AI-Based Object Recognition Using Rekognition
Developed an AI-powered image label generator using Amazon Rekognition to automatically detect and tag objects in uploaded images stored in Amazon S3. The system identifies multiple labels per image along with confidence scores, enabling intelligent image categorization and visual asset recognition.
Amazon Rekognition, Amazon S3, AWS CLI,, AWS IAM, AWS Lambda, Amazon CloudWatch,
Python, Boto3, Matplotlib, PIL (Python Imaging Library)
Computer Vision Modeling Image Recognition Automation, Cloud Architecture Deployment, Python & AWS SDK Integration,
Created a secure S3 bucket in AWS to store and organize sample images for processing. Uploaded multiple images with diverse objects to enhance Rekognition’s accuracy during labeling and detection.
Installed the AWS Command Line Interface (CLI) to interact with AWS services programmatically. Configured access keys, region, and permissions for authenticated Rekognition and S3 operations.
Used Boto3 to initialize the Rekognition client and implement the detect_labels function. The model analyzed each image, detected up to 10 objects, and returned their confidence scores.
Loaded image data from S3 using PIL and visualized results using Matplotlib. Displayed bounding boxes and labels for identified objects directly over the image.
Executed the main Python script to test the end-to-end workflow. Verified Rekognition’s labeling accuracy and ensured consistent detection across images with varying complexity.
Automated Image Labeling System
This solution demonstrates a scalable and cost-effective image recognition pipeline powered by AWS. It enables organizations to automate image tagging, streamline digital asset management, and integrate visual recognition capabilities into enterprise workflows—improving operational efficiency in media, retail, and surveillance applications.