diff --git a/docs/en/macros/augmentation-args.md b/docs/en/macros/augmentation-args.md
index b4d6c9df6d..bee27ddd0d 100644
--- a/docs/en/macros/augmentation-args.md
+++ b/docs/en/macros/augmentation-args.md
@@ -13,7 +13,7 @@
 | `bgr`             | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering.                                  |
 | `mosaic`          | `float` | `1.0`         | `0.0 - 1.0`   | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.                |
 | `mixup`           | `float` | `0.0`         | `0.0 - 1.0`   | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability.             |
-| `copy_paste`      | `float` | `0.0`         | `0.0 - 1.0`   | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion.                                         |
+| `copy_paste`      | `float` | `0.0`         | `0.0 - 1.0`   | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels.                              |
 | `copy_paste_mode` | `str`   | `flip`        | -             | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`).                                                                                      |
 | `auto_augment`    | `str`   | `randaugment` | -             | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
 | `erasing`         | `float` | `0.4`         | `0.0 - 0.9`   | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition.                           |