diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py
index 853ef048..1ca42fe8 100644
--- a/ultralytics/models/yolo/classify/predict.py
+++ b/ultralytics/models/yolo/classify/predict.py
@@ -54,8 +54,6 @@ class ClassificationPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
-            img_path = self.batch[0][i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
         return results
diff --git a/ultralytics/models/yolo/detect/predict.py b/ultralytics/models/yolo/detect/predict.py
index 3a0c6287..9842928d 100644
--- a/ultralytics/models/yolo/detect/predict.py
+++ b/ultralytics/models/yolo/detect/predict.py
@@ -35,9 +35,7 @@ class DetectionPredictor(BasePredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            img_path = self.batch[0][i]
             results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
diff --git a/ultralytics/models/yolo/pose/predict.py b/ultralytics/models/yolo/pose/predict.py
index 7c55709f..911c424f 100644
--- a/ultralytics/models/yolo/pose/predict.py
+++ b/ultralytics/models/yolo/pose/predict.py
@@ -46,12 +46,10 @@ class PosePredictor(DetectionPredictor):
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i]
+        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
             pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
             pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
-            img_path = self.batch[0][i]
             results.append(
                 Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
             )
diff --git a/ultralytics/models/yolo/segment/predict.py b/ultralytics/models/yolo/segment/predict.py
index 9d7015ff..d007eeea 100644
--- a/ultralytics/models/yolo/segment/predict.py
+++ b/ultralytics/models/yolo/segment/predict.py
@@ -42,9 +42,7 @@ class SegmentationPredictor(DetectionPredictor):
 
         results = []
         proto = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]  # tuple if PyTorch model or array if exported
-        for i, pred in enumerate(p):
-            orig_img = orig_imgs[i]
-            img_path = self.batch[0][i]
+        for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
             if not len(pred):  # save empty boxes
                 masks = None
             elif self.args.retina_masks: