config.yaml

model_name: "Watson" # Moriaty_untrained, Moriaty, Lestrade, Watson, Sherlock, Polimi

finetune: True # To unfreeze all layers (from pretrained network)

path_model_moriaty: "./models/checkpoints/moriaty.pt"
path_model_moriaty_adv: "./models/checkpoints/moriaty_adv.pt"

path_model_watson: "./models/checkpoints/watson.pt"
path_model_watson_finetuned: "./models/checkpoints/watson_finetuned.pt"
path_model_sherlock: "./models/checkpoints/sherlock.pt"

train_path: "./data/train"
val_path: "./data/val"
test_path: "./data/test"
eval_res_path: "./eval_results"
test_adv_path: "./data/test_adv_v4" # test_adv_v1 (FGSM 0.01), test_adv_v2 (LinPGD 0.05), test_adv_v3 (LinPGD 0.01)

test_adv_bool: True # Evaluate on the adversarial test set (True, False)

seed: 1234
batch_size: 32
learning_rate: 0.00001
epochs_training: 20
epochs_adversarial_training: 20
early_stopping_patience: 3
num_workers: 24

adversarial_attack_type: "LinfPGD" # LinfPGD, PGD, FGSM
adversarial_eps: 0.05

wandb_logging: True