vgg

image, and finally get a 1x1 classification features, The input 16x16 image will finally get 2x2 classification features, but the global maximum pool can be converted to 1x1 classification features, for multi-scale input, the output is consistent. And from the blue color block can be seen, in the 16x16 roll product can be seen as a window with 14x14 sliding 2 steps of the 4 convolution results. more : Specific content I have mentioned in another paper note: Overfeat.

1 Vgg Network Summary The feeling is alex-net on the basis of the study of how to deepen the network to improve performance. The overall five-story convolution plus three-layer full-link, but the five-layer convolution will be pooling to split, and five-layer convolution attempt to overlay multilayer convolution together, and try to use a smaller core and increase the number of cores to improve the performance of the network, such as alex-net the siz

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.

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, and

First, IntroductionVgg and googlenet are the double males of the 2014 Imagenet race, and the two types of model structures have a common feature of Go deeper. Unlike Googlenet, Vgg inherits some of the lenet and alexnet frameworks, especially the alexnet frame, Vgg is also a convolution of 5 group, 2-Layer FC image feature, a layer FC classification feature, Can be seen as a total of 8 part as alexnet. Base

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

These days run Vgg and googlenet really fast be abused cry, Vgg ran 2 weeks to converge to error rate 40%, then change local tyrants K40, run some test results to everyone to see, the first part share performance report, program run in Nvidia K40, video memory 12G, Memory 64G server, training and test data set built in own datasets and imagenet datasetsTraining configuration: batchsize=128Caffe's own imagen

First, IntroductionVgg NET, a deep convolutional neural network developed by the Visual Geometry Group (Visual Geometry Group) of Oxford University and a researcher at Google DeepMind, achieved second place in ILSVRC 2014, dropping the Top-5 error rate to 7.3 %。 Its main contribution is to demonstrate that the depth of the network (depth) is a key part of the algorithm's excellent performance. At present, more and more network structures are mainly ResNet (152-1000 layers), goolenet (22 layers),

Import torchimport numpyimport torch. nn as nnimport torch. nn. functional as ffrom Pil import imagefrom torchvision import transformsimport torchvision. models as modelsnormalize = transforms. normalize (mean = [0.485, 0.456, 0.406], # This is imagenet STD = [0.229, 0.224, 0.225]) TRAN = transforms. compose ([transforms. resize (224,224), transforms. totensor (), transforms. normalize (mean = [0.485, 0.456, 0.406], STD = [0.22 9, 0.224, 0.225]) Im = '. /1.jpeg '# im = '. /2.jpg 'im = image. ope

neural network, the node is super many, the connection line is also super,So this leads to a dropout layer, which does not have enough active layers in addition to the part.Module Eight is a result of the output, combined with the Softmax to make the classification. There are several types of outputs, and each node holds the probability values belonging to that category.Alexnet Summary: Input Size: 227*227*3 Convolution layer: 5 Reduced sampling layer (pool layer): 3 Full C

Kernel Output Stride Pad 1 128 * 3 * 221 * 221 96 * 3 * 11 * 11 128 * 96 * 106 * 106 2 0 2 128 * 96 * 58 * 58 256 * 96 * 5 * 5 128 * 96 * 54 * 54 1 0 3 128 * 96 * 27 *27 512 * 96 * 3 * 3 128 * 512 * 27 * 27 1 1 4 128 * 512 * 27 * 27 1024 * 512 * 3 * 3 128 * 1024 * 27 * 27 1 1

The VGG model we use is a model of a 19-layer parameter that someone else has trained.First step: Define the volume integral part operation functionmport Scipy.ioImportNumPy as NPImportOSImportScipy.miscImportMatplotlib.pyplot as PltImportTensorFlow as TF#to perform a convolution operationdef_conv_layer (input, weights, bias): Conv= tf.nn.conv2d (input, tf.constant (weights), strides= (1, 1, 1, 1), padding='same') returnTf.nn.bias_add (conv, bias)#

ResNet, AlexNet, Vgg, Inception: Understanding the various CNN architecturesThis article is translated from ResNet, AlexNet, Vgg, inception:understanding various architectures of convolutional Networks, original author retains copyrightConvolution neural network is an amazing performance in visual recognition task. A good CNN network is a "pang monster" with millions of parameters and many hidden layers. In

the convolution feature during training. For very deep VGG-16 models [19], our detection system has a frame rate of 5fps (including all steps) on the GPU, achieving the highest target detection accuracy at Pascal VOC 2007 and Pascal VOC 2012 (2007 is 73.2%map, 2012 is 70.4%map), with 300 suggestion boxes for each image. The code is already exposed.1. IntroductionRecent advances in target detection have been driven by the success of the region-recomme

CNN began in the 90 's lenet, the early 21st century silent 10 years, until 12 Alexnet began again the second spring, from the ZF net to Vgg,googlenet to ResNet and the recent densenet, the network is more and more deep, architecture more and more complex, The method of vanishing gradient disappears in reverse propagation is also becoming more and more ingenious.　　　　 LeNet AlexNet Zf Vgg