diff --git a/alex_train_onesample.py b/alex_train_onesample.py
index 3487eb4bf72c945a1920e36f5e2285f3dfa0a196..57356a673b12089ae4d4fa9737c9d7830ed2406f 100644
--- a/alex_train_onesample.py
+++ b/alex_train_onesample.py
@@ -76,7 +76,7 @@ def train_model(args, model_id, dataset_id):
         for sample in seq_names:
             print(sample)
             # Take a smaller spatial crop to get more granularity in the temporal dimension. (same crop as in E-RAFT)
-            seq_datasets.append(SequenceEDENN(Path(root_folder),sample,num_bins=args.n_bins, crop_window=(288, 384), mode="val"))
+            seq_datasets.append(SequenceEDENN(Path(root_folder),"zurich_city_05_a",num_bins=args.n_bins, crop_window=(288, 384), mode="val"))
 
         # Naive 80/20 (change this I guess)
         print(len(seq_datasets[0]))
diff --git a/edenn/datasets/dsec.py b/edenn/datasets/dsec.py
index 59613569d8f37edd834fe950510bbd6e9da4f6c3..523690a73510e0ec1a0cc034cd32bf382d8d74d5 100644
--- a/edenn/datasets/dsec.py
+++ b/edenn/datasets/dsec.py
@@ -232,7 +232,6 @@ class SequenceEDENN(Dataset):
         else:
             # Convert events to voxel grid
             event_representation = self.events_to_voxel_grid(p, t, x_rect, y_rect)
-            self.flow_names[index] = "/vol/research/datasets/DSEC/train_optical_flow/zurich_city_05_a/flow/forward/000104.png"
             flow_GT, Valid2D = self.load_flow(Path(self.flow_names[index]))
         
         if crop_window is not None: # Lets expect crop window as a tuple of (H, W)