Kornia is a differentiable computer vision library for PyTorch.

It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.


Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.

At a granular level, Kornia is a library that consists of the following components:

Component Description
kornia a Differentiable Computer Vision library, with strong GPU support
kornia.augmentation a module to perform data augmentation in the GPU
kornia.color a set of routines to perform color space conversions
kornia.contrib a compilation of user contrib and experimental operators
kornia.enhance a module to perform normalization and intensity transformation
kornia.feature a module to perform feature detection
kornia.filters a module to perform image filtering and edge detection
kornia.geometry a geometric computer vision library to perform image transformations, 3D linear algebra and conversions using different camera models
kornia.losses a stack of loss functions to solve different vision tasks
kornia.morphology a module to perform morphological operations
kornia.utils image to tensor utilities and metrics for vision problems


From pip:

pip install kornia
Other installation options

From source:

python setup.py install
pip install -e .

From source using pip:

pip install git+https://github.com/kornia/kornia


Run our Jupyter notebooks tutorials to learn to use the library.


If you are using kornia in your research-related documents, it is recommended that you cite the paper. Se more in CITATION.

  author    = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski},
  title     = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},
  booktitle = {Winter Conference on Applications of Computer Vision},
  year      = {2020},
  url       = {https://arxiv.org/pdf/1910.02190.pdf}