v0.5.0-beta

CV-CUDA 0.5.0 is a comprehensive update introducing new security, compliance, and performance enhancements, alongside bug fixes and new features.

Release Highlights

CV-CUDA v0.5.0 includes significant improvements:

  • New Operators: - FindHomography: Calculates a perspective transform from four pairs of the corresponding points - Label: Labels connected regions in an image using 4-way connectivity for foreground and 8-way for background pixels - PairwiseMatcher: Matches features computed separately (e.g. via the SIFT operator) in two images using the brute force method

  • New Features: - Implemented Python class for TensorBatch`, a container type that can hold a list of non-uniformly shaped tensors - Added support for RGBD image formats - Enhanced documentation

  • Bug Fixes: - Resolved memory leak in NvBlurBoxes - Fixed segmentation fault issue in Python with certain imports - Corrected typestr format issue in __cuda_array_interface__ - Addressed occasional hanging in OpBoxBlur on RGBA images

Compatibility

  • Continues to support GPU Compute Capability: 7+.x

  • Compatible with Ubuntu x86_64: 20.04, 22.04

  • CUDA Toolkit: 11.7+ (11.2+ for library build and run)

  • GCC: 11.0+ (9.0 and 10.0 for APIs, with pre-built binary and run)

  • Python: 3.7, 3.8, 3.10

Known Issues/Limitations

  • The release notes do not specify new known issues or limitations for this version.

License

CV-CUDA is licensed under the Apache 2.0 license.

Resources

  1. CV-CUDA GitHub

  2. CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA

  3. NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI

  4. CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI

Acknowledgements

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