A Novel lightweight Convolutional Neural Network, ExquisiteNetV2

Shi-Yao Zhou, Chung-Yen Su

2021-05-19Image Classification Classification

Abstract

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.

Results

Task	Dataset	Metric	Value	Model
Image Classification	MNIST	Accuracy	99.71	ExquisiteNetV2
Image Classification	MNIST	Percentage error	0.29	ExquisiteNetV2
Image Classification	MNIST	Trainable Parameters	518230	ExquisiteNetV2

Related Papers

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations2025-07-18 Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17 Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy2025-07-17 Federated Learning for Commercial Image Sources2025-07-17 MUPAX: Multidimensional Problem Agnostic eXplainable AI2025-07-17 Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation2025-07-16 Safeguarding Federated Learning-based Road Condition Classification2025-07-16 Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking2025-07-15