You can customize augmentations by creating a custom dataset class and trainer. For example, you can replace the default Ultralytics classification augmentations with PyTorch's torchvision.transforms.Resize or other transforms. See the custom training example in the classification documentation for implementation details.

If the albumentations package is installed, Ultralytics automatically applies a set of extra image augmentations using it. These augmentations are handled internally and require no additional configuration. You can find the full list of applied transformations in our technical documentation, as well as in our Albumentations integration guide. Note that only the augmentations with a probability p greater than 0 are active. These are purposefully applied at low frequencies to mimic real-world visual artifacts, such as blur or grayscale effects. You can also provide your own custom Albumentations

Choosing the right augmentations depends on your specific use case and dataset. Here are a few general guidelines to help you decide: In most cases, slight variations in color and brightness are beneficial. The default values for hsv_h, hsv_s, and hsv_v are a solid starting point. If the camera's point of view is consistent and won't change once the model is deployed, you can likely skip geometric transformations such as rotation, translation, scale, shear, or perspective. However, if the camera angle may vary, and you need the model to be more robust, it's better to keep these augmentations.