With respect to image data augmentation, how does fastai presizing workand why is it advantageous?

Fastai's presizing method works in two steps:

1. Resize: The first step is to resize the image to a large dimension. This resizing is not to the final size that the model will need, but to a larger size. This is done to ensure that all the details in the image are retained and not lost during the resizing process. The resizing is done in such a way that the original aspect ratio of the image is maintained, so the image is resized to a square shape by adding black pixels around the edges if necessary.

2. Random Crop: The second step is to take a random crop of the image at the final size needed. This crop is taken from a random location within the resized image. This introduces randomness into the model's training process, which helps to prevent overfitting.

The advantage of presizing is that it allows for data augmentation (like rotation, zooming, warping, etc.) to be done on the GPU rather than the CPU, which is much faster. Also, by doing the resizing first and then the data augmentation, it ensures that the data augmentation methods are being applied to the full resolution image, which results in higher quality transformations and therefore better model performance.