Understanding the Constraints
Mobile devices have limited computational resources compared to servers. CPU and GPU capabilities vary widely across device generations and manufacturers. Memory is constrained, and battery life is a primary concern for users. These factors shape every decision in mobile computer vision development.
We begin each project by defining acceptable performance thresholds. What inference time is tolerable for the use case? What accuracy level meets user expectations? These questions guide model selection and optimization strategies.
Model Selection and Optimization
Choosing the right model architecture is fundamental. MobileNet, EfficientNet, and similar architectures are designed for mobile deployment. They achieve reasonable accuracy with significantly fewer parameters than larger models.
Quantization reduces model size and inference time by using lower-precision arithmetic. We typically use INT8 quantization for production models, which can reduce model size by 75% with minimal accuracy loss. Post-training quantization is straightforward; quantization-aware training yields better results when accuracy is critical.
Camera and Image Processing
Camera integration requires attention to resolution, frame rate, and color space handling. Higher resolutions provide more detail but increase processing time. We typically capture at medium resolution and downsample for inference, preserving original images when needed for user display.
Preprocessing steps like normalization and resizing should be optimized. GPU-accelerated image processing reduces CPU load and improves overall performance. Platform-specific APIs often provide hardware-accelerated image operations.
Quality Assurance Practices
Testing computer vision features requires diverse datasets that represent real-world conditions. Lighting variations, camera angles, motion blur, and subject diversity all affect model performance. We maintain test datasets that cover these variations.
Automated testing pipelines run inference on test datasets and track accuracy metrics over time. Regression testing catches performance degradation when models or preprocessing code changes. Manual testing complements automated testing for subjective quality assessment.
Monitoring in Production
Production monitoring helps identify issues that testing did not catch. We track inference times, error rates, and user feedback patterns. Aggregated data reveals device-specific issues and edge cases that require attention.
A/B testing allows controlled rollout of model updates. We can compare new model performance against the current version before full deployment, reducing risk of quality regressions.