diff --git a/datasets.py b/datasets.py
index e3b93fa..f4a48de 100644
--- a/datasets.py
+++ b/datasets.py
@@ -50,26 +50,13 @@ class CustomDataset(Dataset):
         target["iscrowd"] = iscrowd
         image_id = torch.tensor([idx])
         target["image_id"] = image_id
-        # target["image_name"] = image_name #ToDo: implement this as 3rd return...
+        target["image_name"] = image_name #ToDo: implement this as 3rd return...
 
         return img_tensor, target
 
     def __len__(self):
         return len(self.all_images)
 
-def create_train_test_dataset(path, test_size=0.2):
-    files = glob.glob(os.path.join(path, '*.png'))
-    train_test_idx = np.arange(len(files), dtype=int)
-    np.random.shuffle(train_test_idx)
-
-    train_idx = train_test_idx[int(test_size*len(train_test_idx)):]
-    test_idx = train_test_idx[:int(test_size*len(train_test_idx))]
-
-    train_data = CustomDataset(path)
-    test_data = CustomDataset(path)
-
-    return train_data, test_data
-
 def create_train_or_test_dataset(path, train=True):
     if train == True:
         pfx='train'
diff --git a/inference.py b/inference.py
index 88b5357..fafb828 100644
--- a/inference.py
+++ b/inference.py
@@ -45,6 +45,9 @@ def infere_model(test_loader, model, detection_th=0.8):
     prog_bar = tqdm(test_loader, total=len(test_loader))
     for samples, targets in prog_bar:
         images = list(image.to(DEVICE) for image in samples)
+
+        embed()
+        quit()
         targets = [{k: v for k, v in t.items()} for t in targets]
 
         with torch.inference_mode():