resnet

For reference only, if there is no translation in place please point out. Thesis address: Identity mappings in Deep residual Networks Address: http://blog.csdn.net/wspba/article/details/60750007 Summary As a very deep network framework, the deep residual network has been shown to be very good in precision and convergence. In this paper, we analyze the method of calculating propagation behind the residual block (residual building blocks), indicating that when the jump connection (skip connections

Distributed TensorFlow Official documentation Distributed-tensorflow-example @ GitHub (recommended) Distributedtensorflowsample @ GitHub Parameter Server Paper (Model) CNN Nets LeNet AlexNet Overfeat NIN Googlenet Inception-v1 Inception-v2 Inception-v3 Inception-v4 Inception-resnet-v2 ResNet 50

introduced coarse sub-sampling of features, which could result in the loss of important information.The literature "36" "22" uses intermediate layers to generate high-resolution segmentation results. Here we think that features from all levels is helpful for semantic segmentation. Here we propose a framework to integrate all the features for semantic segmentation. Network Structure The function of Refinenet block is to merge the feature map of different resolution level. The network structure

function [F, MAXF, V, S] = Ford_fulkerson (c, SRC, sink) n = Size (c, 1); F = zeros (n); maxf = 0; V = []; S = [];while true% in:resnet. ResNet = c-f + F '; % residual network. % Out:pre, Df pre = ones (1, N) * NaN; Df = ones (1, N) * INF; % DFS to find augmenting path. STK = [SRC]; unvisited = Setdiff (1:n, SRC); While ~isempty (Stk) if STK (1) = = Sink break; End% Pop from = STK (1); STK (1) =

, the algorithm has higher accuracy and efficiency. The Red Triangle curve corresponds to the result of the paper. The horizontal axis is the inference time, which is the speed of your object detector during testing. The unit is millisecond. The vertical axis is the MMAP of COCO, from 0.5 to 0.95. in this range, an average Map is obtained. The leftmost Red Triangle is the result of running out with a small model, the middle of the triangle is the result of running out with a

understand, convert to Python code, that's it:new_labels = (1.0 - label_smoothing) * one_hot_labels + label_smoothing / num_classes 1 Szegedy when the network is implemented, make label_smoothing = 0.1,num_classes = 1000. Label smooth improves network accuracy by 0.2%.I understand the label smooth, it is the original very abrupt one_hot_labels slightly smooth a little, the gun hit the bird, cut the head of the chicken, the crane, a bit of height to the chickens, to avoid the netwo

labeling is generally one-hot, such as [0,0,0,1], using a loss function similar to cross-entropy will make the ground Truth label allocation in the model learning the probability of excessive confidence, and because ground The logit value of the truth tag is too large for other labels, resulting in overfitting, resulting in reduced generalization. One solution is home Plus, which is to adjust the sample label to a probability distribution, so that the sample label becomes "soft", such as [0.1,0

"Aggregated residual transformations for Deep neural Networks" is saining Xie and other people in 2016 in the public on the arxiv:Https://arxiv.org/pdf/1611.05431.pdf Innovation Point1. The use of group convolution on the basis of traditional resnet, without increasing the number of parameters under the premise of obtaining a stronger representation ability NamedThis paper presents a resnet improved network

convolution method padding parameter set to ' SAME ' is the same. In simple terms, it is ceil (size/kernel_size), which is the same for the following calculations, in short, is to fill in the appropriate 0, so that the output and the above figure should be the corresponding.3. In the above figure the 5~10 column corresponds to the inception module of each convolution operation, the corresponding value is the number of feature output, for maxpool operation, his padding is 2,stride 1.4. When a in

Project homepage: Https://github.com/hszhao/PSPNet 1 Summary rank 1 on PASCAL VOC 2012 ETC Multiple benchmark (information up to 2016.12.16)Http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?cls=meanchallengeid=11compid=6submid =8822#key_pspnet leverages the global context information by different-region-based context aggregation (pyramid pooling) 1 Introduction DataSet :LMO DataSet [22]PASCAL context Datasets [8, 29]ade20k DataSet [43]The mainstream scene parsing algorithm is based on F

of the data is placed in Dataloader so that you can call Imagefolderdataset later to get the original picture setThe data is ready to complete.Model definitionFrom Mxnet.gluon import nnfrom mxnet import ndclass residual (NN. Hybridblock): Def __init__ (self, channels, same_shape=true, **kwargs): Super (residual, self). __init__ (**kwargs) Self.same_shape = Same_shape with Self.name_scope (): strides = 1 if same_shape else 2 Self.conv1 = nn. conv2d (channels, kernel_size=3, Pad

appropriate global features. In this paper, an optimal strategy of moderate supervision loss is proposed, which has excellent performance in many data sets. The main contributions of this paper are as follows: A pyramid scene parsing network is proposed, which can embed the difficult-resolved scene information feature into an effective optimization strategy based on the deep supervision loss resnet based on the FCN prediction Framework, and construct