Preprocessing should be applied to your training and test set to assure learning and inference occurs on the same image properties. For example, if your model learns on 500x500 images, it should do inference on images of the same size.
Auto-orient strips your images of their EXIF data so that you see images displayed the same way they are stored on disk.
EXIF data determines the orientation of a given image. Applications (like Preview on Mac) use this data to display an image in a specific orientation, even if the orientation of how it is stored on disk differs. See this front page Hacker News discussion on how this may silently ruin your object detection models.
Roboflow recommends defaulting to leaving this on and checking how your images in inference are being fed to your model.
Resize changes your images size and, optionally, scale to a desired set of dimensions. Annotations are adjusted proportionally (except in the case of “fill” below).
Stretch to: Stretch your images to a preferred pixel-by-pixel dimension. Annotations are scaled proportionally.
[Converts an image with RGB channels into an image with a single grayscale channel. The value of each grayscale pixel is calculated as the weighted sum of the corresponding red, green and blue pixels as: Y = 0.2125 R + 0.7154 G + 0.0721 B
These weights are used by CRT phosphors as they better represent human perception of red, green and blue than equal weights. (VIA SCIKIT-IMAGE)
Converting to a single channel saves you memory.
Enhances an image with low contrast.
Contrast Stretching: the image is rescaled to include all intensities that fall within the 2nd and 98th percentiles. See more.