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.. _v0.12.0-beta:

v0.12.0-beta
============

Release Highlights
------------------

CV-CUDA v0.12.0 includes critical bug fixes alongside the following changes:​

* **New Features**:​

  * Increased functional test coverage of color conversions. ​
  * Reintroduced from 24.07: Improved performance of color conversion operators (e.g., 2x faster RGB2YUV).

* **Bug Fixes**:​

  * Fixed bug in YUV(420) conversions: The CvtColor operator incorrectly computed the data location of the second chromaticity channel for conversions.​
  * Fixed bug in YUV(422) conversions: The CvtColor operator incorrectly interpreted the interleaved YUV(422) data layout as a three-channel tensor.​
  * Prevent CV_16F alpha addition: some color conversions in the CvtColor operator allowed for the addition of an alpha channel to the destination tensor, which is undefined for the CV_16F data type.


Compatibility and Known Limitations
-----------------------------------

For the full list, see main README on `CV-CUDA GitHub <https://github.com/CVCUDA/CV-CUDA>`_.

License
-------

CV-CUDA is licensed under the `Apache 2.0 <https://github.com/CVCUDA/CV-CUDA/blob/main/LICENSE.md>`_ license.

Resources
---------

1. `CV-CUDA GitHub <https://github.com/CVCUDA/CV-CUDA>`_
2. `CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA <https://developer.nvidia.com/blog/increasing-throughput-and-reducing-costs-for-computer-vision-with-cv-cuda/>`_
3. `NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI <https://blogs.nvidia.com/blog/2023/03/21/cv-cuda-ai-computer-vision/>`_
4. `CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI <https://developer.nvidia.com/zh-cn/blog/cv-cuda-high-performance-image-processing/>`_

Acknowledgements
----------------

CV-CUDA is developed jointly by NVIDIA and the ByteDance Machine Learning team.