From 631bff19eecbc0aa75d25cb4b7324f3fc66439cb Mon Sep 17 00:00:00 2001 From: liuzhuang13 Date: Sat, 2 Dec 2017 20:29:32 -0800 Subject: [PATCH] updaet --- README.md | 72 ++++++++++++++++++++++--------------------------------- 1 file changed, 29 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index b867b50..c5cff9e 100644 --- a/README.md +++ b/README.md @@ -23,31 +23,31 @@ If you find DenseNet useful in your research, please consider citing: ## Other Implementations -0. Our [Caffe](https://github.com/liuzhuang13/DenseNetCaffe). -0. Our memory-efficient [Caffe](https://github.com/Tongcheng/DN_CaffeScript). -0. Our memory-efficient [PyTorch](https://github.com/gpleiss/efficient_densenet_pytorch). -0. [PyTorch](https://github.com/andreasveit/densenet-pytorch) by Andreas Veit. -0. [PyTorch](https://github.com/bamos/densenet.pytorch) by Brandon Amos. -0. [MXNet](https://github.com/Nicatio/Densenet/tree/master/mxnet) by Nicatio. -0. [MXNet (supporting ImageNet)](https://github.com/bruinxiong/densenet.mxnet) by Xiong Lin. -0. [MXNet](https://github.com/miraclewkf/DenseNet) by miraclewkf. -0. [Tensorflow](https://github.com/YixuanLi/densenet-tensorflow) by Yixuan Li. -0. [Tensorflow](https://github.com/LaurentMazare/deep-models/tree/master/densenet) by Laurent Mazare. -0. [Tensorflow (with BC structure)](https://github.com/ikhlestov/vision_networks) by Illarion Khlestov. -0. [Lasagne](https://github.com/Lasagne/Recipes/tree/master/papers/densenet) by Jan Schlüter. -0. [Keras](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet) by tdeboissiere. -0. [Keras](https://github.com/robertomest/convnet-study) by Roberto de Moura Estevão Filho. -0. [Keras (with BC structure)](https://github.com/titu1994/DenseNet) by Somshubra Majumdar. -0. [Chainer](https://github.com/t-hanya/chainer-DenseNet) by Toshinori Hanya. -0. [Chainer](https://github.com/yasunorikudo/chainer-DenseNet) by Yasunori Kudo. -0. [Torch 3D-DenseNet](https://github.com/barrykui/3ddensenet.torch) by Barry Kui. - -Note that we only listed some early implementations here, and didn't label all implementations which support BC structures. If you would like to add yours, please submit a pull request. - -## Projects build on DenseNets -0. [Multi-Scale Dense Convolutional Networks for Efficient Prediction](https://github.com/gaohuang/MSDNet) by Gao Huang. -0. [DSOD: Learning Deeply Supervised Object Detectors from Scratch](https://github.com/szq0214/DSOD) by Zhiqiang Shen, Zhuang Liu, etc. (In ICCV 2017) -0. [Fully Convolutional DenseNets for segmentation](https://github.com/SimJeg/FC-DenseNet) by Simon Jegou. +Our [[Caffe]](https://github.com/liuzhuang13/DenseNetCaffe), Our memory-efficient [[Caffe]](https://github.com/Tongcheng/DN_CaffeScript), Our memory-efficient [[PyTorch]](https://github.com/gpleiss/efficient_densenet_pytorch), +[[PyTorch]](https://github.com/andreasveit/densenet-pytorch) by Andreas Veit, [[PyTorch]](https://github.com/bamos/densenet.pytorch) by Brandon Amos, +[[MXNet]](https://github.com/Nicatio/Densenet/tree/master/mxnet) by Nicatio, +[[MXNet]](https://github.com/bruinxiong/densenet.mxnet) by Xiong Lin, +[[MXNet]](https://github.com/miraclewkf/DenseNet) by miraclewkf, +[[Tensorflow]](https://github.com/YixuanLi/densenet-tensorflow) by Yixuan Li, +[[Tensorflow]](https://github.com/LaurentMazare/deep-models/tree/master/densenet) by Laurent Mazare, +[[Tensorflow]](https://github.com/ikhlestov/vision_networks) by Illarion Khlestov, +[[Lasagne]](https://github.com/Lasagne/Recipes/tree/master/papers/densenet) by Jan Schlüter, +[[Keras]](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet) by tdeboissiere, +[[Keras]](https://github.com/robertomest/convnet-study) by Roberto de Moura Estevão Filho, +[[Keras]](https://github.com/titu1994/DenseNet) by Somshubra Majumdar, +[[Chainer]](https://github.com/t-hanya/chainer-DenseNet) by Toshinori Hanya, +[[Chainer]](https://github.com/yasunorikudo/chainer-DenseNet) by Yasunori Kudo, +[[Torch 3D-DenseNet]](https://github.com/barrykui/3ddensenet.torch) by Barry Kui. + + +Note that we only listed some early implementations here. If you would like to add yours, please submit a pull request. + +## Some Following up Projects +0. [Multi-Scale Dense Convolutional Networks for Efficient Prediction](https://github.com/gaohuang/MSDNet) +0. [DSOD: Learning Deeply Supervised Object Detectors from Scratch](https://github.com/szq0214/DSOD) +0. [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://github.com/ShichenLiu/CondenseNet) +0. [Fully Convolutional DenseNets for Semantic Segmentation](https://github.com/SimJeg/FC-DenseNet) + @@ -133,30 +133,16 @@ More accurate models trained with the memory efficient implementation in the [te ### Caffe -For ImageNet pretrained Caffe models, please see https://github.com/shicai/DenseNet-Caffe. Also, we would like to thank @szq0214 for help on Caffe models. - +https://github.com/shicai/DenseNet-Caffe. ### PyTorch -In PyTorch, ImageNet pretrained models can be directly loaded by - -``` -import torchvision.models as models -densenet = models.densenet161(pretrained=True) -``` - -For ImageNet training, customized models can be constructed by simply calling - -``` -DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000) -``` - -See more details at [PyTorch documentation on models](http://pytorch.org/docs/torchvision/models.html?highlight=densenet) and the [code for DenseNet](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py). We would like to thank @gpleiss for this nice work in PyTorch. +[PyTorch documentation on models](http://pytorch.org/docs/torchvision/models.html?highlight=densenet). We would like to thank @gpleiss for this nice work in PyTorch. ### Keras, Tensorflow and Theano -Please see https://github.com/flyyufelix/DenseNet-Keras. +https://github.com/flyyufelix/DenseNet-Keras. ### MXNet -Please see https://github.com/miraclewkf/DenseNet. +https://github.com/miraclewkf/DenseNet. ## Wide-DenseNet for better Time/Accuracy and Memory/Accuracy Tradeoff