diff --git a/oft/data/encoder.py b/oft/data/encoder.py
index 7805d9e..25582b1 100644
--- a/oft/data/encoder.py
+++ b/oft/data/encoder.py
@@ -167,12 +167,24 @@ def decode(self, heatmaps, pos_offsets, dim_offsets, ang_offsets, grid):
                 self.classnames[cid], pos, dim, ang, score))
         
         return objects
+    
+
+    def decode_batch(self, heatmaps, pos_offsets, dim_offsets, ang_offsets, 
+                     grids):
+        
+        boxes = list()
+        for hmap, pos_off, dim_off, ang_off, grid in zip(heatmaps, pos_offsets, 
+                                                         dim_offsets, 
+                                                         ang_offsets, grids):
+            boxes.append(self.decode(hmap, pos_off, dim_off, ang_off, grid))
+        
+        return boxes
 
     def _decode_heatmaps(self, heatmaps):
         peaks = non_maximum_suppression(heatmaps, self.sigma)
         scores = heatmaps[peaks]
         classids = torch.nonzero(peaks)[:, 0]
-        return peaks, scores, classids
+        return peaks, scores.cpu(), classids.cpu()
 
 
     def _decode_positions(self, pos_offsets, peaks, grid):
@@ -181,18 +193,20 @@ def _decode_positions(self, pos_offsets, peaks, grid):
         centers = (grid[1:, 1:] + grid[:-1, :-1]) / 2.
 
         # Un-normalize grid offsets
-        positions = pos_offsets.permute(0, 2, 3, 1) * self.pos_std + centers
-        return positions[peaks]
+        positions = pos_offsets.permute(0, 2, 3, 1) * self.pos_std.to(grid) \
+            + centers
+        return positions[peaks].cpu()
     
     def _decode_dimensions(self, dim_offsets, peaks):
         dim_offsets = dim_offsets.permute(0, 2, 3, 1)
         dimensions = torch.exp(
-            dim_offsets * self.log_dim_std + self.log_dim_mean)
-        return dimensions[peaks]
+            dim_offsets * self.log_dim_std.to(dim_offsets) \
+                + self.log_dim_mean.to(dim_offsets))
+        return dimensions[peaks].cpu()
     
     def _decode_angles(self, angle_offsets, peaks):
         cos, sin = torch.unbind(angle_offsets, 1)
-        return torch.atan2(sin, cos)[peaks]
+        return torch.atan2(sin, cos)[peaks].cpu()
 
 
 
diff --git a/oft/visualization/bbox.py b/oft/visualization/bbox.py
index eef47fa..8640849 100644
--- a/oft/visualization/bbox.py
+++ b/oft/visualization/bbox.py
@@ -36,7 +36,7 @@ def draw_bbox3d(obj, calib, ax, color='b'):
     corners = utils.bbox_corners(obj)
 
     # Project into image coordinates
-    img_corners = utils.perspective(calib, corners).numpy()
+    img_corners = utils.perspective(calib.cpu(), corners).numpy()
 
     # Draw polygons
     # Front face
@@ -57,7 +57,7 @@ def visualize_objects(image, calib, objects, cmap='tab20', ax=None):
     ax.clear()
 
     # Visualize image
-    ax.imshow(image.permute(1, 2, 0).numpy())
+    ax.imshow(image.permute(1, 2, 0).cpu().numpy())
     extents = ax.axis()
 
     # Visualize objects
@@ -67,8 +67,8 @@ def visualize_objects(image, calib, objects, cmap='tab20', ax=None):
     
     # Format axis
     ax.axis(extents)
-    ax.axis('off')
-    ax.grid('off')
+    ax.axis(False)
+    ax.grid(False)
     return ax
 
 
diff --git a/train.py b/train.py
index a4663df..b70359e 100644
--- a/train.py
+++ b/train.py
@@ -76,6 +76,13 @@ def train(args, dataloader, model, encoder, optimizer, summary, epoch):
             # Visualize scores
             summary.add_figure('train/score', 
                 visualize_score(pred_encoded[0], gt_encoded[0], grid), epoch)
+            
+            # Decode predictions
+            preds = encoder.decode_batch(*pred_encoded, grid)
+
+            # Visualise bounding boxes
+            summary.add_figure('train/bboxes',
+                visualise_bboxes(image, calib, objects, preds), epoch)
         
         # TODO decode and save results        
 
@@ -113,6 +120,9 @@ def validate(args, dataloader, model, encoder, summary, epoch):
                 pred_encoded, gt_encoded, args.loss_weights)       
             epoch_loss += loss_dict
         
+            # Decode predictions
+            preds = encoder.decode_batch(*pred_encoded, grid)
+        
         # Visualize predictions
         if i % args.vis_iter == 0:
 
@@ -123,7 +133,11 @@ def validate(args, dataloader, model, encoder, summary, epoch):
             summary.add_figure('val/score', 
                 visualize_score(pred_encoded[0], gt_encoded[0], grid), epoch)
             
-        # TODO decode and save results
+            # Visualise bounding boxes
+            summary.add_figure('val/bboxes',
+                visualise_bboxes(image, calib, objects, preds), epoch)
+            
+        
 
     # TODO evaluate
     
@@ -175,6 +189,22 @@ def visualize_score(scores, heatmaps, grid):
 
     return fig_score
 
+def visualise_bboxes(image, calib, objects, preds):
+
+    fig = plt.figure(num='bbox', figsize=(8, 6))
+    fig.clear()
+    ax1 = plt.subplot(211)
+    ax2 = plt.subplot(212)
+
+    oft.visualize_objects(image[0], calib[0], preds[0], ax=ax1)
+    ax1.set_title('Predictions')
+
+    oft.visualize_objects(image[0], calib[0], objects[0], ax=ax2)
+    ax2.set_title('Ground truth')
+
+    return fig
+
+
 
 def parse_args():
     parser = ArgumentParser()