JitterResize updates

[martin-gorner]

Looking through the friction log: https://docs.google.com/document/d/1xoq2axs1QHWvRjKRQP-L8HSlputy3N3R9Q5Glp-lN4g/edit#bookmark=id.262un4qwr0if
The most important tasks for now are:

• name change -> RandomZoomAndCrop
• crop box params change: remove target_size, split crop size into crrop_width, crop_height
• Deprecate and delete existing RandomlyZoomedCrop layer
• To align params on the new "Zoom" naming, params change scale_factor => zoom_factor (this might be the inverse of scale_factor, please check)
  The other 5 points are less important, nice-to-have later.

[LukeWood]

There are cases where multiple input sizes are needed for preprocessing techniques: https://github.com/tensorflow/models/blob/526e80f0c03b876757f845b3e8cb5c61f7c96e2f/official/vision/dataloaders/segmentation_input.py#L75

There are others as well.

[martin-gorner]

The goal is to be consistent with all other preprocessing layers which use explicit width and height parameters (or x and y) and not size=(...,...) tuples

keras_cv.layers.RandomCutout(height_factor, width_factor, ...)
keras_cv.layers.RandomShear(x_factor, y_factor, ...)
keras_cv.layers.Resizing(height, width, ...)
keras.layers.CenterCrop(height, width, ...)
keras.layers.RandomCrop(height, width, ...)
keras.layers.RandomTranslation(height_factor, width_factor, ...)
keras.layers.RandomZoom(height_factor, width_factor, ...)

[LukeWood]

I think for the height and width distortion factors separating them makes sense.

For CenterCrop+RandomCrop+Resizing I wish we had gone with a tuple - though was not around for those designs.

[james77777778]

I'm willing to contribute but I can't access the google docs.
Is this contributions-welcome?

[jbischof]

Thanks for the issue @martin-gorner! Ideally we could offer artifacts as a github gist to be accessible to the community.

[sachinprasadhs]

Thanks for reporting the issue! We have consolidated the development of KerasCV into the new KerasHub package, which supports image, text, and multi-modal models. Please read keras-team/keras-hub#1831. KerasHub will support all the core functionality of KerasCV.

KerasHub can be installed with !pip install -U keras-hub. Documentation and guides are available at keras.io/keras_hub.

With our focus shifted to KerasHub, we are not planning any further development or releases in KerasCV. If you encounter a KerasCV feature that is missing from KerasHub, or would like to propose an addition to the library, please file an issue with KerasHub.

[github-actions]

This issue is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.

[github-actions]

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