Click the blue text above to follow us! When training a Convolutional Neural Network (CNN), it is common to first select a well-known backbone (such as ResNet-50) and then adjust the architecture to balance performance and efficiency based on requirements. Often, this adjustment concept relies heavily on experience, which needs to be cultivated through extensive … Read more
Author: Chen Yang Editor: Huang Junjia Introduction Hi everyone, I am the pirate captain from Ocean University of China. Today, I am starting a new series. During this time, I have been helping Xuan Jie run experimental codes and conducted many comparative experiments. I found that the implementation of Keras is very good in terms … Read more
Convolutional Neural Networks (CNN): The Core of Image Processing Image recognition, once considered a “deadlock” in artificial intelligence, has now become a highlight of technology. Convolutional Neural Networks (CNN) serve as the core technology for image processing, shining brightly in many practical applications such as autonomous driving, medical image analysis, and facial recognition. So, how … Read more
Click on the “AIWalker” above, select “Star” or “Top” to receive important content promptly. In the past two years, many CNN architectures have emerged, some designed manually and others developed through NAS. However, once the architecture is established, there are always some commonalities among different methods. This article discusses several recent papers from the perspective … Read more
Table of Contents 1. System Overview 2. System Simulation Effect Preview 3. Core Code 4. Complete Project Acquisition 1. System Overview Faster R-CNN is a deep learning object detection algorithm based on Region Proposal Network (RPN) and Fast R-CNN. This algorithm consists mainly of two parts: the RPN network and the Fast R-CNN network. The … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience that includes NLP master’s and PhD students, university professors, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for … Read more
Click on the above “CVer“, select to add “Star” or “Top” Important content delivered instantly Author: zzq https://zhuanlan.zhihu.com/p/68411179 This article is authorized, and reprinting without permission is prohibited Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between distant pixels are … Read more
From | Zhihu Author | zzq Address | https://zhuanlan.zhihu.com/p/68411179 This article is for communication purposes only. If there is any infringement, please contact for deletion. Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between distant pixels are relatively weak. Therefore, … Read more
Click aboveComputer Vision Alliance to get more valuable content For academic sharing only, does not represent the position of this public account, contact for removal in case of infringement Reproduced from: Author丨zzq Source丨https://zhuanlan.zhihu.com/p/68411179 Editor丨Jishi Platform Recommended notes from 985 AI PhD Zhou Zhihua’s “Machine Learning” handwritten notes are officially open-source! Includes PDF download link, Github … Read more
Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between pixels that are far apart are weaker. Therefore, each neuron does not need to perceive the entire image globally; it only needs to perceive local information, which can then be combined … Read more