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Title: GitHub - Salmck/DenseNet: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). · GitHub

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X Title: GitHub - Salmck/DenseNet: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Description: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). - Salmck/DenseNet

Open Graph Description: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). - Salmck/DenseNet

X Description: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). - Salmck/DenseNet

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datasetshttps://github.com/Salmck/DenseNet/tree/master/datasets
modelshttps://github.com/Salmck/DenseNet/tree/master/models
modelshttps://github.com/Salmck/DenseNet/tree/master/models
LICENSEhttps://github.com/Salmck/DenseNet/blob/master/LICENSE
LICENSEhttps://github.com/Salmck/DenseNet/blob/master/LICENSE
README.mdhttps://github.com/Salmck/DenseNet/blob/master/README.md
README.mdhttps://github.com/Salmck/DenseNet/blob/master/README.md
checkpoints.luahttps://github.com/Salmck/DenseNet/blob/master/checkpoints.lua
checkpoints.luahttps://github.com/Salmck/DenseNet/blob/master/checkpoints.lua
dataloader.luahttps://github.com/Salmck/DenseNet/blob/master/dataloader.lua
dataloader.luahttps://github.com/Salmck/DenseNet/blob/master/dataloader.lua
main.luahttps://github.com/Salmck/DenseNet/blob/master/main.lua
main.luahttps://github.com/Salmck/DenseNet/blob/master/main.lua
opts.luahttps://github.com/Salmck/DenseNet/blob/master/opts.lua
opts.luahttps://github.com/Salmck/DenseNet/blob/master/opts.lua
train.luahttps://github.com/Salmck/DenseNet/blob/master/train.lua
train.luahttps://github.com/Salmck/DenseNet/blob/master/train.lua
READMEhttps://github.com/Salmck/DenseNet
Licensehttps://github.com/Salmck/DenseNet
https://github.com/Salmck/DenseNet#densely-connected-convolutional-networks-densenets
Densely Connected Convolutional Networkshttp://arxiv.org/abs/1608.06993
Gao Huanghttp://www.cs.cornell.edu/~gaohuang/
Zhuang Liuhttps://liuzhuang13.github.io/
Laurens van der Maatenhttps://lvdmaaten.github.io/
Kilian Weinbergerhttps://www.cs.cornell.edu/~kilian/
Convolutional Networks with Dense Connectivityhttp://www.gaohuang.net/papers/DenseNet_Journal.pdf
Gao Huanghttp://www.cs.cornell.edu/~gaohuang/
Zhuang Liuhttps://liuzhuang13.github.io/
Geoff Pleisshttps://geoffpleiss.com/
Laurens van der Maatenhttps://lvdmaaten.github.io/
Kilian Weinbergerhttps://www.cs.cornell.edu/~kilian/
technical reporthttps://arxiv.org/pdf/1707.06990.pdf
codehttps://github.com/liuzhuang13/DenseNet/tree/master/models
fb.resnet.torchhttps://github.com/facebook/fb.resnet.torch
https://github.com/Salmck/DenseNet#citation
https://github.com/Salmck/DenseNet#other-implementations
[Caffe]https://github.com/liuzhuang13/DenseNetCaffe
[Caffe]https://github.com/Tongcheng/DN_CaffeScript
[PyTorch]https://github.com/gpleiss/efficient_densenet_pytorch
[PyTorch]https://github.com/andreasveit/densenet-pytorch
[PyTorch]https://github.com/bamos/densenet.pytorch
[PyTorch]https://github.com/baldassarreFe/pytorch-densenet-tiramisu
[MXNet]https://github.com/Nicatio/Densenet/tree/master/mxnet
[MXNet]https://github.com/bruinxiong/densenet.mxnet
[MXNet]https://github.com/miraclewkf/DenseNet
[Tensorflow]https://github.com/YixuanLi/densenet-tensorflow
[Tensorflow]https://github.com/LaurentMazare/deep-models/tree/master/densenet
[Tensorflow]https://github.com/ikhlestov/vision_networks
[Lasagne]https://github.com/Lasagne/Recipes/tree/master/papers/densenet
[Keras]https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet
[Keras]https://github.com/robertomest/convnet-study
[Keras]https://github.com/titu1994/DenseNet
[Chainer]https://github.com/t-hanya/chainer-DenseNet
[Chainer]https://github.com/yasunorikudo/chainer-DenseNet
[Torch 3D-DenseNet]https://github.com/barrykui/3ddensenet.torch
[Keras]https://github.com/cmasch/densenet
[Tensorflow2]https://github.com/okason97/DenseNet-Tensorflow2
https://github.com/Salmck/DenseNet#some-following-up-projects
