computer vision / 2025
Computer Vision Science Centre Optimization
Optimized CV models for a Science Centre, reaching 98% classification accuracy under strict memory constraints.
Problem
A computer vision exhibit needed higher reliability while staying within strict deployment constraints.
Role
Optimized the CV pipeline, reworked backend architecture, and applied model quantization to balance memory and accuracy.
Result
Reached 98% classification accuracy while keeping the model practical for resource-constrained deployment.
case study
What was built
At CAWIL.AI, optimized computer vision models for a Science Centre exhibit by restructuring the backend architecture and implementing model quantization to meet strict memory constraints while pushing classification accuracy to 98%. Balanced the trade-off between model size and accuracy for real-world deployment on resource-limited hardware.
Highlights
- 98% classification accuracy under memory constraints
- Model quantization for resource-limited deployment
- Backend architecture restructuring for performance
Best Audience
Technology Stack
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