Click on “Computer Vision Life” above, and select “Star” Quickly get the latest insights This article is compiled from Zhihu Q&A. If there is any infringement, please delete it. Editor丨Extreme City Platform Viewpoint 1 Author丨Ruo Yu I think the most enlightening work for me is by Xu Zhiqin from Shanghai Jiao Tong University. https://ins.sjtu.edu.cn/people/xuzhiqin/fprinciple/index.html His … Read more
Click the “Beginner’s Visual Learning” above to choose to add “Star” or “Pinned“. Important content delivered promptly. From a high-level perspective of our deep learning project, we have prepared the data, and now we are ready to build our model. Prepare Data Build Model Train Model Analyze Model Results When we talk about the model, … Read more
Editor | Fish Da Reprinted from | Champion’s Trial Blog Total words:21041 Images:18 Estimated reading time: 53 minutes Convolutional Neural Networks (CNNs) have become a prominent framework in the field of deep learning, especially excelling in computer vision. Starting from LeNet in the 1990s, CNNs went through a decade of silence until AlexNet revived them … Read more
↑ ClickBlue Text Follow the Extreme Market Platform Author丨zzk Source丨GiantPandaCV Editor丨Extreme Market Platform Extreme Market Guide The network structure of this article is manually designed, mainly improving the multi-level division convolution of feature maps, concatenation, which enhances the network’s accuracy while also reducing inference time. It feels like a combination of res2net and ghostnet, and … Read more
In 2014, the Visual Geometry Group at the University of Oxford and Google DeepMind developed a new convolutional neural network called VGGNet. VGGNet is a deeper deep convolutional neural network than AlexNet, and this model achieved second place in the 2014 ILSVRC competition, with GoogLeNet taking first place (which we will introduce later). Paper: Very … Read more
Using classic CNN methods to build an automatic extraction model for road elements in Guiyang She Zuoming, Shen Yongzhi, Song Jianhong, Xiang Yuchen Guiyang Surveying and Mapping Institute, Guiyang, Guizhou 550000 Keywords: CNN, Deep Learning, Road Extraction, Remote Sensing Interpretation Citation format: She Zuoming, Shen Yongzhi, Song Jianhong, et al. Using classic CNN methods to … Read more
Click the above “AI Youdao” and select “Star” public account Heavyweight content delivered immediately Editor: Yi Zhen https://www.zhihu.com/question/337470480 This article is for academic sharing only. If there is an infringement, it will be deleted. Reports on machine learning algorithms and natural language processing What Bold and Innovative Neural Network Structures Exist in Convolutional Neural Networks? … Read more
Hello everyone, I am Redstone! In the previous article: Implementing VGGNet (Theoretical Part) We detailed the network structure of VGGNet. Today, we will use PyTorch to reproduce the VGGNet network and apply the VGGNet model to solve a classic Kaggle image recognition competition problem. Let’s get started! 1. Dataset Preparation In the paper, the authors … Read more
Selected from arXiv Translated by Machine Heart Contributors: Huang Xiaotian, Lu Xue, Jiang Siyuan Recently, researchers from Nanyang Technological University published a paper describing the mathematical principles of convolutional networks. This paper explains the entire operation and propagation process of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence … Read more
R-CNN series object detection networks are the first series of networks in the field of object detection using deep learning, serving as a typical Two-Stage object detection network. This series includes R-CNN, Fast R-CNN, and Faster R-CNN, and as their names suggest, each generation is faster than the previous one, primarily because the characteristic of … Read more