轻量卷积网络的相关内容

文章 2025-02-09 来自：开发者社区

RT-DETR改进策略【模型轻量化】| PP-LCNet：轻量级的CPU卷积神经网络

一、本文介绍本文记录的是利用PP-LCNet中的DepSepConv模块优化RT-DETR。本文利用DepSepConv模块改善模型结构，使模型在几乎不增加延迟的情况下提升网络准确度。模型参数量计算量推理速度 rtdetr-l 32.8M 108.0GFLOPs 11.6ms Improv...

文章 2025-02-04 来自：开发者社区

YOLOv11改进策略【模型轻量化】| PP-LCNet：轻量级的CPU卷积神经网络

一、本文介绍本文记录的是利用PP-LCNet中的DepSepConv模块优化YOLOv11中的C3k2。本文利用DepSepConv模块改善模型结构，使模型在几乎不增加延迟的情况下提升网络准确度。模型参数量计算量推理速度 YOLOv11m 20.0M 67.6GFLOPs 3.5ms Im...

文章 2022-10-27 来自：开发者社区

ShuffleNet:极致轻量化卷积神经网络(分组卷积+通道重排)

@toc) 参考论文：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices作者：Xiangyu Zhang ，Xinyu Zhou，Mengxiao Lin，Jian Sun1、论文摘要我们引入了一个高效计算的CNN结构名字叫做shuffleNet，这个结构被设计用来解决部署算力非....

文章 2022-10-27 来自：开发者社区

ShuffleNetV2:设计轻量化卷积神经网络的理论准则和应用实现

@toc 参考论文：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design 作者：Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun; 上一篇我们复现过ShuffleNetV1架构，今天来复现ShuffleNetV2架构前置知识：Mobile....

文章 2022-02-17 来自：开发者社区

轻量化卷积神经网络MobileNet论文详解（V1&V2）

本文是 Google 团队在 MobileNet 基础上提出的 MobileNetV2，其同样是一个轻量化卷积神经网络。目标主要是在提升现有算法的精度的同时也提升速度，以便加速深度网络在移动端的应用。

文章 2022-02-16 来自：开发者社区

Google论文解读：轻量化卷积神经网络MobileNetV2 | PaperDaily #38

本文是 Google 团队在 MobileNet 基础上提出的 MobileNetV2，其同样是一个轻量化卷积神经网络。目标主要是在提升现有算法的精度的同时也提升速度，以便加速深度网络在移动端的应用。如果你对本文工作感兴趣，点击底部的阅读原文即可查看原论文。关于作者：陈泰红，小米高级算法工程师，研究方向为人脸检测识别，手势识别与跟踪。论文 | Inverted Residuals an...

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更新时间 2025-02-10 13:40:19

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