diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py
index 0f9b4c6acb..1ea8e3b31c 100644
--- a/ultralytics/data/augment.py
+++ b/ultralytics/data/augment.py
@@ -794,106 +794,7 @@ class LetterBox:
         return labels
 
 
-class CopyPaste(BaseMixTransform):
-    """
-    Implements Copy-Paste augmentation as described in https://arxiv.org/abs/2012.07177.
-
-    This class applies Copy-Paste augmentation on images and their corresponding instances.
-
-    Attributes:
-        dataset: The dataset on which the copypaste augmentation is applied.
-        pre_transform: The pre-transforms for the mixed labels.
-        p (float): Probability of applying the Copy-Paste augmentation. Must be between 0 and 1.
-
-    Methods:
-        __call__: Applies Copy-Paste augmentation to given image and instances.
-
-    Examples:
-        >>> copypaste = CopyPaste(dataset, p=0.5)
-        >>> augmented_labels = copypaste(labels)
-        >>> augmented_image = augmented_labels['img']
-    """
-
-    def __init__(self, dataset, pre_transform=None, p=0.5) -> None:
-        """Initializes CopyPaste object with dataset, pre_transform, and probability of applying MixUp."""
-        super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
-
-    def get_indexes(self):
-        """
-        Get a random index from the dataset.
-
-        This method returns a single random index from the dataset, which is used to select an image for MixUp
-        augmentation.
-
-        Returns:
-            (int): A random integer index within the range of the dataset length.
-
-        Examples:
-            >>> copypaste = CopyPaste(dataset)
-            >>> index = copypaste.get_indexes()
-            >>> print(index)
-            42
-        """
-        return random.randint(0, len(self.dataset) - 1)
-
-    def _mix_transform(self, labels):
-        """Applies CopyPaste augmentation."""
-        labels2 = labels["mix_labels"][0]
-        im = labels["img"]
-        cls = labels["cls"]
-        h, w = im.shape[:2]
-        instances = labels.pop("instances")
-        instances.convert_bbox(format="xyxy")
-        instances.denormalize(w, h)
-
-        im_new = np.zeros(im.shape, np.uint8)
-        instances2 = labels2.pop("instances")
-        ioa = bbox_ioa(instances2.bboxes, instances.bboxes)  # intersection over area, (N, M)
-        indexes = np.nonzero((ioa < 0.30).all(1))[0]  # (N, )
-        n = len(indexes)
-        # for j in random.sample(list(indexes), k=round(self.p * n)):
-        sorted_idx = np.argsort(ioa.max(1)[indexes])
-        indexes = indexes[sorted_idx]
-        for j in indexes[: round(self.p * n)]:
-            cls = np.concatenate((cls, labels2["cls"][[j]]), axis=0)
-            instances = Instances.concatenate((instances, instances2[[j]]), axis=0)
-            cv2.drawContours(im_new, instances2.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)
-
-        result = labels2["img"]  # augment segments
-        i = im_new.astype(bool)
-        im[i] = result[i]
-
-        labels["img"] = im
-        labels["cls"] = cls
-        labels["instances"] = instances
-        return labels
-
-    def __call__(self, labels):
-        """Applies pre-processing transforms and copy_paste transforms to labels data."""
-        if len(labels["instances"].segments) == 0 or self.p == 0:
-            return labels
-        # Get index of one or three other images
-        indexes = self.get_indexes()
-        if isinstance(indexes, int):
-            indexes = [indexes]
-
-        # Get images information will be used for Mosaic or MixUp
-        mix_labels = [self.dataset.get_image_and_label(i) for i in indexes]
-
-        if self.pre_transform is not None:
-            for i, data in enumerate(mix_labels):
-                mix_labels[i] = self.pre_transform(data)
-        labels["mix_labels"] = mix_labels
-
-        # Update cls and texts
-        labels = self._update_label_text(labels)
-        # Mosaic or MixUp
-        labels = self._mix_transform(labels)
-        labels.pop("mix_labels", None)
-        return labels
-
-
-class OldCopyPaste:
+class CopyPaste:
     """
     Implements the Copy-Paste augmentation as described in the paper https://arxiv.org/abs/2012.07177. This class is
     responsible for applying the Copy-Paste augmentation on images and their corresponding instances.
@@ -947,7 +848,7 @@ class OldCopyPaste:
             # for j in random.sample(list(indexes), k=round(self.p * n)):
             sorted_idx = np.argsort(ioa.max(1)[indexes])
             indexes = indexes[sorted_idx]
-            for j in indexes[: round(self.p * n)]:
+            for j in indexes[:round(self.p * n)]:
                 cls = np.concatenate((cls, cls[[j]]), axis=0)
                 instances = Instances.concatenate((instances, ins_flip[[j]]), axis=0)
                 cv2.drawContours(im_new, instances.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)
@@ -1195,22 +1096,18 @@ class RandomLoadText:
 
 def v8_transforms(dataset, imgsz, hyp, stretch=False):
     """Convert images to a size suitable for YOLOv8 training."""
-    mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic)
-    affine = RandomPerspective(
-        degrees=hyp.degrees,
-        translate=hyp.translate,
-        scale=hyp.scale,
-        shear=hyp.shear,
-        perspective=hyp.perspective,
-        pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
-    )
     pre_transform = Compose(
         [
-            mosaic,
-            # CopyPaste(dataset, pre_transform=mosaic, p=hyp.copy_paste),
-            # OldCopyPaste(p=hyp.copy_paste),
-            affine,
-            CopyPaste(dataset, pre_transform=Compose([mosaic, affine]), p=hyp.copy_paste),
+            Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic),
+            CopyPaste(p=hyp.copy_paste),
+            RandomPerspective(
+                degrees=hyp.degrees,
+                translate=hyp.translate,
+                scale=hyp.scale,
+                shear=hyp.shear,
+                perspective=hyp.perspective,
+                pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
+            ),
         ]
     )
     flip_idx = dataset.data.get("flip_idx", [])  # for keypoints augmentation