Multi-Scale Dense Convolutional Networks for Efficient Predictionhttps://github.com/gaohuang/MSDNet
DSOD: Learning Deeply Supervised Object Detectors from Scratchhttps://github.com/szq0214/DSOD
CondenseNet: An Efficient DenseNet using Learned Group Convolutionshttps://github.com/ShichenLiu/CondenseNet
Fully Convolutional DenseNets for Semantic Segmentationhttps://github.com/SimJeg/FC-DenseNet
Pelee: A Real-Time Object Detection System on Mobile Deviceshttps://github.com/Robert-JunWang/Pelee
https://github.com/Salmck/DenseNet#contents
Introductionhttps://github.com/Salmck/DenseNet#introduction
Usagehttps://github.com/Salmck/DenseNet#usage
Results on CIFARhttps://github.com/Salmck/DenseNet#results-on-cifar
Results on ImageNet and Pretrained Modelshttps://github.com/Salmck/DenseNet#results-on-imagenet-and-pretrained-models
Updateshttps://github.com/Salmck/DenseNet#updates
https://github.com/Salmck/DenseNet#introduction
https://cloud.githubusercontent.com/assets/8370623/17981494/f838717a-6ad1-11e6-9391-f0906c80bc1d.jpg
https://cloud.githubusercontent.com/assets/8370623/17981496/fa648b32-6ad1-11e6-9625-02fdd72fdcd3.jpg
https://github.com/Salmck/DenseNet#usage
herehttps://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
fb.resnet.torchhttps://github.com/facebook/fb.resnet.torch
https://github.com/Salmck/DenseNet#densenet-and-densenet-bc
https://github.com/Salmck/DenseNet#memory-efficient-implementation-newly-added-feature-on-june-6-2017
herehttps://github.com/facebook/fb.resnet.torch/blob/master/models/init.lua#L102
herehttps://github.com/liuzhuang13/DenseNet/tree/master/models
https://github.com/Salmck/DenseNet#results-on-cifar
https://github.com/Salmck/DenseNet#results-on-imagenet-and-pretrained-models
https://github.com/Salmck/DenseNet#torch
DenseNet modelshttps://pytorch.org/vision/stable/models.html
https://github.com/Salmck/DenseNet#models-in-the-original-paper
fb.resnet.torchhttps://github.com/facebook/fb.resnet.torch
Download (64.5MB)https://drive.google.com/open?id=0B8ReS-sYUS-HWFViYlVlZk9sdHc
Download (114.4MB)https://drive.google.com/open?id=0B8ReS-sYUS-HY25Dc2VrUGlVSGc
Download (161.8MB)https://drive.google.com/open?id=0B8ReS-sYUS-HaDdpNmlWRjJkd3c
Download (230.8MB)https://drive.google.com/open?id=0B8ReS-sYUS-HVXp2RExSTmMzZVU
https://github.com/Salmck/DenseNet#models-in-the-tech-report
technical reporthttps://arxiv.org/pdf/1707.06990.pdf
Download (256MB)https://drive.google.com/file/d/0By1NwtA2JPGzdVRqOEotMUZrbTA/view?usp=sharing&resourcekey=0-5D_u52k6wCy6doaLwdBfqw
Download (426MB)https://drive.google.com/file/d/0By1NwtA2JPGzdkRDaWQ5M3VHTDg/view?usp=sharing&resourcekey=0-yl4XWJ2J6GZaF6RPj43QHQ
Download (256MB)https://drive.google.com/file/d/0By1NwtA2JPGzRDhxWGo2a3pOTjA/view?usp=sharing&resourcekey=0-AOIBvppNz9cdDcuKvH7_aQ
Download (557MB)https://drive.google.com/file/d/0By1NwtA2JPGzcnFDSE1HQVh4c0k/view?usp=sharing&resourcekey=0-uAyd9bsTas2twCzAZ1DUpA
https://github.com/Salmck/DenseNet#caffe
https://github.com/shicai/DenseNet-Caffehttps://github.com/shicai/DenseNet-Caffe
https://github.com/Salmck/DenseNet#pytorch
PyTorch documentation on modelshttp://pytorch.org/docs/torchvision/models.html?highlight=densenet
https://github.com/Salmck/DenseNet#keras-tensorflow-and-theano
https://github.com/flyyufelix/DenseNet-Kerashttps://github.com/flyyufelix/DenseNet-Keras
https://github.com/Salmck/DenseNet#mxnet
https://github.com/miraclewkf/DenseNethttps://github.com/miraclewkf/DenseNet
https://github.com/Salmck/DenseNet#wide-densenet-for-better-timeaccuracy-and-memoryaccuracy-tradeoff
wide residual networkshttps://github.com/szagoruyko/wide-residual-networks
https://github.com/Salmck/DenseNet#updates
https://github.com/Salmck/DenseNet#contact
Readme https://github.com/Salmck/DenseNet#readme-ov-file
BSD-3-Clause license https://github.com/Salmck/DenseNet#BSD-3-Clause-1-ov-file
Please reload this pagehttps://github.com/Salmck/DenseNet
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Releaseshttps://github.com/Salmck/DenseNet/releases
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