resnet

has surpassed the human eye. The models in Figure 1 are also a landmark representation of the deep learning vision Development.Figure 1. ILSVRC Top-5 Error rate over the yearsBefore we look at the model structures in Figure 1, we need to look at one of the deep-learning Troika ———— Lecun's lenet network Structure. Why to mention LeCun and lenet, because now visually these artifacts are based on convolutional neural network (cnn), and LeCun is CNN huang, Lenet is lecun to create the CNN Classic.

followed by NX1. In fact, the authors found that using this decomposition effect in the early days of the networkAnd not good, and in the medium size feature map on the use of the effect will be better, for MXM size of feature map, recommended m between 12 to 20.Use the NX1 convolution to replace the large convolution core, where n=7 is set to deal with 17x17 size feature map. The structure is formally used in the Googlenet V2.4, Inception V4 structure, it combines the residual neural network r

Disclaimer: The Caffe series is an internal learning document written by our lab Huangjiabin god, who has been granted permission to do So.This reference is made under the Ubuntu14.04 version, and the required environment for the default Caffe is already configured, and the following teaches you how to build the kaiming He residual network (residual network).Cite:he K, Zhang X, Ren S, et al residual learning for image recognition[c]//proceedings of the IEEE Conference on Computer Vision and Patt

Http://berwynzhang.com/2017/06/18/machine_learning/Inception-v4_Inception-ResNet_and_the_Impact_of_Residual_ connection_on_learning/***inception and Skip Connection http://blog.csdn.net/quincuntial/article/details/77263607 ***resnet Translation http://blog.csdn.net/buyi_shizi/article/details/53336192 *****resnet Understanding http://blog.csdn.net/mao_feng/article/details/52734438 https://www.leiphone.co

/* Copyright notice: Can be reproduced arbitrarily, please indicate the original source of the article and the author information . * /Residual network by introducing the skip connection into the CNN network structure, so that the depth of the web reached the scale of the thousand layers, and its performance in the CNN significantly improved, but why this new structure will take effect? This question is actually a very important question. This ppt summarizes the very deep network-related work

This article explains the use of TensorFlow to implement residual network resnet-50. The focus is not on the theoretical part, but on the implementation part of the code. There are other open source implementations on the GitHub, and if you want to run your own data directly using the code, it's not recommended to use my code. But if you want to learn ResNet code implementation ideas, then reading this arti

ResNet in 2015, and has affected the development of DL in academia and industry for 2016 years. Here is the network structure of this resnet, we have a sneak peek.It makes a reference for each layer's input, learning to form residual functions, rather than learning some functions without reference. This residual function is more easily optimized, which can greatly deepen the network layer number.We know tha

We have introduced the classic network in the front, we can view the previous article: Shallow into the TensorFlow 6-to achieve the classic network With the network more and more deep, we found that only by BN, Relu, dropout and other trick can not solve the convergence problem, on the contrary, the deepening of the network to bring the increase in parameters. Based on previous experience, we know: The network is not the deeper the better, on the one hand too many parameters easily lead to the f

ResNet, AlexNet, Vgg, inception:understanding various architectures of convolutional Networksby koustubh This blog from: http://cv-tricks.com/cnn/understand-resnet-alexnet-vgg-inception/ 　　　　 convolutional neural Networks is fantastic For visual recognition Tasks.good convnets is beasts withmillions of parameters and many hidden layers. In fact, a bad rule of thumb is: ' higher the number of hidden layers

First, the foreword recently in the Inception V3 and Inception ResNet v2 These two networks, these two network architectures I don't think I said more, Google produced. By fusing the feature map of different scales to replace the nxn convolution by 1xn convolution kernel nx1 convolution, the computational volume is effectively reduced, and the computational volume is reduced by using multiple 3x3 convolution instead of 5x5 convolution and 7x7 convolut

experimental data : Cat-dog Two classification, training set: 19871 validation set: 3975Experimental model : resnet-18batchsize: 128*2 (one K80 to eat 128 photos) the problem : the training set accuracy can reach 0.99 loss=1e-2-3, but the validation set accuracy 0.5,loss is very high, try a number of initial learning rate (0.1-0.0001) are not solve the above problem : Take the warm up method, a little help to the above problem Training