Image classification has become one of the main tasks in the field of
computer vision technologies. In this context, a recent algorithm called
CapsNet that implements an approach based on activity vectors and dynamic
routing between capsules may overcome some of the limitations of the current
state of the art artificial neural networks (ANN) classifiers, such as
convolutional neural networks (CNN). In this paper, we evaluated the
performance of the CapsNet algorithm in comparison with three well-known
classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification
accuracy on four datasets with a different number of instances and classes,
including images of faces, traffic signs, and everyday objects. The evaluation
results show that even for simple architectures, training the CapsNet algorithm
requires significant computational resources and its classification performance
falls below the average accuracy values of the other three classifiers.
However, we argue that CapsNet seems to be a promising new technique for image
classification, and further experiments using more robust computation resources
and re-fined CapsNet architectures may produce better outcomes.

通过使用四个不同的数据集（包括人脸、交通标志和日常物品图像）来评估 CapsNet 算法与传统分类器的性能。结果表明，CapsNet 是一种有前途的图像分类技术，但其分类准确性低于传统分类器的平均准确度值，且需要大量计算资源。