RKNN Quantization in Practice: Choosing a Calibration Set and Controlling Accuracy Loss
Quantization compresses FP32 weights to INT8 with big speed and memory wins, but a calibration set mismatched to your scene collapses accuracy.
The calibration set is the soul of quantization
- Calibrate from real cameras: 200-500 frames covering day/night, weather and angles beat generic datasets;
- Per-layer sensitivity: keep accuracy-critical head/tail layers in FP16 mixed quantization;
- Always evaluate after quantizing: compute mAP on a real validation set; a 2-3% drop means fix the calibration set, not push through.
Version-control the calibration set and quantization config — you will need them when the model retrains in three months.
Related products: RP01A dev board, RM182X 20-TOPS AI module. Datasheets, firmware and SDKs are free at the Hugsun Documentation Center; for selection advice or volume pricing, talk to us.
