Abstract: Deep learning-based image classification models have been successfully applied in various scenarios.The current image classification models can be categorized into two classes:the CNN-based classifiers and the Transformer-based classifiers.Due to its limited receptive field,the CNN-based classifiers cannot model the global relation of image,which decreases the classification accuracy.While the Transformer-based classifiers usually segmente the image into non-overlapping image patches with equal size,which harms the local information between each pair of adjacent image patches.Additionally,the Transformer-based classification models often require pre-training on large datasets,resulting in high computational costs.To tackle these problems,an efficient pyramid vision Transformer(EPVT) based on depth-wise convolution is proposed in this paper to extract both the local and glo-bal information between adjacent image patches at a low computational cost.The EPVT model consists of three key components:local perception module(LP),spatial information fusion module(SIF) and convolutional feed-forward network module(CFFN).The LP module is used to capture the local correlation of image patches.SIF module is used to fuse local information between adjacent image patches and improve the feature expression ability of the proposed EPVT by utilizing the long-distance dependence between different image patches.CFFN module is used to encode the location information and reconstruct tensors between feature image patches.To validate the proposed EPVT model’s performance,various experiments are conducted on the benchmark datasets,and experimental results show the EPVT achieves 82.6% classification accuracy on ImageNet-1K,which outperforms most of the SOTA models with lower computational complexity.

Key words: Deep learning, Image classification, Depth-wise convolution, Visual transformer, Self-attention mechanism