Paper: Squeeze-and-Excitation Networks
This code takes NWPU-RESISC45 dataset as example. You can download NWPU-RESISC45 dataset and put them as follows.
├── train_resnext.py # train resnext script
├── train_senet.py # train senet script
├── split_datasets.py # split datasets script
├── se_resnet.py # network of se_resnet
├── se_resnext.py # network of se_resnext
├── resnext.py # network of resnext
├── read_image.py # my dataset read script
├── dataset # train and validation data
├── train
├──airplane
├── ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
├── ...
├── ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
├── ...
├──wetland
├── val
├──airplane
├── ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
├── ...
├── ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
├── ...
├──wetland
- If you want to train from scratch, you can run as follows:
python split_datasets.py
python train_senet.py --network se_resnext_50 --batch-size 256 --gpus 0,1,2,3
parameter --network can be se_resnet_18 or se_resnet_34 or se_resnet_50 or se_resnet_101 or se_resnet_152 or se_resnext_50 or se_resnext_101 or se_resnext_152.
- If you want to train from one checkpoint, you can run as follows(for example train from
epoch_4.pth, the--start-epoch` parameter is corresponding to the epoch of the checkpoint):
python train_senet.py --network se_resnext_50 --batch-size 256 --gpus 0,1,2,3 --resume output-senet/epoch_4.pth --start-epoch 4