diff --git a/export.py b/export.py
index 7b398fdc4d93..166b5f406a20 100644
--- a/export.py
+++ b/export.py
@@ -436,8 +436,7 @@ def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
               f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
         subprocess.run(cmd.split())
 
-        with open(f_json) as j:
-            json = j.read()
+        json = Path(f_json).read_text()
         with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
             subst = re.sub(
                 r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py
index 34704b625294..b95a463717f8 100644
--- a/utils/loggers/__init__.py
+++ b/utils/loggers/__init__.py
@@ -187,18 +187,16 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
 
     def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
         # Callback runs on model save event
-        if self.wandb:
-            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
+        if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
+            if self.wandb:
                 self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
-
-        if self.clearml:
-            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
+            if self.clearml:
                 self.clearml.task.update_output_model(model_path=str(last),
                                                       model_name='Latest Model',
                                                       auto_delete_file=False)
 
     def on_train_end(self, last, best, plots, epoch, results):
-        # Callback runs on training end
+        # Callback runs on training end, i.e. saving best model
         if 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'))]
@@ -220,15 +218,11 @@ def on_train_end(self, last, best, plots, epoch, results):
                                    aliases=['latest', 'best', 'stripped'])
             self.wandb.finish_run()
 
-        if self.clearml:
-            # Save the best model here
-            if not self.opt.evolve:
-                self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
-                                                      name='Best Model')
+        if self.clearml and not self.opt.evolve:
+            self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), name='Best Model')
 
-    def on_params_update(self, params):
+    def on_params_update(self, params: dict):
         # Update hyperparams or configs of the experiment
-        # params: A dict containing {param: value} pairs
         if self.wandb:
             self.wandb.wandb_run.config.update(params, allow_val_change=True)