Cropping

• Cropping images can be used as a practical processing step for image data with mixed height and width dimensions by cropping a central patch of each image
• Additionally, random cropping can also be used to provide an effect very similar to translations.
• whereas translations preserve the spatial dimensions of the image
• Depending on the reduction threshold chosen for cropping, this might not be a label-preserving transformation. Rotation
• Rotation augmentations are done by rotating the image right or left on an axis between 1° and 359°
• The safety of rotation augmentations is heavily determined by the rotation degree parameter.
• as the rotation degree increases, the label of the data is no longer preserved post-transformation. Translation
• Shifting images left, right, up, or down can be a very useful transformation to avoid positional bias in the data
• For example, if all the images in a dataset are centered, which is common in face recognition datasets, this would require the model to be tested on perfectly centered images as well.
• remaining space can be filled with either a constant value such as 0 s or 255 s, or it can be filled with random or Gaussian noise