diff --git a/train.py b/train.py
index ac38d04db..e4c9b6ae6 100644
--- a/train.py
+++ b/train.py
@@ -219,7 +219,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)  # run AutoAnchor
             model.half().float()  # pre-reduce anchor precision
 
-        callbacks.run('on_pretrain_routine_end', labels, names, plots)
+        callbacks.run('on_pretrain_routine_end', labels, names)
 
     # DDP mode
     if cuda and RANK != -1:
@@ -328,7 +328,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                 pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
                                      (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
-                callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
+                callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
                 if callbacks.stop_training:
                     return
             # end batch ------------------------------------------------------------------------------------------------
@@ -420,7 +420,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     if is_coco:
                         callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
 
-        callbacks.run('on_train_end', last, best, plots, epoch, results)
+        callbacks.run('on_train_end', last, best, epoch, results)
 
     torch.cuda.empty_cache()
     return results
diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py
index b9869df26..98a123eee 100644
--- a/utils/loggers/__init__.py
+++ b/utils/loggers/__init__.py
@@ -49,6 +49,7 @@ class Loggers():
         self.weights = weights
         self.opt = opt
         self.hyp = hyp
+        self.plots = not opt.noplots  # plot results
         self.logger = logger  # for printing results to console
         self.include = include
         self.keys = [
@@ -110,26 +111,26 @@ class Loggers():
         # Callback runs on train start
         pass
 
-    def on_pretrain_routine_end(self, labels, names, plots):
+    def on_pretrain_routine_end(self, labels, names):
         # Callback runs on pre-train routine end
-        if plots:
+        if self.plots:
             plot_labels(labels, names, self.save_dir)
-        paths = self.save_dir.glob('*labels*.jpg')  # training labels
-        if self.wandb:
-            self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
-        # if self.clearml:
-        #    pass  # ClearML saves these images automatically using hooks
+            paths = self.save_dir.glob('*labels*.jpg')  # training labels
+            if self.wandb:
+                self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
+            # if self.clearml:
+            #    pass  # ClearML saves these images automatically using hooks
 
-    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
+    def on_train_batch_end(self, model, ni, imgs, targets, paths):
         # Callback runs on train batch end
         # ni: number integrated batches (since train start)
-        if plots:
-            if ni == 0 and not self.opt.sync_bn and self.tb:
-                log_tensorboard_graph(self.tb, model, imgsz=list(imgs.shape[2:4]))
+        if self.plots:
             if ni < 3:
                 f = self.save_dir / f'train_batch{ni}.jpg'  # filename
                 plot_images(imgs, targets, paths, f)
-            if (self.wandb or self.clearml) and ni == 10:
+                if ni == 0 and self.tb and not self.opt.sync_bn:
+                    log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
+            if ni == 10 and (self.wandb or self.clearml):
                 files = sorted(self.save_dir.glob('train*.jpg'))
                 if self.wandb:
                     self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
@@ -197,9 +198,9 @@ class Loggers():
                                                       model_name='Latest Model',
                                                       auto_delete_file=False)
 
-    def on_train_end(self, last, best, plots, epoch, results):
+    def on_train_end(self, last, best, epoch, results):
         # Callback runs on training end, i.e. saving best model
-        if plots:
+        if self.plots:
             plot_results(file=self.save_dir / 'results.csv')  # save results.png
         files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
         files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
@@ -291,6 +292,7 @@ class GenericLogger:
             wandb.log_artifact(art)
 
 
+@threaded
 def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
     # Log model graph to TensorBoard
     try:
@@ -300,5 +302,5 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
         with warnings.catch_warnings():
             warnings.simplefilter('ignore')  # suppress jit trace warning
             tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
-    except Exception:
-        print('WARNING: TensorBoard graph visualization failure')
+    except Exception as e:
+        print(f'WARNING: TensorBoard graph visualization failure {e}')