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Releases: keras-team/keras-cv

0.7.2.dev1

07 Dec 00:10
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0.7.2.dev0

06 Dec 21:08
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Full Changelog: v0.7.1...0.7.2.dev0

0.7.1

22 Nov 22:18
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Summary

KerasCV is now compatible with Keras 3 (TensorFlow, Jax and PyTorch backends). Users utilizing tf-nightly or Keras 3.0 can expect seamless compatibility with KerasCV. KerasCV also works with Keras 2.0 (with TensorFlow).

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Full Changelog: v0.6.4...v0.7.1

0.7.1.dev0

22 Nov 16:36
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Full Changelog: v0.6.4...0.7.1.dev0

v0.7.0.dev0

20 Nov 22:43
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v0.7.0.dev0 Pre-release
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Summary

  • KerasCV is now compatible with Keras 3. Users utilizing tf-nightly or Keras 3.0 can expect seamless compatibility with KerasCV.
  • bug fixes

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Full Changelog: v0.6.4...v0.7.0.dev0

v0.6.4

12 Sep 03:03
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Fix an import issue from 0.6.2 and 0.6.3

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v0.6.3

08 Sep 22:44
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This is a minor bug fix to resolve some import issues with 0.6.2

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Full Changelog: v0.6.2...v0.6.3

v0.6.2

08 Sep 00:15
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With this release, we're bringing Stable Diffusion to multi-backend Keras, as well as a few other improvements

Introduction of SegFormer for semantic segmentation
Improvements to DeepLabV3Plus
Expansion of segmentation mask support in preprocessing layers
Migrating CenterPillar (3D OD) to multi-backend Keras
Thank you to all of our wonderful contributors for your efforts on this release!

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Full Changelog: v0.6.1...v0.6.2

v0.6.1

11 Jul 23:42
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This is a fast-follow to 0.6.0 with two notable fixes:

  • Re-added support for TF 2.12
  • Fixed Non-Max-Suppression for Keras Core + TensorFlow backend

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Full Changelog: v0.6.0...v0.6.1

v0.6.0

11 Jul 13:08
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KerasCV is adding experimental support for Jax and PyTorch backends on top of the Keras Core library. Read the anouncement, and browse the full library documentation, including how to specify the backend when running your code.

Support for both Jax and PyTorch is still experimental, expect some rough edges and please give us feedback!

Summary

This release should be equivalent to 0.5.1 with the addition of multi-backend support.
The following API components do not yet support Jax + PyTorch:

  • keras_cv.models.StableDiffusion[V2]
  • keras_cv.models.MultiHeadCenterPillar
  • keras_cv.training.ContrastiveTrainer
  • All models under keras_cv.models.legacy

Note that there are two ways you can run on top of Tensorflow.
If you run your scripts/colab without any changes, KerasCV will use tf.keras for all layer and modeling implementations. This should be a no-op from previous releases of the library.

If you run your scripts/colab with KERAS_BACKEND={jax, torch, tensorflow}, you will be trying the new Keras Core library, using the specified backend. This is a great way to test out the future of the library!

Full details on runtime specification is available along with the Keras Core documentation.

What's Changed

New Contributors

Full Changelog: v0.5.1...v0.6.0.dev0