Startsida Utforska Hjälp
Logga in
AIModelWatermark
/
classification-main
2
0
Fork 0
Filer Ärenden 0 Pull-förfrågningar 0 Wiki
Träd: 3e05280c71
Grenar Taggar
classificati...
/
bash_train.sh

bash_train.sh 3.1 KB
Historik Rå

123456789101112131415161718192021222324252627 
  1. 

  2. # For 用于训练不同模型，以及保存相应的路径
    3. 

  3. # -------------------------------------------------------------------------------------------------------------------- #
    4. 

  4. python train.py --model 'resnet' --save_path './checkpoints/resnet/watermarking/best.pt' --save_path_last './checkpoints/resnet/watermarking/last.pt' --epoch 100
    5. 

  5. python train.py --model 'VGG19' --save_path './checkpoints/VGG19/watermarking/best.pt' --save_path_last './checkpoints/VGG19/watermarking/last.pt' --epoch 100
    6. 

  6. python train.py --model 'Alexnet' --input_size 112 --save_path './checkpoints/Alexnet/watermarking/best.pt' --save_path_last './checkpoints/Alexnet/watermarking/last.pt' --epoch 100
    7. 

  7. python train.py --model 'mobilenetv2' --save_path './checkpoints/mobilenetv2/watermarking/best.pt' --save_path_last './checkpoints/mobilenetv2/watermarking/last.pt' --epoch 100
    8. 

  8. python train.py --model 'GoogleNet' --input_size 32 --save_path './checkpoints/GoogleNet/watermarking/best.pt' --save_path_last './checkpoints/GoogleNet/watermarking/last.pt' --epoch 100
    9. 

  9. python train.py --model 'badnet' --input_size 32 --save_path './checkpoints/badnet/watermarking/best.pt' --save_path_last './checkpoints/badnet/watermarking/last.pt' --epoch 100
    10. 

  10. python train.py --model 'efficientnet' --input_size 32 --save_path './checkpoints/efficientnetv2_s/watermarking/best.pt' --save_path_last './checkpoints/efficientnetv2_s/watermarking/last.pt' --epoch 100
    11. 

  11. 

  12. 

  13. 

  14. # For 用于剪枝模型，剪枝后微调训练，保存剪枝后模型路径，以及验证微调模型准确性
    15. 

  15. # -------------------------------------------------------------------------------------------------------------------- #
    16. 

  16. python train.py --model 'resnet'  --prune True --prune_weight './checkpoints/resnet/watermarking/best.pt' --prune_save './checkpoints/resnet/watermarking/prune_best.pt' --epoch 40
    17. 

  17. python train.py --model 'VGG19'  --prune True --prune_weight './checkpoints/VGG19/watermarking/best.pt' --prune_save './checkpoints/VGG19/watermarking/prune_best.pt' --epoch 40
    18. 

  18. python train.py --model 'Alexnet'  --prune True --input_size 112 --prune_weight './checkpoints/Alexnet/watermarking/best.pt' --prune_save './checkpoints/Alexnet/watermarking/prune_best.pt' --epoch 40
    19. 

  19. python train.py --model 'mobilenetv2'  --prune True --prune_weight './checkpoints/mobilenetv2/watermarking/best.pt' --prune_save './checkpoints/mobilenetv2/watermarking/prune_best.pt' --epoch 40
    20. 

  20. python train.py --model 'GoogleNet' --input_size 32 --prune True --prune_weight './checkpoints/GoogleNet/watermarking/best.pt' --prune_save './checkpoints/GoogleNet/watermarking/prune_best.pt' --epoch 40
    21. 

  21. python train.py --model 'badnet' --input_size 32 --prune True --prune_weight './checkpoints/badnet/watermarking/best.pt' --prune_save './checkpoints/badnet/watermarking/prune_best.pt' --epoch 40
    22. 

  22. python train.py --model 'efficientnet' --input_size 32 --prune True --prune_weight './checkpoints/efficientnetv2_s/watermarking/best.pt' --prune_save './checkpoints/efficientnetv2_s/watermarking/prune_best.pt' --epoch 40
    23. 

  23. 

  24. 

  25. 

  26. # For 用于剪枝模型后边缘部署
    27. 

  27. # -------------------------------------------------------------------------------------------------------------------- #
    28.