ultralytics/docs/en/macros/augmentation-args.md

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.