AI-Powered Image Tagging & Annotation for Computer Vision by Nandan Vallamdasu

AI-Powered Image Tagging & Annotation for Computer Vision

Nandan Vallamdasu

Contact for pricing

About this service

Summary

This image annotation service delivers high-accuracy, model-ready datasets tailored for computer vision applications. Unlike generic crowd-sourced labeling, all annotations are handled directly with strict quality control. Key differentiators include:

Manual precision combined with optional automation for scalability

Support for multiple formats (COCO, YOLO, VOC, custom)

Consistency enforced through strict annotation guidelines

Quality assurance with overlap checks and optional IoU validation

Use of modular, open-source tools like CVAT, Label Studio, or custom scripts

Custom class schema and instruction adaptation per project requirements

Full transparency with revision logs, metadata, and dataset reports

Process

Receive project brief and sample images

Define annotation schema and format (COCO, YOLO, VOC, custom)

Perform manual and/or semi-automated image labeling

Conduct quality checks (consistency, overlap, IoU validation if needed)

Deliver annotated dataset, format report, and visual previews

Apply revisions (if requested), then finalize and close project

FAQs

What tools do you use for annotation?
CVAT, Label Studio, Azure, or custom Python scripts based on the task.
Can you follow a custom labeling guideline?
Yes, all guidelines are strictly followed after client approval.
Do you support large-scale datasets?
Yes, including batch delivery and milestone-based review.
What formats can you export annotations in?
COCO, YOLO, Pascal VOC, CSV, JSON, or any custom format.

What's included

Annotated Image Dataset
Labeled images in the requested format (COCO, YOLO, VOC, CSV, JSON) with accurate annotations based on your specifications, ready for direct use in ML training pipelines.
Visual Preview Files
Overlay images showing annotations on original images for visual verification, delivered in PNG or JPEG format.
Bounding Box Annotations
Precisely labeled bounding boxes around objects of interest within images, provided in your chosen format (COCO, YOLO, Pascal VOC, or custom schema), structured for compatibility with object detection models.
Metadata File
Supplementary file containing image-level metadata: image dimensions, tag distributions, annotation counts, and class frequencies.

Skills and tools

Data Engineer

Data Entry Specialist

Data Science Specialist