Cloud Technologies
Image Label Generator
This project leverages AWS Rekognition to detect and label objects in images stored in an Amazon S3 bucket. Built with Python and Boto3, it integrates AWS services for seamless image analysis and object detection. The application generates bounding boxes around detected objects and provides confidence scores for each label. Using Matplotlib, results are visualized for better interpretation.
My Role
Cloud Solutions Architect
Duration
1 year
Tools
AWS Rekognition, AWS S3, AWS CLI, Python, Boto3, Matplotlib
Link
Overview
Image Processing Efficiency – Handling large image datasets efficiently while ensuring quick and accurate labeling.
AWS Service Integration – Configuring AWS Rekognition, S3, and CLI to work seamlessly without performance bottlenecks.
Data Visualization – Effectively displaying detected objects and confidence scores in an interpretable way.
Security & Access Control – Managing AWS permissions to securely store and process image data.
Optimized AWS Rekognition Usage – Implemented efficient API calls to minimize processing time and cost.
Automated AWS Configuration – Used Boto3 and AWS CLI to streamline bucket setup, data storage, and model execution.
Enhanced Data Presentation – Integrated Matplotlib to overlay bounding boxes and display confidence scores visually.
Improved Security Practices – Configured IAM roles and bucket policies to restrict unauthorized access.
