To run Reactnet for cifar10
- go to mobilenet/1_step1 directory
- run run.sh, or execute python train.py --batch_size=128 --learning_rate=5e-3 --epochs=60 --weight_decay=1e-5
- go to mobilenet/2_step2 directory
- run run.sh, or execute python train.py --batch_size=128 --learning_rate=5e-3 --epochs=35 --weight_decay=0, after making /models directory and put the model trained on step1
To run Reactnet with cutmix(data augmentation technique)
- go to mobilenet/1_step1 directory
- run run_cutmix.sh, or execute python train_cutmix_binary.py --batch_size=128 --learning_rate=5e-3 --epochs=60 --weight_decay=1e-5 --beta 1 --cutmix_prob 1
- go to mobilenet/2_step2 directory
- run run_cutmix.sh, or execute python train_cutmix_binary.pyy --batch_size=128 --learning_rate=5e-3 --epochs=35 --weight_decay=0 --beta 1 --cutmix_prob 1, after making /models directory and put the model trained on step1
To run Reactnet with mixup(data augmentation technique)
- go to mobilenet/1_step1 directory
- run run_mixup.sh, or execute python train_cutmix_binary.py --batch_size=128 --learning_rate=5e-3 --epochs=60 --weight_decay=1e-5 --beta 1 --cutmix_prob 1
- go to mobilenet/2_step2 directory
- run run_mixup.sh, or execute python train_cutmix_binary.py --batch_size=128 --learning_rate=5e-3 --epochs=35 --weight_decay=0 --beta 1 --cutmix_prob 1, after making /models directory and put the model trained on step1