Calibration Data Study

Key Insight

Post-training quantization depends on representative calibration data to determine the optimal scaling factors for weights and activations. Sweeping the number of calibration samples demonstrates how the quality of a quantized model converges as more representative input variety is observed. Understanding this relationship helps developers optimize the calibration phase, ensuring maximum accuracy recovery with minimal computational overhead.