumPy, OpenCV, pandas, Python, PyTorch, Tesseract OCRWhat's included
| Service Tiers |
Starter
$20
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Number of Model Variations | 0 | 0 | 0 |
Number of Scenarios | 0 | 0 | 0 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$15
Additional 100 images
(+ 1 Day)
+$20
Polygon segmentation upgrade
(+ 2 Days)
+$50
Detailed dataset QA report
(+ 1 Day)
+$20Frequently asked questions
About Vu
AI Automation Engineer | n8n, LLM, APIs & PostgreSQL
Ho Chi Minh City, Vietnam - 10:23 pm local time
I build reliable AI automation, n8n workflows, and data pipelines for real business operations.
My core services include:
• n8n workflow development, debugging, and optimization
• REST API, webhook, email, database, and cloud integrations
• AI document processing using OCR and LLM extraction
• Human-in-the-loop review and approval workflows
• RAG assistants and LLM-powered business tools
• PostgreSQL, ETL, data validation, and system synchronization
• Custom Python and JavaScript logic for complex workflows
I have hands-on experience building production automation for purchase orders, delivery notes, invoices, inventory operations, document extraction, duplicate detection, review queues, database synchronization, scheduled reporting, and AI usage monitoring.
My AI experience also includes:
• A real-time food detection and classification system using YOLOE, MobileCLIP, FastAPI, and WebSocket
• A healthcare document assistant using RAG and LLMs
• Computer vision data preparation, annotation, validation, and dataset quality control
I focus on building systems that are:
• Reliable and maintainable
• Protected against duplicate processing
• Equipped with validation, retries, and error handling
• Clearly documented for future maintenance
• Designed around your existing business process
Send me your current workflow, the systems you need to connect, and the expected result. I will help you define a practical solution before implementation.
Steps for completing your project
After purchasing the project, send requirements so Vu can start the project.
Delivery time starts when Vu receives requirements from you.
Vu works on your project following the steps below.
Revisions may occur after the delivery date.
Review Dataset and Guidelines
I review your sample images, class list, annotation type, labeling rules, output format, and package scope.
Confirm Annotation Rules
I confirm how objects should be labeled, including unclear, partially visible, overlapping, or difficult cases.