| Argument        | Type    | Default       | Range         | Description                                                                                                                                                               |
| --------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `hsv_h`         | `float` | `0.015`       | `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`         | `float` | `0.7`         | `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`         | `float` | `0.4`         | `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`       | `float` | `0.0`         | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations.                                 |
| `translate`     | `float` | `0.1`         | `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`         | `float` | `0.5`         | `>=0.0`       | Scales the image by a gain factor, simulating objects at different distances from the camera.                                                                             |
| `shear`         | `float` | `0.0`         | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles.                                                               |
| `perspective`   | `float` | `0.0`         | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space.                                                |
| `flipud`        | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics.                               |
| `fliplr`        | `float` | `0.5`         | `0.0 - 1.0`   | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity.                                   |
| `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.                                         |
| `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.                           |
| `crop_fraction` | `float` | `1.0`         | `0.1 - 1.0`   | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions.                      |
