npu-detection

You can deploy YOLO object detection models to NPU hardware using an end-to-end pipeline that covers ONNX validation, quantization, and accuracy verification. This skill enforces a strict maximum 2% mAP drop after quantization with fallbacks like mixed-precision to preserve inference accuracy.

The best way to quantize YOLO defect detection models is using a calibration dataset to tune the inference parameters for edge AI cameras. This process includes configurable NMS parameters to handle edge cases like small or overlapping objects, ensuring real-time low-latency performance on embedded devices.

Yes, this NPU deployment workflow fully supports RKNN, TFLite, and TensorRT runtimes. It covers the full pipeline from ONNX model validation and input dimension checks to quantization and post-processing tuning specifically for these embedded NPU runtime environments.

YOLO models often lose mAP after NPU quantization due to improper calibration and lack of accuracy guardrails. You can fix this by enforcing a maximum 2% mAP drop threshold and applying fallback options such as mixed-precision quantization or Quantization-Aware Training to prevent performance degradation.

You should use Quantization-Aware Training instead of post-training quantization when your NPU inference accuracy delta exceeds the strict 2% mAP threshold. It serves as a fallback option to prevent performance degradation when standard calibration datasets and mixed-precision quantization fail to meet accuracy requirements.

Yes, you can configure NMS parameters for small or overlapping objects during edge AI deployment. This skill supports configurable NMS parameters during post-processing tuning to handle edge cases and ensure high-throughput NPU inference for industrial or security camera systems.