diff --git a/CTRAIN/data_loaders/data_loaders.py b/CTRAIN/data_loaders/data_loaders.py
index b2ec0cf..6234e9d 100644
--- a/CTRAIN/data_loaders/data_loaders.py
+++ b/CTRAIN/data_loaders/data_loaders.py
@@ -59,7 +59,7 @@ def load_mnist(batch_size=64, normalise=True, train_transforms=[], val_split=Tru
     test_dataset = datasets.MNIST(root=data_root, train=False, transform=test_transform)
     if val_split:
         train_dataset, val_dataset = random_split(train_dataset, [0.8, 0.2])
-        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
+        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2)
     
 
     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
@@ -137,7 +137,7 @@ def load_cifar10(batch_size=64, normalise=True, train_transforms=[transforms.Ran
     test_dataset = datasets.CIFAR10(root=data_root, train=False, transform=test_transform, download=True)
     if val_split:
         train_dataset, val_dataset = random_split(train_dataset, [0.8, 0.2])
-        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
+        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2)
         val_loader.mean, val_loader.std = mean, std
     
 
@@ -229,7 +229,7 @@ def load_gtsrb(batch_size=64, normalise=True, train_transforms=[transforms.Rando
         train_dataset = Subset(train_dataset_ori, train_ids)
         val_dataset = Subset(train_dataset_ori, val_ids)
 
-        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
+        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
         val_loader.mean, val_loader.std = mean, std
 
     else:
@@ -371,7 +371,7 @@ def load_tinyimagenet(batch_size=64, normalise=True, train_transforms=[transform
     test_dataset = datasets.ImageFolder(root=data_root + '/tiny-imagenet-200/val/images', transform=test_transform)
     if val_split:
         train_dataset, val_dataset = random_split(train_dataset, [0.8, 0.2])
-        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
+        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2)
         val_loader.mean, val_loader.std = mean, std
     
 
diff --git a/CTRAIN/eval/eval.py b/CTRAIN/eval/eval.py
index a6da1e9..54074c7 100644
--- a/CTRAIN/eval/eval.py
+++ b/CTRAIN/eval/eval.py
@@ -320,15 +320,18 @@ def eval_adaptive(model, eps, data_loader, n_classes=10, test_samples=np.inf, de
     certified = torch.tensor([], device=device)
     total_images = 0
     
+    ibp_data_loader = DataLoader(data_loader.dataset, batch_size=data_loader.batch_size, shuffle=False)
+    ibp_data_loader.max, ibp_data_loader.min, ibp_data_loader.std = data_loader.max, data_loader.min, data_loader.std
+
     crown_data_loader = DataLoader(data_loader.dataset, batch_size=1, shuffle=False)
     crown_data_loader.max, crown_data_loader.min, crown_data_loader.std = data_loader.max, data_loader.min, data_loader.std
     
-    for batch_idx, (data, targets) in tqdm(enumerate(data_loader)):
+    for batch_idx, (data, targets) in tqdm(enumerate(ibp_data_loader)):
         certified_idx = torch.zeros(len(data), device=device, dtype=torch.bool)
         
-        ptb = PerturbationLpNorm(eps=eps, norm=np.inf, x_L=torch.clamp(data - eps, data_loader.min, data_loader.max).to(device), x_U=torch.clamp(data + eps, data_loader.min, data_loader.max).to(device))
+        ptb = PerturbationLpNorm(eps=eps, norm=np.inf, x_L=torch.clamp(data - eps, ibp_data_loader.min, ibp_data_loader.max).to(device), x_U=torch.clamp(data + eps, ibp_data_loader.min, ibp_data_loader.max).to(device))
         data, targets = data.to(device), targets.to(device)
-        if batch_idx * data_loader.batch_size >= test_samples:
+        if batch_idx * ibp_data_loader.batch_size >= test_samples:
             continue
         
         total_images += len(targets)
@@ -346,7 +349,7 @@ def eval_adaptive(model, eps, data_loader, n_classes=10, test_samples=np.inf, de
         
         data = data.to('cpu')
         certified_idx = certified_idx.to("cpu")
-        ptb = PerturbationLpNorm(eps=eps, norm=np.inf, x_L=torch.clamp(data[~certified_idx] - eps, data_loader.min, data_loader.max).to(device), x_U=torch.clamp(data[~certified_idx] + eps, data_loader.min, data_loader.max).to(device))
+        ptb = PerturbationLpNorm(eps=eps, norm=np.inf, x_L=torch.clamp(data[~certified_idx] - eps, ibp_data_loader.min, ibp_data_loader.max).to(device), x_U=torch.clamp(data[~certified_idx] + eps, ibp_data_loader.min, ibp_data_loader.max).to(device))
         data = data.to(device)
         certified_idx = certified_idx.to(device)