alexnet

AlexNet contribution : ILSVRC2012 champion, showing the depth of CNN in the image task of the astonishing performance, the upsurge of CNN research, is now deep learning and the rapid development of AI important reason. The Imagenet competition provides a platform for the Hinton that has been studying neural networks, Alexnet was published by Hinton and his two students, and deep learning has been sile

Reference. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural Networks [J]. Advances in neural information processing Systems, 2012, 25 (2): 2012.https://code.google.com/p/cuda-convnet/ Say ashamed, see deep study Fast five months, the previous weeks of paper review just notice alexnet, that decisive use Ah, said lenet although good, that is fast 20 years ago Network structure,

song not "i don't do eldest brother for many years" Mo belongs.This deep learning model is the later famous Alexnet model. Why is this alexnet so big? There are three very important reasons: A lot of data, deep learning field should thank the Li Feifei team to make such a large collection of labeled Data imagenet; GPU, This highly parallel computing artifact really helped the force of t

song not "I don't do eldest brother for many years" Mo belongs.This deep learning model is the later famous alexnet model. Why is this alexnet so big? There are three very important reasons: A lot of data, deep learning field should thank the Li Feifei team to make such a large collection of labeled Data imagenet; GPU, this highly parallel computing artifact really helped the force of t

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

[Caffe] alexnet interpretation of the image classification model of deep learningOriginal address: http://blog.csdn.net/sunbaigui/article/details/39938097This article has been included in:Deep learning Knowledge BaseClassification:Deep Learning (+)Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.On the Imagenet Image Classification Challenge, Alex proposed the

In 2012, Geoffrey and his student Alex, in order to respond to the doubters, in the imagenet contest shot, refreshing the imageclassification record, laid a deep learning in computer vision status. The story behind us all know, deeplearning eminence, invincible. The structure Alex used in this competition is known as alexnet. In this part, we first introduce the basic architecture of alexnet, and then analy

"Convolutional neural Networks-evolutionary history" from Lenet to Alexnet This blog is "convolutional neural network-evolutionary history" of the first part of "from Lenet to Alexnet" If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/51440344 More related blog please poke: http://blog.csdn.net/cyh_24 This series of blogs is an expande

sliding scanning, the sliding network each time with the feature map window is connected, mapped to the low-dimensional vector, into two full-connected layers (box regression layer box-regression layers and box classification layer box-classification layers). Repeated calculations, thousands of suggested regions (region) overlap each other, repeated extraction characteristics.Fast R-CNN, accelerated version, finally recommended area mapping CNN finally convolution layer feature map, a picture o

How does the AlexNet into FCNs?FCNs is a network, only contain convolution layers and no FC layer at all. It ' s structure can be shown as the following figures:This image from the paper: Fully convolutional Networks for Semantic segmentation> CVPR 2015.It could locate the location of object target perfectly as shown in above images and it doesn ' t need to resize the Resolut Ion of input images, which is the mostly different from traditional cnns. Fi

catalog view Summary view Subscription [Top] "convolutional neural network-evolutionary history" from Lenet to AlexnetTags: CNN convolutional neural Network Deep learningMay 17, 2016 23:20:3046038 people read Comments (4) favorite reports Classification:"Machine Learning Deep Learning" (a)Copyright NOTICE: If you want to reprint, please attach this article link. Author Home: Http://blog.csdn.net/cyh_24 51440344Directory (?) [+]"Convolutional neural Networks-evolutionary history

Before looking at Caffenet, now look at this alexnet, mainly to help understand the paper.Here is the main record of some and caffenet different places.The first layer: mainly first normalized re-poolingSecond layer: deviation is 0.1. First normalized and then pooledLayer Three: identicalFourth floor: deviation is 0.1.Fifth floor: Deviation is 0.1.Sixth floor: Deviation of 0.1Seventh floor: Deviation of 0.1From, can also be seen, with Caffenet, is con

The basic structure of alexnetAlexnet is composed of 5 convolutional layers and three fully connected layers, a total of 8 weight layers (the pooling layer is not a weight layer because it has no parameters), wherein the RELU activation function on each convolution layer and the full join layer, the first convolution layer and the second convolution layer behind the connection of a local response normalization layer, The maximum pooling layer acts on the output of the first convolution layer, th

) 120 120* (400+1) =48120 FC4 (84,1) 84 84* (120+1) =10164 Softmax (10,1) 10 10* (84+1) =850 Third, alexnet networkAlexnet Network total: convolutional layer 5, Pool layer 3, full connectivity layer: 3 (which contains the output layer).The structure of convolutional neural network is not a simple combination of each layer, it is composed of a "module", within the module,The a

[Caffe] alexnet interpretation of the image classification model of deep learningOn the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model won the 2012-term championship. In order to study the application of the CNN type DL network model in image classification, we can not escape the research alexnet, which is the classic m

On the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model won the 2012-term championship. In order to study the application of the CNN type DL network model in image classification, we can not escape the research alexnet, which is the classic model of CNN in image classification (after the DL fires up).In the model example of the DL open source implementation Caffe, i

The structure of the classic convolutional neural network generally satisfies the following expressions: Output layer, (convolutional layer +--pooling layer?) ) +-Full connection layer + In the above formula, "+" means one or more, "? "represents one or 0, such as" convolutional layer + ", which represents one or more convolutional layers," pooling layer? " "represents one or 0 pooled layers. "--" indicates the forward direction. The LeNet-5, AlexNet