Researchers state that they see edge training as an optimization problem, thereby discovering the optimal scheduling to achieve minimal energy consumption under a given memory budget. Currently, deep learning models are widely deployed on edge devices such as smartphones and embedded platforms for inference. Training, however, is still primarily conducted on large cloud servers equipped … Read more
This article is sourced from Machine Heart. Researchers indicate that they viewedge training as an optimization problem, thus discovering the optimal scheduling to achieve minimal energy consumption under a given memory budget. Currently, deep learning models are widely deployed for inference on edge devices such as smartphones and embedded platforms. However, training predominantly occurs on … Read more
Click the “Computer Vision Life” above and select “Star” Quickly get the latest insights This article is reproduced from Machine Heart Researchers state that they view edge training as an optimization problem, thus discovering the optimal scheduling to achieve minimal energy consumption given a memory budget. Currently, deep learning models are widely deployed for inference … Read more
Click on "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavyweight content delivered first License Plate Recognition Overview License plate recognition based on deep learning, where the vehicle detection network directly uses YOLO for detection. Then, a network is used to detect and recognize the license plate number. The license plate detection network … Read more
Approximately 10,000 words, recommended reading time: 15 minutes. This article shares the top 10 models in deep learning, which hold significant positions in terms of innovation, application value, and impact. Since the concept of deep learning was proposed in 2006, nearly 20 years have passed. Deep learning, as a revolution in the field of artificial … Read more
“Academic Window” is a paper recommendation column launched by the Academic Research Department of the Graduate Affairs Center of the School of Computer Science and Technology, aimed at recommending and sharing the latest academic achievements and classic papers in various fields of computer science to students. In the future, it will be pushed on this … Read more
Follow the official account “ML_NLP“ Set as “Starred“, heavy content delivered to you first! Reprinted from | Smarter From DETR to ViT, various works have validated the potential of Transformers in the field of computer vision. Naturally, this raises a new question: which is better for image feature extraction, CNN or Transformer? The advantage of … Read more
Click on the above“Beginner Learning Vision“, select to add “Star” or “Top“ Essential content delivered promptly In this article, we will learn how to classify images of any size without using computationally intensive sliding windows. By modifying the ResNet-18 CNN framework, we will change the required input size from 224×224 to any size. First, we … Read more
Click the above“Beginner Learning Vision” to selectStar or “Pin” Heavyweight content delivered first Introduction In the field of CV, we need to master the most basic knowledge, which is the various architectures of Convolutional Neural Networks (CNNs). Whether we are dealing with image classification, segmentation, object detection, or NLP, we will use the basic CNN … Read more
Click the above“Beginner Learning Vision” and select toStar or “Pin” Important information delivered first time Source｜Daniel Liu@Zhihu, https://zhuanlan.zhihu.com/p/138555896 Multi-scale self-guided attention for medical image segmentation The method in this paper is the optimal method for medical image segmentation on the CHAOS MRI Dataset published in 2019, with a final Dice score of 86.75. Introduction: Views … Read more