- Implementation with torch
- Other code for model and normalization available at https://github.com/LEEYEONSU/pytorch--SENet
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For training
python main.py --print_freq 32 --save_dir ./save_model/ --save_every 10 --lr 0.1 --weight_decay 1e-4 --momentum 0.9 --Epoch 500 --batch_size 128 --test_batch_size 100 --cutout False --n_masks 1 --length 16 --normalize batchnorm --alpha 1.0 --cutmix_prob 1.0 # For Cutmix
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Comparison between SE + resnet-32 + batchnorm & SE + resnet-32 + batchnorm + cutmix
SE + resnet-32 + batchnorm SE + resnet-32 + batchnorm + cutmix top - 1 error 4.76 4.50
- CIFAR -10
- SE (Squeeze and Excitation Network with Resnet-32)