Model Pruning in Depth Completion CNNs for Forestry Robotics with Simulated AnnealingDownload PDF

Published: 03 Jun 2022, Last Modified: 23 May 2023IFRRIA OralReaders: Everyone
Keywords: Model Compression, Model Pruning, Depth Completion, Forestry Robotics, Neural Network
TL;DR: In this paper, we analyze the benefits of model compression in depth completion neural networks which uses RGB and Depth maps as input
Abstract: In this article, we present an analysis of model compression in depth completion neural networks for forestry robotics, considering the increasing demands of real time autonomous solutions. Specifically, we implement a single state simulated annealing meta-heuristic for model pruning in the ENet and MSG-CHN neural networks for depth completion. We run experiments in three different datasets and analyze how different levels of pruning affect the accuracy and speed of the models. Experimental tests show that increasing sparsity has different effects depending on the neural network and dataset. ENet has neglectable difference in accuracy and it would greatly benefit from lowering the amount of FLOPs, while MSG-CHN displays an inconsistent behavior depending on the dataset. This suggests that while both models benefit from model compression techniques, the optimal sparsity level depends on environment, dataset and neural network.
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