npu-recognition

What problem does it solve?

Deploying face and identity recognition models on NPU hardware often faces challenges with quantization accuracy loss, latency failures, and integration complexity, making it difficult to meet production-grade performance and reliability requirements.

Core Features & Use Cases

• End-to-End NPU Deployment Workflow: Covers pipeline setup, model quantization, accuracy verification, and system integration for face recognition use cases.
• Production-Grade Validation: Built-in FAR/FRR metric checks and embedding drift thresholds to ensure models meet strict accuracy and latency requirements before deployment.
• Use Case: Deploy a face access control system for an embedded smart camera that verifies user identities in under 50ms with a false accept rate below 0.001.

Quick Start

Use the npu-recognition skill to deploy an ArcFace face recognition model to NPU hardware with INT8 quantization and verify it meets the 0.001 FAR threshold on the LFW dataset.