What is model quantization in Computer Vision deployment?

Quantization reduces model precision (e.g., FP32 to INT8) to improve speed and reduce memory.

Quantization maps floating-point weights and activations to lower-bit representations. Post-training quantization or quantization-aware training allows efficient inference on edge devices with minimal accuracy loss. It significantly reduces latency and model size.