May 8, 2024, 4:45 a.m. | Svetlana Pavlitska, Oliver Bagge, Federico Peccia, Toghrul Mammadov, J. Marius Z\"ollner

cs.CV updates on arXiv.org arxiv.org

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

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Business Intelligence Analyst Insights & Reporting

@ Bertelsmann | Hilversum, NH, NL, 1217WP