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 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 proposed by Jie Hu et. al. They deployed this SE block in SENet and win the Imagenet 2017 classification task.
The author's caffe implementation can be found in his repo on GitHub.
This is an illustration of a Squeeze-and-Excitation block.
The SE-ResNet module is implemented as followed:
The SE-ResNext 50 is implemented following this table:
This MXNet implementation is refered to taki0112's tensorflow version. I also refered a PyTorch implementation from kuangliu. BTW, I add a dropout layer before the last FullyConnected layer. For Inception v4, I refers the MXnet implementation from Trangle. Finally, I attach the training code if you want to train your own data with SE-ResNext architecture by yourself.
Install MXNet(0.11.0) on GPUs mechine with NVIDIA CUDA 8.0, and it's better also installed with cuDNN v6 or later version (I'm not testing cuDNN v7).
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.
For data preparation, you can refer my pervious part of densenet or you can also visit the repo of Wei Wu. 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.
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.
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)
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).
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.
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).
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.
[1] Jie Hu, Li Shen and Gang Sun. "Squeeze-and-Excitation Networks"
[2] Tensorflow implementation of SENet from taki0112's
[4] Inception V4 from Trangle