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https://github.com/bruinxiong/SENet.mxnet

:fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2) :fire::fire:
https://github.com/bruinxiong/SENet.mxnet

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:fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2) :fire::fire:

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# SENet.mxnet

A MXNet implementation of Squeeze-and-Excitation Networks 

(**SE-ResNext 18,50,101,152, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2**)



This is a [MXNet](http://mxnet.io/) implementation of Squeeze-and-Excitation Networks (**SE-ResNext, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2**) architecture as described in the paper [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.01507v1.pdf) proposed by [Jie Hu](https://github.com/hujie-frank) et. al. They deployed this SE block in SENet and win the Imagenet 2017 classification task.



![](title.png)



The author's caffe implementation can be found in his [repo](https://github.com/hujie-frank/SENet) on GitHub.



This is an illustration of a Squeeze-and-Excitation block.



![](SE_Block.png) 



The SE-ResNet module is implemented as followed:










The SE-ResNext 50 is implemented following this table:



![](SE-ResNext_50.png)



This MXNet implementation is refered to [taki0112's](https://github.com/taki0112) [tensorflow version](https://github.com/taki0112/SENet-Tensorflow). I also refered a [PyTorch implementation](https://github.com/kuangliu/pytorch-cifar/blob/master/models/senet.py) from [kuangliu](https://github.com/kuangliu). BTW, I add a dropout layer before the last FullyConnected layer. For Inception v4, I refers the [MXnet implementation](https://github.com/Trangle/mxnet-inception-v4) from [Trangle](https://github.com/Trangle). Finally, I attach the training code if you want to train your own data with SE-ResNext architecture by yourself. 



## Requirements



Install MXNet(0.11.0) on GPUs mechine with NVIDIA CUDA 8.0, and it's better also installed with [cuDNN v6](https://developer.nvidia.com/cudnn) or later version (I'm not testing cuDNN v7).



## Data



ImageNet'12 dataset



Imagenet 1000 class dataset with 1.2 million images. Because this dataset is about 120GB, so you have to download by yourself. Sorry for this inconvenience.



## How to Train



For data preparation, you can refer [my pervious part of densenet](https://github.com/bruinxiong/densenet.mxnet) or you can also visit the repo of [Wei Wu](https://github.com/tornadomeet/ResNet). In his page, there is a very detailed information about how to prepare your data. 



When you finised data preparation, please make sure the data locates the same folder of source codes. You also need to change path of path_imgrec in line 84 and line 108 of train_se_resnext_w_d.py. Then you can run the training cmd just like this (here, I use 4 gpus for training):



python -u train_se_resnext_w_d.py --data-dir data/imagenet --data-type imagenet --depth 50 --batch-size 192 --num-group 64 --drop-out 0.0 --gpus=6,7,8,9



Maybe you should change batch-size from 256 to 128 due to the memory size of GPU.



## How to retrain



When we want to train the large dataset and hope to change learning rate manually, or the machine is suddenly shutdown due to some reason, of course, we definitely hope we can continue to train model with previous trained weights. Then, your can use this cmd:



python -u train_se_renext_w_d.py --data-dir data/imagenet --data-type imagenet --depth 50 --batch-size 192 --num-group 64 --gpus=0,1,2,3 --model-load-epoch=50 --lr 0.001 --retrain



This means you can retrain your se_resnext model from epoch 50 and change lr=0.001 using 4 GPUs.



## Training curves



The training procedure is ongoing. So, I hope anyone who are mxnet fun can test this code with me. When I finish, I will update more information about training and validation.



**I update the learning curves of SE-Resnext 50 (batchsize=192) trained on imagenet datasets (Updated at Oct-20, 2017)**



![](se_resnext_50_imagenet_curves.png)



**I update the learning curves of SE-Resnext 50 (batchsize=192) trained on vggface datasets with the comparison of Resnext 50 (batchsize=256) (Updated at Oct-7, 2017)**. 



![](Curves.png)



## Pretrained model



**Pretrained model of SE-Resnext 50 on imagenet 1k dataset (Updated at Oct 20, 2017)**



We provide the pretrained model of SE-Resnext 50 trained on imagenet 1k dataset. The json file of se-resnext-imagenet-50-0-symbol can be found in master folder. The parameter file can be found in [here](https://drive.google.com/open?id=0B_M7XF_l0CzXOHNybXVWLWZteEE).



## TO BE CONTINUE



**Added SE-Resnet 18, 50, 101, 152 (Updated at Sep-27, 2017)**.



**Added SE-inception-v4 (Updated at Oct-30, 2017, Thanks to [Cher Keng Heng](https://github.com/hengck23))**.



**Added SE-inception-resnet-v2 (Updated at Nov-5, 2017)**.



Note, the backbone of inception-v4 or inception-resnet-v2 is modified from original version (299x299) to different version (224x224). If you want to deploy original one, you can easily modify to what you want.



**Gluon version is coming soon**.



## Reference



[1]  Jie Hu, Li Shen and Gang Sun. ["Squeeze-and-Excitation Networks"](https://arxiv.org/pdf/1709.01507v1.pdf) 



[2]  [Tensorflow implementation](https://github.com/taki0112/SENet-Tensorflow) of SENet from [taki0112's](https://github.com/taki0112)



[3]  [PyTorch implementation](https://github.com/kuangliu/pytorch-cifar/blob/master/models/senet.py)



[4]  [Inception V4](https://github.com/Trangle/mxnet-inception-v4) from [Trangle](https://github.com/Trangle)