CV-CUDA

NVIDIA CV-CUDA™ is an open-source project for building cloud-scale Artificial Intelligence (AI) imaging and Computer Vision (CV) applications. It uses graphics processing unit (GPU) acceleration to help developers build highly efficient pre- and post-processing pipelines. It can improve throughput by more than 10x while lowering cloud computing costs.

CV-CUDA includes:

A unified, specialized set of high-performance CV and image processing kernels
C, C++, and Python APIs
Batching support, with variable shape images
Zero-copy interfaces to PyTorch
Sample applications: classification, object detection, and image segmentation

CV-CUDA Pre- and Post-Processing Operators

CV-CUDA offers more than 20 Computer Vision and Image Processing operators. Find the operator that is right for your workflow below.

Where Are the Release Notes?

An awesome product requires excellent support. CV-CUDA release notes can be found here.

Where Can I Get Help?

File requests for enhancements and bug reports here.

What Other Computer Vision Products Does NVIDIA Offer?

NVIDIA offers a number of products for accelerating computer vision and image processing applications. In addition to CV-CUDA, some of the others include:

DALI (Data Loading Library), a portable, holistic framework for accelerated data loading and augmentation in deep learning workflows involving images, videos, and audio data.
VPI (Vision Programming Interface), an accelerated computer vision and image processing software library primarily for embedded/edge applications.
cuCIM (Compute Unified Device Architecture Clara Image), an open source, accelerated computer vision and image processing library for multidimensional images in biomedical, geospatial, material life science, and remote sensing use cases.
NPP (NVIDIA Performance Primitives), an image, signal, and video processing library that accelerates and performs domain-specific functions.

If you want to learn more about what computer vision solutions are available, review the computer vision solutions landing page.

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