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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