HumP-KD

HumP-KD: Uncertainty-aware multi-stage knowledge distillation for efficient fire classification

Researchers propose HumP-KD, a knowledge distillation framework that compresses two heterogeneous transformer teachers (Swin-Tiny and ViT-Base) into a lightweight MobileViT-S student for real-time fire classification. The student model achieves 0.9876 mean F1 on a 31K-image dataset while retaining only 4.94M parameters—a 5.7× reduction over Swin-Tiny—and runs at 37.72 CPU FPS. The framework combines hierarchical feature alignment, spatial attention masking, and progressive multi-stage distillation to maintain accuracy under degraded visual conditions.