A Novel lightweight Convolutional Neural Network, ExquisiteNetV2
This work addresses the need for lightweight and accurate neural networks for image classification tasks, though it appears incremental as it builds on ExquisiteNetV1.
The authors tackled the problem of improving classification accuracy and efficiency in convolutional neural networks by proposing ExquisiteNetV2, which achieved the highest accuracy on over half of 15 datasets and had the fewest parameters with fast computing speed.
In the paper of ExquisiteNetV1, the ability of classification of ExquisiteNetV1 is worse than DenseNet. In this article, we propose a faster and better model ExquisiteNetV2. We conduct many experiments to evaluate its performance. We test ExquisiteNetV2, ExquisiteNetV1 and other 9 well-known models on 15 credible datasets under the same condition. According to the experimental results, ExquisiteNetV2 gets the highest classification accuracy over half of the datasets. Important of all, ExquisiteNetV2 has fewest amounts of parameters. Besides, in most instances, ExquisiteNetV2 has fastest computing speed.