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.. _v0.3.0-beta:

v0.3.0-beta
===========

CV-CUDA 0.3.0 is the next open-source release of the project.

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

CV-CUDA 0.3.0 includes the following key features:

* 6 new computer vision operators

  - Adaptive Thresholding, Bounding Box, Bounding Box Blur, Non-Maximum Suppression, Remap, and Thresholding.

* Additional compiler support for GCC 9+ (unit tests build with GCC 11+)
* Compatibility with CUDA Toolkit 11.2+ (unit tests build with Toolkit 11.7+)

* New sample applications

  - Object Detection
    - This pipeline is based on Peoplenet Tao model which detects Persons, Bags and Face
    - Supports TensorRT Inference Backend

  - Segmentation with NVIDIA Triton Backend
    - Uses VPF decoder/encoder on client side and runs segmentation pipeline on Triton server

* Improved testing


Compatibility
-------------
CV-CUDA has been tested on the following compute stack

*  Ubuntu x86_64: 18.04, 20.04, 22.04
*  CUDA Toolkit: 11.7+ (11.2+ for library build and run)
*  GCC: 11.0+ (9.0 for library build and run)
*  Python: 3.7, 3.8, 3.10

Refer to documentation of the sample applications for dependencies.


Known Issues/Limitations
------------------------
*  Open compilation issue with CUDA Toolkit 11.2 + GCC 10.3


License
-------
CV-CUDA operates 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>`_
2.  `CV-CUDA Corporate Blog Announcement <https://blogs.nvidia.com/blog/2022/09/20/computer-vision-cloud/>`_
3.  `CV-CUDA High Performance Image Process Acceleration Library TechBlog <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.
