#ImageAnnotation #PolygonLabeling #RecyclableMaterials #AITrainingData #CVAT #COCOFormat #WasteSortingAI #AnnotationPortfolio #MachineLearningDataset #DataAnnotationExpert #SmartAnnotation #SecureAnnotation #EfficientLabeling
Welcome to my professional data annotation portfolio!
In this video, I showcase a sample of my work as an Image Annotation Specialist, specifically focused on polygon-based labeling for recyclable materials using high-resolution images. The goal of this annotation project is to prepare training data for machine learning models used in automated waste sorting systems within industrial environments.
This sample highlights my approach to performing precise polygon segmentation across various recyclable categories such as:
PET Bottles
Aluminum Cans (ALU)
HDPE Containers
Cardboard, Film, and Mixed Plastics
All annotations follow COCO format, ensuring compatibility with most computer vision frameworks.
✅ What You’ll See in This Video:
Annotating recyclable objects using polygon tools in CVAT
Labeling multiple material classes in cluttered scenes
Smart handling of overlapping or visually similar objects
Maintaining segmentation precision on fine edges and contours
Efficient category selection based on waste type guidelines
Real-time demonstration of working with COCO export-ready formatting
Time-saving techniques that preserve quality while boosting speed
Quality assurance practices to review and revise annotations
This video reflects the actual procedure I follow to ensure my work is both technically accurate and efficiently produced — a crucial combination when working at scale for machine learning teams.
🔍 Why This Work Matters for AI Projects:
Well-annotated image datasets are mission-critical for AI models in industrial automation, especially in environmental applications like:
Robotic waste sorting
Sustainability monitoring systems
Material classification and recycling stream optimization
High-precision polygon segmentation makes it possible for these models to detect and distinguish recyclables even in noisy, cluttered images — which is exactly the skill demonstrated in this video.
🚀 Why Hire Me?
I bring deep experience in image annotation for industrial, retail, and scientific use cases. Here’s what I offer:
✅ Expert polygon labeling in tools like CVAT, VIA, and LabelMe
✅ Familiarity with recyclable materials and waste categories (PET, HDPE, ALU)
✅ Ability to label complex scenes with overlapping instances
✅ Clean COCO-formatted exports ready for AI training pipelines
✅ Efficient workflows to deliver large-scale projects on time
✅ Strict data handling, privacy, and QA standards
Whether you’re training an AI to sort waste or detect specific materials, I provide the clean, scalable annotations you need.
📧 Contact me at syed.fakhr-e-ali@piezee.com to collaborate on your next computer vision project.
🛡️ Disclaimer:
This video is a visual sample created solely to showcase my annotation skills. No original client data, images, methods, project details, or proprietary information have been disclosed or shared.
All content shown is either simulated or recorded only for portfolio purposes. If this project is related to one I’ve worked on for you, and you would like this video removed, please contact me at syed.fakhr-e-ali@piezee.com, and I will immediately take it down. I uphold the highest standards of client confidentiality and data security.
