diff --git a/NewtonOutput/best_model.pt b/NewtonOutput/best_model.pt
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diff --git a/NewtonOutput/confiance_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/confiance_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png
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diff --git a/NewtonOutput/confusion_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/confusion_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png
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diff --git a/NewtonOutput/training_plot_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/training_plot_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png
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diff --git a/dataset/dataset.py b/dataset/dataset.py
index f064c404c9126088164e11b1040bdf5a93362d41..0ea51076a6b3d6382b5c9b6d18902bbb6098e096 100644
--- a/dataset/dataset.py
+++ b/dataset/dataset.py
@@ -252,7 +252,7 @@ def load_data_duo(base_dir, batch_size, args, shuffle=True):
         dataset=train_dataset,
         batch_size=batch_size,
         shuffle=shuffle,
-        num_workers=8,
+        num_workers=1,
         collate_fn=None,
         pin_memory=False,
     )
@@ -260,7 +260,7 @@ def load_data_duo(base_dir, batch_size, args, shuffle=True):
         dataset=val_dataset,
         batch_size=batch_size,
         shuffle=shuffle,
-        num_workers=8,
+        num_workers=1,
         collate_fn=None,
         pin_memory=False,
     )
diff --git a/main.py b/main.py
index 54886332f556978232ca685f985f1d66fe7f70d6..2cd28d3050f8ab7ae463989b269f1b2bfc129394 100644
--- a/main.py
+++ b/main.py
@@ -254,10 +254,10 @@ def run_duo(args):
     plt.plot(train_loss)
     plt.plot(train_loss)
 
-    plt.savefig(f'NewtonOutput/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+    plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
     #load and evaluate best model
     load_model(model, args.save_path)
-    make_prediction_duo(model,data_test, f'NewtonOutput/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+    make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
     return best_loss,best_acc
 
 def make_prediction_duo(model, data, f_name):