An implementation of SENet, proposed in Squeeze-and-Excitation Networks by Jie Hu, Li Shen and Gang Sun, who are the winners of ILSVRC 2017 classification competition.
Now SE-ResNet (18, 34, 50, 101, 152/20, 32) and SE-Inception-v3 are implemented.
python cifar.py runs SE-ResNet20 with Cifar10 dataset.
python imagenet.py IMAGENET_ROOT runs SE-ResNet50 with ImageNet(2012) dataset.
You need to prepare dataset by yourself
First download files and then follow the instruction.
The number of workers and some hyper parameters are fixed so check and change them if you need.
This script uses all GPUs available. To specify GPUs, use CUDA_VISIBLE_DEVICES variable. (e.g. CUDA_VISIBLE_DEVICES=1,2 to use GPU 1 and 2)