Case Study

Increased Accuracy of Relevant Image Retrieval by Up to 25% with Custom Vision Transformer Model

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Industry 

Trade and Investment

Expertise 

Amazon VPC, Amazon S3, Amazon CloudWatch, AWS IAM, Amazon SageMaker

Offerings/solutions 

Optimized performance with faster retrieval, increased accuracy, scalable handling, and alignment with client goals.

About the Client

The public sector agency, established in 2019 from the Department of Industrial Policy & Promotion, drives industrial growth and development, aligns policies with national priorities, and facilitates foreign direct investment (FDI). It oversees industrial policy, including internal trade, to strengthen the nation’s industrial landscape.

Highlights

2 Seconds

Reduced Image Retrieval Time

25%

Increase in Accuracy for Similar Image Retrieval

Increased Load

Highly Scalable Solution

The Challenge

The client faced challenges with their existing system for managing Intellectual Property Rights, including delayed searches for similar images and lower accuracy in retrieving relevant images from the repository. They needed a solution that would retrieve images within 2 seconds and provide Vienna code suggestions for new brand registrations.

Solutions

• Migrated data from on-premises Oracle to Amazon S3 using AWS DMS.
• Preprocessed images in Amazon S3 with Amazon SageMaker Processing Jobs.
• Trained and optimized a custom vision transformer model, storing artifacts in Amazon S3.
• Built and deployed a custom container image for a real-time endpoint on Amazon EC2.
• Deployed the model as a real-time endpoint with Amazon SageMaker.
• Integrated the endpoint with FastAPI, handling requests with Amazon EC2.
• Used Amazon Bedrock for text extraction from images and passed it to the model for search results.
• Monitored performance with Amazon CloudWatch, analyzing metrics like latency and resource utilization.
• Managed access with AWS IAM, creating roles and policies for secure and efficient resource use.

The Results

Enhanced system efficiency by reducing image retrieval time to under 2 seconds, boosted accuracy by 25%, and scaled to handle increased loads, leading to higher customer satisfaction and alignment with business objectives.

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