Removing fire modules from SqueezeNet yields much smaller malaria classifiers with a modest accuracy drop, but the comparisons rest on single training runs.
Burden of malaria in Ethiopia, 2000– 2016: findings from the Global Health Estimates 2016
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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices
Removing fire modules from SqueezeNet yields much smaller malaria classifiers with a modest accuracy drop, but the comparisons rest on single training runs.