About This Architecture

ResNet34-UNet with CBAM attention mechanism powers this end-to-end image segmentation pipeline, flowing from user request through WAF and CDN to a React SPA frontend. The API Gateway routes authenticated requests to a REST backend that preprocesses images and orchestrates inference via a dedicated Model Serving tier, which loads the model from a Model Registry and executes the segmentation. Output processing writes masks to File Storage and metadata to a cached Database, while original images persist in Object Storage, creating a scalable, multi-tier architecture for production computer vision workloads. This pattern demonstrates best practices for model serving, request authentication, and result caching that reduce latency and improve throughput. Fork this diagram on Diagrams.so to customize for your inference framework, add monitoring layers, or adapt the data flow for batch processing. The CBAM module enhances spatial and channel attention, making this architecture suitable for high-precision segmentation tasks requiring fine-grained feature refinement.

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How do you architect a production image segmentation pipeline with ResNet34-UNet and CBAM attention?

This diagram shows a multi-tier architecture where user requests flow through WAF and CDN to a React SPA, then to an API Gateway and REST backend that authenticates and preprocesses images. The Inference Tier loads the ResNet34-UNet+CBAM model from a Model Registry and executes segmentation, with output processing writing masks to File Storage and metadata to a cached Database.