| Argument                                                                                              | Type    | Default                 | Supported Tasks                                                     | Range         | Description                                                                                                                                                    |
| ----------------------------------------------------------------------------------------------------- | ------- | ----------------------- | ------------------------------------------------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`hsv_h`](../guides/yolo-data-augmentation.md#hue-adjustment-hsv_h)                                   | `float` | `{{ hsv_h }}`           | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0.0 - 1.0`   | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| [`hsv_s`](../guides/yolo-data-augmentation.md#saturation-adjustment-hsv_s)                            | `float` | `{{ hsv_s }}`           | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0.0 - 1.0`   | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions.                 |
| [`hsv_v`](../guides/yolo-data-augmentation.md#brightness-adjustment-hsv_v)                            | `float` | `{{ hsv_v }}`           | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0.0 - 1.0`   | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions.                               |
| [`degrees`](../guides/yolo-data-augmentation.md#rotation-degrees)                                     | `float` | `{{ degrees }}`         | `detect`, `segment`, `semantic`, `depth`, `pose`, `obb`             | `0.0 - 180`   | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations.                      |
| [`translate`](../guides/yolo-data-augmentation.md#translation-translate)                              | `float` | `{{ translate }}`       | `detect`, `segment`, `semantic`, `depth`, `pose`, `obb`             | `0.0 - 1.0`   | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects.                      |
| [`scale`](../guides/yolo-data-augmentation.md#scale-scale)                                            | `float` | `{{ scale }}`           | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0 - 1`       | Scales the image by a gain factor, simulating objects at different distances from the camera.                                                                  |
| [`shear`](../guides/yolo-data-augmentation.md#shear-shear)                                            | `float` | `{{ shear }}`           | `detect`, `segment`, `semantic`, `depth`, `pose`, `obb`             | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles.                                                    |
| [`perspective`](../guides/yolo-data-augmentation.md#perspective-perspective)                          | `float` | `{{ perspective }}`     | `detect`, `segment`, `semantic`, `depth`, `pose`, `obb`             | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space.                                     |
| [`flipud`](../guides/yolo-data-augmentation.md#flip-up-down-flipud)                                   | `float` | `{{ flipud }}`          | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0.0 - 1.0`   | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics.                    |
| [`fliplr`](../guides/yolo-data-augmentation.md#flip-left-right-fliplr)                                | `float` | `{{ fliplr }}`          | `detect`, `segment`, `semantic`, `depth`, `classify`, `pose`, `obb` | `0.0 - 1.0`   | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity.                        |
| [`bgr`](../guides/yolo-data-augmentation.md#bgr-channel-swap-bgr)                                     | `float` | `{{ bgr }}`             | `detect`, `segment`, `semantic`, `depth`, `pose`, `obb`             | `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`](../guides/yolo-data-augmentation.md#mosaic-mosaic)                                         | `float` | `{{ mosaic }}`          | `detect`, `segment`, `semantic`, `pose`, `obb`                      | `0.0 - 1.0`   | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.     |
| [`mixup`](../guides/yolo-data-augmentation.md#mixup-mixup)                                            | `float` | `{{ mixup }}`           | `detect`, `segment`, `semantic`, `pose`, `obb`                      | `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.  |
| [`cutmix`](../guides/yolo-data-augmentation.md#cutmix-cutmix)                                         | `float` | `{{ cutmix }}`          | `detect`, `segment`, `pose`, `obb`                                  | `0.0 - 1.0`   | Combines portions of two images, creating a partial blend while maintaining distinct regions. Enhances model robustness by creating occlusion scenarios.       |
| [`copy_paste`](../guides/yolo-data-augmentation.md#copy-paste-copy_paste)                             | `float` | `{{ copy_paste }}`      | `segment`                                                           | `0.0 - 1.0`   | Copies and pastes objects across images to increase object instances.                                                                                          |
| [`copy_paste_mode`](../guides/yolo-data-augmentation.md#copy-paste-mode-copy_paste_mode)              | `str`   | `{{ copy_paste_mode }}` | `segment`                                                           | -             | Specifies the `copy-paste` strategy to use. Options include `'flip'` and `'mixup'`.                                                                            |
| [`auto_augment`](../guides/yolo-data-augmentation.md#auto-augment-auto_augment)                       | `str`   | `{{ auto_augment }}`    | `classify`                                                          | -             | Applies a predefined augmentation policy (`'randaugment'`, `'autoaugment'`, or `'augmix'`) to enhance model performance through visual diversity.              |
| [`erasing`](../guides/yolo-data-augmentation.md#random-erasing-erasing)                               | `float` | `{{ erasing }}`         | `classify`                                                          | `0.0 - 1.0`   | Randomly erases regions of the image during training to encourage the model to focus on less obvious features.                                                 |
| [`augmentations`](../guides/yolo-data-augmentation.md#custom-albumentations-transforms-augmentations) | `list`  | `None`                  | `detect`, `segment`, `semantic`, `pose`, `obb`                      | -             | Custom Albumentations transforms for advanced data augmentation (Python API only). Accepts a list of transform objects for specialized augmentation needs.     |
