1. Introduction to this article
The improvement mechanism this article brings to you is the ConvNeXtV2 network. ConvNeXt V2 is a new convolutional neural network architecture that integrates self-supervised learning technology and architectural improvements, especially the addition of a fully convolutional masked autoencoder framework and Global response normalization (GRN) layer . I replace it with YOLOv8's feature extraction network to extract more useful features. After my experiments, the backbone network can indeed improve the detection of three types of objects: large, medium and small. At the same time, the backbone network also provides multiple versions . You can use modified versions in the source code. This article introduces its main framework principles and then teaches you how to add the network structure to the network model. At the same time, before I start explaining, I would like to recommend my column . The content of this column supports (classification, detection, segmentation, tracking, key point detection). The column is currently a limited-time discount. Everyone is welcome to subscribe to this column. This column is updated 3-5 times a week. The latest mechanism of this article, as well as files and communication groups containing all my improvements are provided to everyone.
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