以下内容摘自<Bag of Tricks for Image Classification with Convolutional Neural Networks>. 1 高效训练 1.1 大batch训练 当我们有一定资源后,当然希望能充分利用起来,所以通常会增加batch size来达到加速训练的效果.但是,有不少实验结果表明增大batch size可能降低收敛率,所以为了解决这一问题有人以下方法可供选择: 1.1.1 线性增加学习率 一句话概括就是batch size增加多少倍,学习率也增…

训练技巧详解[含有部分代码]Bag of Tricks for Image Classification with Convolutional Neural Networks 置顶 2018-12-11 22:07:40 Snoopy_Dream 阅读数 1332更多 分类专栏: 计算机视觉 pytorch 深度学习tricks   版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/e015…

这篇文章来自李沐大神团队,使用各种CNN tricks,将原始的resnet在imagenet上提升了四个点.记录一下,可以用到自己的网络上.如果图片显示不了,点击链接观看 baseline model: resnet50 transform部分使用pytorch的torchvision接口 train transform: transforms.RandomResizedCrop(224) transforms.RandomHorizontalFlip(0.5) transforms.Colo…

一.高效的训练     1.Large-batch training 使用大的batch size可能会减小训练过程(收敛的慢?我之前训练的时候挺喜欢用较大的batch size),即在相同的迭代次数下, 相较于使用小的batch size,使用较大的batch size会导致在验证集上精度下降.文中介绍了四种方法. Linear scaling learning rate 梯度下降是一个随机过程,增大batch size不会改变随机梯度的期望,但是减小了方差(variance).换句话说,增大…

Use bigger datasets for CNN in hope of better performance. A new data set for sports video classification: sports-1M. CNN in one frame is about the same as many frames. CNN is good at image but not modeling temporal sequences. The result is not good.…

CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译 综述深度卷积神经网络架构:从基本组件到结构创新 目录 摘要    1.引言    2.CNN基本组件        2.1 卷积层        2.2 池化层        2.3 激活函数        2.4 批次归一化        2.5 Dropout        2.6 全连接层…

<ImageNet Classification with Deep Convolutional Neural Networks> 剖析 CNN 领域的经典之作, 作者训练了一个面向数量为 1.2 百万的高分辨率的图像数据集ImageNet, 图像的种类为1000 种的深度卷积神经网络.并在图像识别的benchmark数据集上取得了卓越的成绩. 和之间的LeNet还是有着异曲同工之妙.这里涉及到 category 种类多的因素,该网络考虑了多通道卷积操作, 卷积操作也不是 LeNet 的单通道…

本文以下内容来自读论文以后认为有价值的地方,论文来自:convolutional Neural Networks Applied to House Numbers Digit Classification . 对于房门号的数字识别问题,文中提出的方法是基于卷积神经网络的,卷积神经网络集特征提取与目标分类于一体,这一点有别于传统的识别方法(传统方法中一般都是基于人工设计的特征提取器,然后把提取到的特征输入给分类器). 文中在传统的卷积神经网络基础上有两点改进: 第一:pooling层,传统的方法的…

感谢: XNOR-Net ImageNet Classification Using Binary Convolutional Neural Networks XNOR-Net ImageNet Classification Using Binary Convolutional Neural Networks 本人想把算法思想实现在mxnet上(不单纯是一个layer),有意愿一起的小伙伴可以联系我,本人qq(邮箱):564326047(@qq.com),或者直接在下面留言. 一.Introdu…

ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton 摘要 我们训练了一个大型的深度卷积神经网络,来将在ImageNet LSVRC-2010大赛中的120万张高清图像分为1000个不同的类别.对测试数据,我们得到了top-1误差率37.5%,以及top-5误差率17.0%,这个效果比之前最顶尖的都要好得多.该神经网络有…

ImageNet Classification with Deep Convolutional Neural Networks 深度卷积神经网络的ImageNet分类 Alex Krizhevsky University of Toronto 多伦多大学 kriz@cs.utoronto.ca Ilya Sutskever University of Toronto 多伦多大学 ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toront…

ImageNet Classification with Deep Convolutional Neural Networks 摘要 我们训练了一个大型深度卷积神经网络来将ImageNet LSVRC-2010竞赛的120万高分辨率的图像分到1000不同的类别中.在测试数据上,我们得到了top-1 37.5%, top-5 17.0%的错误率,这个结果比目前的最好结果好很多.这个神经网络有6000万参数和650000个神经元,包含5个卷积层(某些卷积层后面带有池化层)和3个全连接层,最后是一个1…

How to Use Convolutional Neural Networks for Time Series Classification 2019-10-08 12:09:35 This blog is from: https://towardsdatascience.com/how-to-use-convolutional-neural-networks-for-time-series-classification-56b1b0a07a57 Introduction A large am…

论文  < Convolutional Neural Networks for Sentence Classification>通过CNN实现了文本分类. 论文地址: 666666 模型图: 模型解释可以看论文,给出code and comment: # -*- coding: utf-8 -*- # @time : 2019/11/9 13:55 import numpy as np import torch import torch.nn as nn import torch.optim…

论文题目<Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks> 论文作者:Y ushi Chen, Member , IEEE, Hanlu Jiang, Chunyang Li, Xiuping Jia, Senior Member , IEEE, and Pedram Ghamisi, Member , IEEE 论文发表年份:20…

http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ 讲CNN以及其在NLP的应用,非常深入浅出的讲法,好文,mark. When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakt…

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated pho…

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C…

http://cs231n.github.io/   里面有很多相当好的文章 http://cs231n.github.io/convolutional-networks/ Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Laye…

About this Course This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applica…

A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do reali…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but…

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 本文提出了一种新的CNN 框架来处理跟踪问题.众所周知,CNN在很多视觉领域都是如鱼得水,唯独目标跟踪显得有点“慢热”,这主要是因为CNN的训练需要海量数据,纵然是在ImageNet 数据集上微调后的model 仍然不足以很好的表达要跟踪地物体,因为Tracking问题的特殊性,至于怎么特殊的,且听细细道来. 目标跟踪之所以很少被 C…

Convolutional Neural Networks NOTE: This tutorial is intended for advanced users of TensorFlow and assumes expertise and experience in machine learning. Overview CIFAR-10 classification is a common benchmark problem in machine learning. The problem i…

This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…

p.p1 { margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px "Helvetica Neue"; color: #323333 } p.p2 { margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px "Helvetica Neue"; color: #042eee } span.s1 { } span.s2 { text-decoration: underline } Is objec…

Convolutional Neural Networks: Application Welcome to Course 4's second assignment! In this notebook, you will: Implement helper functions that you will use when implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlow (…

Andrew Ng deeplearning courese-4:Convolutional Neural Network Convolutional Neural Networks: Step by Step Convolutional Neural Networks: Application Residual Networks Autonomous driving - Car detection YOLO Face Recognition for the Happy House Art: N…

Learning Convolutional Neural Networks for Graphs 2018-01-17  21:41:57 [Introduction] 这篇 paper 是发表在 ICML 2016 的:http://jmlr.org/proceedings/papers/v48/niepert16.pdf 上图展示了传统 CNN 在 image 上进行卷积操作的工作流程.(a)就是通过滑动窗口的形式,利用3*3 的卷积核在 image 上进行滑动,来感知以某一个像素点为中心…