vgg

13x13x384 3x3x256 1 1 13x13x256 3x3x384x256+256 884992 MaxPool3 13x13x256 3x3 2 0 6x6x256 0 FC6 6x6x256 4096 6x6x256x4096+4096 37752832 FC7 4096 4096 4096x4096+4096 16781312 FC8 4096 1000 4096x1000+1000 4097000 Total Parameters: 62378344parameter memory consumption: 237.9545MB

Define the main function, get the content and style features corresponding to the 5 convolution layers, and calculate Content_loss and Style_loss respectively. def main (config): #定义图像变换操作, must be defined. Totensor (). Transform = Transforms.compose ([Transforms. Totensor (), transforms. Normalize ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)]) #加速content和style图像, S Tyle image resize into the same size content = Load_image (config.content, transform, max_size = config.max_size) style = Lo

---restore content starts---First configure the operating environment of the TENSORFLOW-GPU under WIN10 and then copy the Neuralstyle on GitHub,Finally, according to the document description parameters, run the file, you can get your favorite style.steps for the entire experimental project :I. Environment configuration1. Installing vs20152. Installing Cuda 8.03.python3.5.2 installation (note win10 to install the TENSORFLOW-GPU version will need to install a higher version of Python, pro-Test 3.5

second 11%. From this deep learning to fame Sparrow.2. EvolutionSince 2012, the depth of the network has been increasing year in, is the ILSVRC competition champion's Network layer trend chart:In 2014, Vgg and Googlenet reached 19 and 22, respectively, and the accuracy was also increased by an unprecedented level. By 2015, Highway Networks reported that 900 layers could converge. Microsoft Research launched the ResNet, so that network depth of 152-la

the Faster r-cnn:towards Real-time Object Detection with region proposal Networks " Shaoqing Ren, kaiming He, Ross Girshick, Jian Sun --Learning data record (Simon John) The article intends to solve the problem (Towards real-time)The method of extracting proposal (s) from SPP net and fast r-cnn is very time consuming. Therefore, the author proposes that the Regional extraction Network (region proposal Networks, RPN), which and the detection network share full-image convolution feature, har

applications, from the scene understanding, inferred that the support between the object is related to autonomous driving. Early methods that rely on low-level visual cues have been replaced by popular machine learning algorithms. In particular, deep learning has been successful in the detection of handwritten numerals, speech, integer images, and images [ Vgg][googlenet]. Now the image segmentation field is also very interested in this method [Crfas

Object Contour Detection with a fully convolutional encoder-decoder network Using convolutional encoding and decoding network to detect the edges of primary targets The network structure is:Code: VGG-16Decoding: Reverse pooling-convolution-activation-dropout Convolution cores: The number of channels of every decoder layer is properlyDesigned to allow unpooling the maxpooling layer from its corresponding. Dropout: We also add a dropout layer after ea

deep network, the deep-roll integration network has good feature extraction ability, different layer extraction features have different meanings, each well-trained network can be regarded as a good feature extractor, in addition, the depth network has a layer of non-linear functions, can be regarded as complex multivariate nonlinear functions, This function completes the mapping of the input image to the output. As a result, thousands can use a well-trained depth network as a loss function calc

a lot of content is wrong, hey ... First look at the VGG-16 network structure is as follows: In this section, the bottleneck feature is extracted and rolled into the next "small" model, which is the fully connected layer, through a trained model.The implementation steps are: 1, the weight of the training model to take to, Model 2, run, extract bottleneck feature (network in full connection before the last layer of activated featureMap, convolution-fu

A tool for converting a Caffe model into a mxnet model Https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter Model conversion ./run.sh Vgg16 (Note: The process is lengthy and sometimes it resolves a bash error) Enter the pre-training model and network definition for the Caffe format, outputting the two items corresponding to the mxnet. Finally, two files are generated in this directory: Vgg16-0001.paramsVgg16-symbol.json Attention The script is actually downloading the

Preface Bloggers have spent a lot of time explaining how to Kitti raw data into an SSD-trained format, and then use the relevant Caffe code to implement SSD training. Download Vgg pre-training model The SSD is used for its own inspection task, it is required fine-tuning a pretrained network, the friends who have read the paper may know that the SSD framework in the paper is made up of the Vgg network as th

seemingly eliminated This problem.However, take the most competative network as of recently, Vgg[2]. They does not use the this kind of initialization, although they the report of it is tricky to get their networks to converge. They say that they first trained their most shallow architecture and then used so to help initialize the second one, and So forth. They presented 6 networks, so it seems like a awfully complicated training process to get to th

Reference article: http://blog.csdn.net/u013059662/article/details/52770198Caffe installation configuration, as well as the use of FCN in my front of the article has been mentioned, this side will not be more detailed. In the following section, let's look at how to use data sets provided by others to train your model! After this article, I plan to write about how to fine-tune and make my own datasets, and fine-tune with my own datasets.(i) Data preparation (take Sift-flow data set for example)Do

large-scale learning, deserves further study in the future. 3.2. The application of deep learning in object detectionobject detection is a more difficult task than object recognition. An image may contain multiple objects belonging to different categories, and object detection needs to determine the location and category of each object. In 2013, organizers of the ImageNet ILSVRC game increased the task of object detection, requiring the detection of 200 types of objects in 40,000 Internet image

describe the object by selecting the appropriate convolution layer on the size of the object. For example, if the height of a candidate area is between 0-64 pixels, the characteristics of the third convolution layer (for example, Conv3 in Vgg) are used to pooling as the input characteristics of the classifier and border regression, and if the candidate area is above 128 pixels, the last convolution layer is used ( For example, Conv5 in

1) For details about hog, refer to blog: Http://blog.csdn.net/kezunhai/article/details/8830860 2) For more information about phog, see: Http://www.robots.ox.ac.uk /~ Vgg/research/Caltech/phog.html 3) download phog source code (MatLab ):Http://www.robots.ox.ac.uk /~ Vgg/research/Caltech/phog/phog.zip 4) phog-related papers: [1] P. felzenszwalb, D. mcallester, D. ramaman.A discriminatively trained, multiscale

online draft coloring procedures, such: PaintschainerHttps://paintschainer.preferred.tech/index_en.html DeepcolorHttps://github.com/kvfrans/deepcolor Auto-painterHttps://arxiv.org/abs/1705.01908 In addition to paintschainer, the authors of other similar products are not very familiar with it. He said that many Asian papers claim to be able to migrate the cartoon style, but after carefully reading the paper, they will find that their so-called "new method" is a modified

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural network CNN Classic model finishing Lenet,alexnet,googlenet,vgg,deep residual learning" The article describ

structure is introduced into the Inception structure behind the traditional convolution layer and the pool layer, compared with AlexNet although the network layer increases, but the number of parameters is reduced because most of the parameters are concentrated in the full connection layer, and finally achieved the ImageNet 6.67% the results.2.2 Inception V2Inception V2 learned that the Vgg used two 3′3 convolution instead of the large convolution of

This article constructs a basic "block" and introduces a new dimension "cardinality" on this "block" (The letter "C" represents this dimension in a graph and a table). The other two dimensions of the depth network are depth (number of layers), width (width refers to the number of channel of a layer).First, let's start by understanding how this "Block" is built, as shown (Resnext is the simplified representation of the model presented in this paper)On the left is the standard residual network "bl