diff --git a/train.py b/train.py
index bfed25d4c..086ae2665 100644
--- a/train.py
+++ b/train.py
@@ -346,24 +346,24 @@ def train(hyp, tb_writer, opt, device):
                                                  dataloader=testloader,
                                                  save_dir=log_dir)
 
-                # Write
-                with open(results_file, 'a') as f:
-                    f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
-                if len(opt.name) and opt.bucket:
-                    os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
+            # Write
+            with open(results_file, 'a') as f:
+                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
+            if len(opt.name) and opt.bucket:
+                os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
-                # Tensorboard
-                if tb_writer:
-                    tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
-                            'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                            'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
-                    for x, tag in zip(list(mloss[:-1]) + list(results), tags):
-                        tb_writer.add_scalar(tag, x, epoch)
+            # Tensorboard
+            if tb_writer:
+                tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
+                        'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
+                        'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
+                for x, tag in zip(list(mloss[:-1]) + list(results), tags):
+                    tb_writer.add_scalar(tag, x, epoch)
 
-                # Update best mAP
-                fi = fitness(np.array(results).reshape(1, -1))  # fitness_i = weighted combination of [P, R, mAP, F1]
-                if fi > best_fitness:
-                    best_fitness = fi
+            # Update best mAP
+            fi = fitness(np.array(results).reshape(1, -1))  # fitness_i = weighted combination of [P, R, mAP, F1]
+            if fi > best_fitness:
+                best_fitness = fi
 
             # Save model
             save = (not opt.nosave) or (final_epoch and not opt.evolve)