alexnet

NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China Research Institute, mainly introduces a "dynamic surgery" algorithm, which effectively sol

. Training, evaluation lenet, alexnet, vgg, inception_v1, inception_v2, inception_v3, inception_v4, resnet_v1, and resnet_v2. The model is located in slim/nets: Alexnet. pyAlexnet_test.pyCifarnet. pyInception. pyInception_resnet_v2.pyInception_resnet_v2_test.pyInception_utils.pyInception_v1.pyInception_v1_test.pyInception_v2.pyInception_v2_test.pyInception_v3.pyInception_v3_test.pyInception_v4.pyInception_v

initialized with the parameter file you already have (that is, the previously trained Caffemodel)* * Part One: Caffe command-line parsing * * ————— First, training model code Script:./build/tools/caffe train -solver models/finetune/solver.prototxt -weights models/vgg_face_caffe/VGG_FACE.caffemodel -gpu 0BAT Command:..\..\bin\caffe.exe train --solver=.\solver.prototxt -weights .\test.caffemodelpause Second, Caffe command full analysis Http://

, the model is well generalized for other datasets.1 INTRODUCTIONThis article describes the Another important aspect of the convnet architecture is design - depth. Many people try to improve the AlexNet proposed in the year to achieve better results , zfnet in the first convolution layer using smaller convolution ( Receptive window size) and smaller step size (Stride) 2, the other strategy is to intensively train and test the entire image on a multi

1. Biologists have experimented with finding that the brain cortex responds to simple structures such as horns and edges, and through complex neurons, these simple structures ultimately help organisms to have more complex visual systems. 1970 David Marr's vision processing process follows the principle that after getting the image, it extracts simple geometric elements such as angles, edges, curves, and so on, and then uses more sophisticated information, such as depth information, surface infor

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered receptive Field. On the basis of feeling wild, 1980 Fukushima proposed a theoretical model Neocognitron is the first application of the field of artificial neural network. In 1998, the LENET-5 model proposed by LeCun was successful

Today to see a more classical semantic segmentation network, that is FCN, full name title, the original English thesis website: https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdfThree big guys: Jonathan Long Evan shelhamer Trevor DarrellThis web site is a big guy on the Internet FCN blog, at the same time deeply felt the gap between himself and the big guy, but still have to bite the bullet to complete the paper, paste out the Web site, and we learn together:47205839To get to the p

, ie (59,59)Take the following diagram as a simple example:Pool: Is the pooling operation, may translate the reason to say differently, the pool layer is a single layer , the pool layer is divided into the maximum pooling and the average value pooling; in fact, that is, the most obvious value of extracting eigenvalues, the maximum pooling effect is relatively better, so it is recommended to use.FC: That is all connected, namely Wx+b=ySoftmax: Normalization of treatment, classificationIn the proc

Refer to "Deeplearning.ai convolutional neural Network Week 2 Lecture Notes".1. AlexNet (Krizhevsky et al. 2012), 8-layer network.Learn to calculate the shape of the output for each layer: for the convolution layer, the edge length of the output = (input side length-filter side length)/step + 1, the output number of channels equals the number of filter. The number of channels per filter equals the number of channels entered. The parameters of the conv

enough. For images that can be flipped horizontally, randomly trimmed crop, rotated, twisted, scaled, stretched, changed hue, saturation (HSV), etc., can also be randomly combined. Attention should be paid to the changes in the picture (vertical flip) Whether it conforms to the actual, whether the loss of important features and so on. Alexnet 256 of the picture to 224 random crop sampling, for each picture, produced 2048 different samples, using the

Preface CIFAR-10 datasets are a common data set in the field of deep learning. The Cifar-10 consists of 60000 32*32 RGB color images, all of which include aircraft, cars, birds, fur, deer, dogs, frogs, horses, boats and trucks in 10 categories. 50000 training, 10000 tests. is often used as a classification task to evaluate the merits and demerits of deep learning frameworks and models. More well-known models such as Alexnet, NIN, ResNet, etc. have al

are specific to a training data set or a recognition task, it may not be effective to transfer learning with it. For medical images, it is not easy to get large-scale training data, so can we use transfer learning to help medical image recognition by using ready-made imagenet images? Images in imagenet (two-dimensional, color) there is no medical image, including some such as birds, cats, dogs, helicopters and other objects identified, and medical images (two-dimensional or three-dimensional, n

convergence. MSRA initialization: https://arxiv.org/abs/1502.01852 All need is a good init:https://arxiv.org/abs/1511.06422 Unfortunately, Vgg has two big drawbacks: The number of network architectures weight is quite large and consumes disk space. Training is very slow. Due to the number of its full-connected nodes, coupled with a deep network, VGG16 has 533mb+,vgg19 574MB. This makes deploying VGG more time consuming. We still use Vgg in many deep-learning imag

the NVIDIA Maxwell GPU architecture, the TITAN x combines 3,072 processing cores with a single-precision peak performance of 7 teraFLOPS, plus onboard 12GB memory, 336.5gb/s bandwidth to handle millions of of the data used to train deep neural networks.Nvidia introduced that TITAN X spent less than three days on the industry standard Model AlexNet, using 1.2 million ImageNet image datasets to train the model, while using a 16-core CPU took more than

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

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4. inception[V4]: inception-v4, Inception-resnet and the Impact of residual Connections on learning1. PrefaceThe NIN presented in the previous article made a notable contribution to the transformati

neural networks, the statistical characteristics of the activated values can be analyzed and the highly correlated outputs are clustered to build an optimal network on a per-layer basis. This indicates that a bloated sparse network can be simplified without sacrificing performance. while mathematical proofs have strict conditionality, the Hebbian guidelines strongly support this: fire together,wire together.Earlier, in order to break the network symmetry and improve learning ability, the tradit

neural networks, the statistical characteristics of the activated values can be analyzed and the highly correlated outputs are clustered to build an optimal network on a per-layer basis. This indicates that a bloated sparse network can be simplified without sacrificing performance. while mathematical proofs have strict conditionality, the Hebbian guidelines strongly support this: fire together,wire together.Earlier, in order to break the network symmetry and improve learning ability, the tradit

Matlab section: xMAP = Repmat (Linspace (-REGIONW/2, REGIONW/2, regionw), Regionh, 1);%linspace [x1,x2,n] arithmetic progression ymap = Repmat (li Nspace (-REGIONH/2, REGIONH/2, Regionh) ', 1, regionw); % transpose%compute the angle of the vector p1-->p2vecp1p2 = labeldata (2,:)-labeldata (1,:); angle =-atan2 (VECP1P2 (2), VECP1P2 (1)); The% angle calculates the four-quadrant inverse tangent widthoftherealregion = norm (vecp1p2) + offset; % seek p1p2 point distance, open radical +offsetmidpoin

Squeezenet is in the thesis Iandola F N, Han S, Moskewicz M W, et al squeezenet:alexnet-level accuracy with 50x fewer parameters ANDL T 0.5 MB model Size[j]. ArXiv preprint arxiv:1602.07360, 2016. A network model which is not focused on improving the classification accuracy and reducing the model parameters is proposed in this paper. In general, the deeper the layer number of convolution neural network, the stronger the expressive ability, the better parameters and structure can be found to solv