Iterative Filter Pruning for Concatenation-based CNN Architectures

arXiv:2405.03715v1 Announce Type: new
Abstract: Model compression and hardware acceleration are essential for the resource-efficient deployment of deep neural networks. Modern object detectors have highly interconnected convolutional layers with concatenations. In this work, we study how pruning can be applied to such architectures, exemplary for YOLOv7. We propose a method to handle concatenation layers, based on the connectivity graph of convolutional layers. By automating iterative sensitivity analysis, pruning, and subsequent model fine-tuning, we can significantly reduce model size both in …

architectures arxiv cnn cs.cv filter iterative pruning type