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Image classification has become one of the main tasks in the field ofcomputer vision technologies. In this context, a recent algorithm calledCapsNet that implements an approach based on activity vectors and dynamicrouting between capsules may overcome some of the limitations of the currentstate of the art artificial neural networks (ANN) classifiers, such asconvolutional neural networks (CNN). In this paper, we evaluated theperformance of the CapsNet algorithm in comparison with three well-knownclassifiers (Fisher-faces, LeNet, and ResNet). We tested the classificationaccuracy on four datasets with a different number of instances and classes,including images of faces, traffic signs, and everyday objects. The evaluationresults show that even for simple architectures, training the CapsNet algorithmrequires significant computational resources and its classification performancefalls below the average accuracy values of the other three classifiers.However, we argue that CapsNet seems to be a promising new technique for imageclassification, and further experiments using more robust computation resourcesand re-fined CapsNet architectures may produce better outcomes.

CapsNet comparative performance evaluation for image classification