Strukturiertes Filter Pruning angewendet auf Mask R-CNN: Ein Weg zur effizienten Bildsegmentierung
DOI:
https://doi.org/10.26034/lu.akwi.2024.4673Schlagworte:
Pruning, Filter Pruning, Mask R-CNN, Bildsegmentierung, Zweistufige DetektorenAbstract
Die Studie erforscht die Effizienzsteigerung zweistufiger Objekterkennungssysteme mittels fortschrittlicher maschineller Lernmethoden. Trotz hoher Genauigkeit dieser Systeme besteht das Problem der Überparametrisierung und hohen Rechenanforderungen. Es wird eine auf Mask R-CNN zugeschnittene Pruning-Methode entwickelt, welche Modellkomplexität und Speicherbedarf verringert, ohne die Leistung zu beeinträchtigen. Eine globale Kernel-Level-Pruning-Strategie, identifiziert durch die L1-Norm, eliminiert überflüssige Parameter post-Training, wobei die Genauigkeit bei bis zu 40% Pruning stabil bleibt. Mit einer Kompressionsrate von 1,25 und einem IoU-Wert von 0,72 zeigen die Ergebnisse, dass der Ansatz die Effizienz von neuronalen Netzen verbessern kann, ohne deren Erkennungsgenauigkeit zu mindern, und leisten damit einen Beitrag zur Diskussion über KI-Optimierung.
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