diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py
index 7453632c..5c9cb21c 100644
--- a/ultralytics/models/fastsam/predict.py
+++ b/ultralytics/models/fastsam/predict.py
@@ -15,7 +15,6 @@ class FastSAMPredictor(DetectionPredictor):
         self.args.task = 'segment'
 
     def postprocess(self, preds, img, orig_imgs):
-        """TODO: filter by classes."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,
@@ -32,22 +31,20 @@ class FastSAMPredictor(DetectionPredictor):
             full_box[0][6:] = p[0][critical_iou_index][:, 6:]
             p[0][critical_iou_index] = full_box
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
         for i, pred in enumerate(p):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            img_path = self.batch[0][i]
             if not len(pred):  # save empty boxes
-                results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))
-                continue
-            if self.args.retina_masks:
-                if not isinstance(orig_imgs, torch.Tensor):
+                masks = None
+            elif self.args.retina_masks:
+                if is_list:
                     pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
                 masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
             else:
                 masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
-                if not isinstance(orig_imgs, torch.Tensor):
+                if is_list:
                     pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            results.append(
-                Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results
diff --git a/ultralytics/models/nas/predict.py b/ultralytics/models/nas/predict.py
index 32f031c7..b29d2dc2 100644
--- a/ultralytics/models/nas/predict.py
+++ b/ultralytics/models/nas/predict.py
@@ -24,11 +24,11 @@ class NASPredictor(BasePredictor):
                                         classes=self.args.classes)
 
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            if not isinstance(orig_imgs, torch.Tensor):
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            if is_list:
                 pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
-            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
+            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/rtdetr/predict.py b/ultralytics/models/rtdetr/predict.py
index d966d0d0..b81e97eb 100644
--- a/ultralytics/models/rtdetr/predict.py
+++ b/ultralytics/models/rtdetr/predict.py
@@ -28,6 +28,7 @@ class RTDETRPredictor(BasePredictor):
         nd = preds[0].shape[-1]
         bboxes, scores = preds[0].split((4, nd - 4), dim=-1)
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, bbox in enumerate(bboxes):  # (300, 4)
             bbox = ops.xywh2xyxy(bbox)
             score, cls = scores[i].max(-1, keepdim=True)  # (300, 1)
@@ -35,14 +36,13 @@ class RTDETRPredictor(BasePredictor):
             if self.args.classes is not None:
                 idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx
             pred = torch.cat([bbox, score, cls], dim=-1)[idx]  # filter
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            orig_img = orig_imgs[i] if is_list else orig_imgs
             oh, ow = orig_img.shape[:2]
-            if not isinstance(orig_imgs, torch.Tensor):
+            if is_list:
                 pred[..., [0, 2]] *= ow
                 pred[..., [1, 3]] *= oh
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
-            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
+            img_path = self.batch[0][i]
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
 
     def pre_transform(self, im):
diff --git a/ultralytics/models/sam/predict.py b/ultralytics/models/sam/predict.py
index 8c0604d5..221d3ce4 100644
--- a/ultralytics/models/sam/predict.py
+++ b/ultralytics/models/sam/predict.py
@@ -318,8 +318,9 @@ class Predictor(BasePredictor):
         pred_bboxes = preds[2] if self.segment_all else None
         names = dict(enumerate(str(i) for i in range(len(pred_masks))))
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, masks in enumerate([pred_masks]):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            orig_img = orig_imgs[i] if is_list else orig_imgs
             if pred_bboxes is not None:
                 pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
                 cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
@@ -327,9 +328,8 @@ class Predictor(BasePredictor):
 
             masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
             masks = masks > self.model.mask_threshold  # to bool
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
-            results.append(Results(orig_img=orig_img, path=img_path, names=names, masks=masks, boxes=pred_bboxes))
+            img_path = self.batch[0][i]
+            results.append(Results(orig_img, path=img_path, names=names, masks=masks, boxes=pred_bboxes))
         # Reset segment-all mode.
         self.segment_all = False
         return results
diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py
index 95b17e44..18815137 100644
--- a/ultralytics/models/yolo/classify/predict.py
+++ b/ultralytics/models/yolo/classify/predict.py
@@ -39,10 +39,9 @@ class ClassificationPredictor(BasePredictor):
     def postprocess(self, preds, img, orig_imgs):
         """Post-processes predictions to return Results objects."""
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
-            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
-
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            img_path = self.batch[0][i]
+            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 46de75f5..fdf3e176 100644
--- a/ultralytics/models/yolo/detect/predict.py
+++ b/ultralytics/models/yolo/detect/predict.py
@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import torch
-
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
 from ultralytics.utils import ops
@@ -32,11 +30,11 @@ class DetectionPredictor(BasePredictor):
                                         classes=self.args.classes)
 
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            if not isinstance(orig_imgs, torch.Tensor):
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            if is_list:
                 pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
-            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
+            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 1a410a17..bf89fb1a 100644
--- a/ultralytics/models/yolo/pose/predict.py
+++ b/ultralytics/models/yolo/pose/predict.py
@@ -38,18 +38,13 @@ class PosePredictor(DetectionPredictor):
                                         nc=len(self.model.names))
 
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         for i, pred in enumerate(preds):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            shape = orig_img.shape
-            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            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, shape)
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
+            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
+            img_path = self.batch[0][i]
             results.append(
-                Results(orig_img=orig_img,
-                        path=img_path,
-                        names=self.model.names,
-                        boxes=pred[:, :6],
-                        keypoints=pred_kpts))
+                Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts))
         return results
diff --git a/ultralytics/models/yolo/segment/predict.py b/ultralytics/models/yolo/segment/predict.py
index 866c32ca..3d650a1a 100644
--- a/ultralytics/models/yolo/segment/predict.py
+++ b/ultralytics/models/yolo/segment/predict.py
@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import torch
-
 from ultralytics.engine.results import Results
 from ultralytics.models.yolo.detect.predict import DetectionPredictor
 from ultralytics.utils import DEFAULT_CFG, ops
@@ -27,7 +25,6 @@ class SegmentationPredictor(DetectionPredictor):
         self.args.task = 'segment'
 
     def postprocess(self, preds, img, orig_imgs):
-        """TODO: filter by classes."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,
@@ -36,22 +33,20 @@ class SegmentationPredictor(DetectionPredictor):
                                     nc=len(self.model.names),
                                     classes=self.args.classes)
         results = []
+        is_list = isinstance(orig_imgs, list)  # input images are a list, not a torch.Tensor
         proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
         for i, pred in enumerate(p):
-            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            path = self.batch[0]
-            img_path = path[i] if isinstance(path, list) else path
+            orig_img = orig_imgs[i] if is_list else orig_imgs
+            img_path = self.batch[0][i]
             if not len(pred):  # save empty boxes
-                results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))
-                continue
-            if self.args.retina_masks:
-                if not isinstance(orig_imgs, torch.Tensor):
+                masks = None
+            elif self.args.retina_masks:
+                if is_list:
                     pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
                 masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
             else:
                 masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
-                if not isinstance(orig_imgs, torch.Tensor):
+                if is_list:
                     pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            results.append(
-                Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results