diff --git a/src/datasets.py b/src/datasets.py
index e1d02f94..7eef713e 100644
--- a/src/datasets.py
+++ b/src/datasets.py
@@ -317,26 +317,57 @@ def rectify_events(self, x: np.ndarray, y: np.ndarray):
         return rectify_map[y, x]
     
     def get_data(self, index) -> Dict[str, any]:
-        ts_start: int = self.timestamps_flow[index] - self.delta_t_us
-        ts_end: int = self.timestamps_flow[index]
+        #ts_start: int = self.timestamps_flow[index] - self.delta_t_us
+        #ts_end: int = self.timestamps_flow[index]
+        ts_start1: int = self.timestamps_flow[index] - self.delta_t_us
+        ts_end1: int = self.timestamps_flow[index]
+        ts_start2: int = self.timestamps_flow[index + 1] - self.delta_t_us
+        ts_end2: int = self.timestamps_flow[index + 1]
 
-        file_index = self.indices[index]
+        #file_index = self.indices[index]
+        file_index1 = self.indices[index]
+        file_index2 = self.indices[index + 1]
 
         output = {
-            'file_index': file_index,
-            'timestamp': self.timestamps_flow[index],
+            #'file_index': file_index,
+            'file_index1': file_index1,
+            'file_index2': file_index2,
+            #'timestamp': self.timestamps_flow[index],
+            'timestamp1': self.timestamps_flow[index],
+            'timestamp2': self.timestamps_flow[index + 1],
             'seq_name': self.seq_name
         }
         # Save sample for benchmark submission
         output['save_submission'] = file_index in self.idx_to_visualize
         output['visualize'] = self.visualize_samples
-        event_data = self.event_slicer.get_events(
-            ts_start, ts_end)
-        p = event_data['p']
-        t = event_data['t']
-        x = event_data['x']
-        y = event_data['y']
+        
+        #event_data = self.event_slicer.get_events(ts_start, ts_end)
+        event_data1 = self.event_slicer.get_events(ts_start1, ts_end1)
+        event_data2 = self.event_slicer.get_events(ts_start2, ts_end2)
+        
+        #p = event_data['p']
+        #t = event_data['t']
+        #x = event_data['x']
+        #y = event_data['y']
+        
+        #event_data1 = self.event_slicer.get_events(ts_start1, ts_end1)
+        p1 = event_data1['p']
+        t1 = event_data1['t']
+        x1 = event_data1['x']
+        y1 = event_data1['y']
+
+        #event_data2 = self.event_slicer.get_events(ts_start2, ts_end2)
+        p2 = event_data2['p']
+        t2 = event_data2['t']
+        x2 = event_data2['x']
+        y2 = event_data2['y']
+        
+        p = np.concatenate((p1, p2))
+        t = np.concatenate((t1, t2))
+        x = np.concatenate((x1, x2))
+        y = np.concatenate((y1, y2))
 
+        
         xy_rect = self.rectify_events(x, y)
         x_rect = xy_rect[:, 0]
         y_rect = xy_rect[:, 1]
@@ -344,19 +375,24 @@ def get_data(self, index) -> Dict[str, any]:
         if self.voxel_grid is None:
             raise NotImplementedError
         else:
-            event_representation = self.events_to_voxel_grid(
-                p, t, x_rect, y_rect)
+            event_representation = self.events_to_voxel_grid(p, t, x_rect, y_rect)
             output['event_volume'] = event_representation
         output['name_map'] = self.name_idx
         
-        if self.load_gt:
-            output['flow_gt'
-                ] = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]
-
-            output['flow_gt'
-                ][0] = torch.moveaxis(output['flow_gt'][0], -1, 0)
-            output['flow_gt'
-                ][1] = torch.unsqueeze(output['flow_gt'][1], 0)
+        if self.load_gt:   
+            #output['flow_gt'] = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]
+            flow_gt1 = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]
+            flow_gt2 = [torch.tensor(x) for x in self.load_flow(self.flow_png[index + 1])]
+            
+            #output['flow_gt'][0] = torch.moveaxis(output['flow_gt'][0], -1, 0)
+            #output['flow_gt'][1] = torch.unsqueeze(output['flow_gt'][1], 0)
+            flow_gt1[0] = torch.moveaxis(flow_gt1[0], -1, 0)
+            flow_gt1[1] = torch.unsqueeze(flow_gt1[1], 0)
+            flow_gt2[0] = torch.moveaxis(flow_gt2[0], -1, 0)
+            flow_gt2[1] = torch.unsqueeze(flow_gt2[1], 0)
+            
+            output['flow_gt'] = [flow_gt1, flow_gt2]
+            
         return output
 
     def __getitem__(self, idx):