diff --git a/app/yolov5/train_server.py b/app/yolov5/train_server.py
index 3cc1c56..07ff125 100644
--- a/app/yolov5/train_server.py
+++ b/app/yolov5/train_server.py
@@ -467,30 +467,25 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
     # end training -----------------------------------------------------------------------------------------------------
     if RANK in {-1, 0}:
         LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
-        print('##############',best)
-        for f in best:
-            print('##################',f)
-            if os.path.exists(best):
-                strip_optimizer(f)  # strip optimizers
-                if f is best:
-                    LOGGER.info(f'\nValidating {f}...')
-                    results, _, _ = validate.run(
-                        data_dict,
-                        batch_size=batch_size // WORLD_SIZE * 2,
-                        imgsz=imgsz,
-                        model=attempt_load(f, device).half(),
-                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
-                        single_cls=single_cls,
-                        dataloader=val_loader,
-                        save_dir=save_dir,
-                        save_json=is_coco,
-                        verbose=True,
-                        plots=plots,
-                        callbacks=callbacks,
-                        compute_loss=compute_loss)  # val best model with plots
-                    if is_coco:
-                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
-
+        if os.path.exists(best):
+            strip_optimizer(best)  # strip optimizers
+            LOGGER.info(f'\nValidating {f}...')
+            results, _, _ = validate.run(
+                data_dict,
+                batch_size=batch_size // WORLD_SIZE * 2,
+                imgsz=imgsz,
+                model=attempt_load(best, device).half(),
+                iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
+                single_cls=single_cls,
+                dataloader=val_loader,
+                save_dir=save_dir,
+                save_json=is_coco,
+                verbose=True,
+                plots=plots,
+                callbacks=callbacks,
+                compute_loss=compute_loss)  # val best model with plots
+            if is_coco:
+                callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
         #callbacks.run('on_train_end', best, epoch, results)
 
     torch.cuda.empty_cache()