* Agg Vgg16 backbone

* update names

* update tests

* update test

* add image classifier

* incorporate review comments

* Update test case

* update backbone test

* add image classifier

* classifier cleanup

* code reformat

* add vgg16 image classifier

* make vgg generic

* update doc string

* update docstring

* add classifier test

* update tests

* update docstring

* address review comments

* code reformat

* update the configs

* address review comments

* fix task saved model test

* update init

* code reformatted

* Add ResNetV1 and ResNetV2

* Address comments

* Add CSP DarkNet

* Add CSP DarkNet

* snake_case function names

* change use_depthwise to block_type

…Backbone` (keras-team#1769)

* Add FeaturePyramidBackbone and update ResNetBackbone

* Simplify the implementation

* Fix CI

* Make ResNetBackbone compatible with timm and add FeaturePyramidBackbone

* Add conversion implementation

* Update docstrings

* Address comments

* Add DenseNet

* fix testcase

* address comments

* nit

* fix lint errors

* move description

* add vit det vit_det_backbone

* update docstring

* code reformat

* fix tests

* address review comments

* bump year on all files

* address review comments

* rename backbone

* fix tests

* change back to ViT

* address review comments

* update image shape

* Add MixTransformer

* fix testcase

* test changes and comments

* lint fix

* update config list

* modify testcase for 2 layers

* update input_image_shape -> image_shape

* update docstring example

* code reformat

* update tests

add missing __init__ file to vit_det

This is a temporary way to test out the keras-hub branch.
- Does a global rename of all symbols during package build.
- Registers the "old" name on symbol export for saving compat.
- Adds a github action to publish every commit to keras-hub as
  a new package.
- Removes our descriptions on PyPI temporarily, until we want
  to message this more broadly.

* Add `CLIPTokenizer`, `T5XXLTokenizer`, `CLIPTextEncoder` and `T5XXLTextEncoder`.

* Make CLIPTextEncoder as Backbone

* Add `T5XXLPreprocessor` and remove `T5XXLTokenizer`

Add `CLIPPreprocessor`

* Use `tf = None` at the top

* Replace manual implementation of `CLIPAttention` with `MultiHeadAttention`

* Bounding box utils

* - Correct test cases

* - Remove hard tensorflow dtype

* - fix api gen

* - Fix import for test cases
- Use setup for converters test case

* - fix api_gen issue

* - FIx api gen

* - Fix api gen error

* - Correct test cases as per new api changes

* mobilenet_v3 added in keras-nlp

* minor bug fixed in mobilenet_v3_backbone

* formatting corrected

* refactoring backbone

* correct_pad_downsample method added

* refactoring backbone

* parameters updated

* Testcaseupdated, expected output shape corrected

* code formatted with black

* testcase updated

* refactoring and description added

* comments updated

* added mobilenet v1 and v2

* merge conflict resolved

* version arg removed, and config options added

* input_shape changed to image_shape in arg

* config updated

* input shape corrected

* comments resolved

* activation function format changed

* minor bug fixed

* minor bug fixed

* added vision_backbone_test

* channel_first bug resolved

* channel_first cases working

* comments  resolved

* formatting fixed

* refactoring

---------

Co-authored-by: ushareng <[email protected]>

* migrating efficientnet models to keras-hub

* merging changes from other sources

* autoformatting pass

* initial consolidation of efficientnet_backbone

* most updates and removing separate implementation

* cleanup, autoformatting, keras generalization

* removed layer examples outside of effiicient net

* many, mainly documentation changes, small test fixes

* Add ResNet_vd to ResNet backbone

* Addressed requested parameter changes

* Fixed tests and updated comments

* Added new parameters to docstring

* Add `VAEImageDecoder` for StableDiffusionV3

* Use `keras.Model` for `VAEImageDecoder` and follows the coding style in `VAEAttention`

…TextEncoder` (keras-team#1802)