diff --git a/ultralytics/yolo/engine/validator.py b/ultralytics/yolo/engine/validator.py
index 4c756f0..aae1daa 100644
--- a/ultralytics/yolo/engine/validator.py
+++ b/ultralytics/yolo/engine/validator.py
@@ -109,9 +109,6 @@ class BaseValidator:
                 self.plot_val_samples(batch, batch_i)
                 self.plot_predictions(batch, preds, batch_i)
 
-            if self.args.save_json:
-                self.pred_to_json(preds, batch)
-
         stats = self.get_stats()
         self.check_stats(stats)
         self.print_results()
@@ -126,8 +123,7 @@ class BaseValidator:
                 with open(str(self.save_dir / "predictions.json"), 'w') as f:
                     self.logger.info(f"Saving {f.name}...")
                     json.dump(self.jdict, f)  # flatten and save
-
-            stats = self.eval_json(stats)
+                stats = self.eval_json(stats)  # update stats
             return stats
 
     def get_dataloader(self, dataset_path, batch_size):
diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py
index 763d86d..a8ce7f3 100644
--- a/ultralytics/yolo/v8/detect/val.py
+++ b/ultralytics/yolo/v8/detect/val.py
@@ -71,7 +71,7 @@ class DetectionValidator(BaseValidator):
 
     def update_metrics(self, preds, batch):
         # Metrics
-        for si, (pred) in enumerate(preds):
+        for si, pred in enumerate(preds):
             labels = self.targets[self.targets[:, 0] == si, 1:]
             nl, npr = labels.shape[0], pred.shape[0]  # number of labels, predictions
             shape = batch["ori_shape"][si]
@@ -103,11 +103,11 @@ class DetectionValidator(BaseValidator):
                     self.confusion_matrix.process_batch(predn, labelsn)
             self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0]))  # (conf, pcls, tcls)
 
-            # TODO: Save/log
-            '''
-            if self.args.save_txt:
-                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
-            '''
+            # Save
+            if self.args.save_json:
+                self.pred_to_json(predn, batch["im_file"][si])
+            # if self.args.save_txt:
+            #    save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
 
     def get_stats(self):
         stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*self.stats)]  # to numpy
@@ -197,18 +197,17 @@ class DetectionValidator(BaseValidator):
                     fname=self.save_dir / f'val_batch{ni}_pred.jpg',
                     names=self.names)  # pred
 
-    def pred_to_json(self, preds, batch):
-        for i, f in enumerate(batch["im_file"]):
-            stem = Path(f).stem
-            image_id = int(stem) if stem.isnumeric() else stem
-            box = ops.xyxy2xywh(preds[i][:, :4])  # xywh
-            box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
-            for p, b in zip(preds[i].tolist(), box.tolist()):
-                self.jdict.append({
-                    'image_id': image_id,
-                    'category_id': self.class_map[int(p[5])],
-                    'bbox': [round(x, 3) for x in b],
-                    'score': round(p[4], 5)})
+    def pred_to_json(self, predn, filename):
+        stem = Path(filename).stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        box = ops.xyxy2xywh(predn[:, :4])  # xywh
+        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+        for p, b in zip(predn.tolist(), box.tolist()):
+            self.jdict.append({
+                'image_id': image_id,
+                'category_id': self.class_map[int(p[5])],
+                'bbox': [round(x, 3) for x in b],
+                'score': round(p[4], 5)})
 
     def eval_json(self, stats):
         if self.args.save_json and self.is_coco and len(self.jdict):