Kornia AI

non-profit

kornia

\n\n---\n\nEnglish | [简体中文](README_zh-CN.md)\n\n\nDocs •\nTry it Now •\nTutorials •\nExamples •\nBlog •\nCommunity\n\n

\n\n**Kornia** is a differentiable computer vision library for [PyTorch](https://pytorch.org).\n\nIt 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.\n\nInspired 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.\n\n## Sponsorship\n\nKornia is an open-source project that is developed and maintained by volunteers. Whether you're using it for research or commercial purposes, consider sponsoring or collaborating with us. Your support will help ensure Kornia's growth and ongoing innovation. Reach out to us today and be a part of shaping the future of this exciting initiative!\n\n\n

\n\n\n## Installation\n\n### From pip\n\n ```bash\n pip install kornia\n ```\n\n

Other installation options

\n\n#### From source with editable mode\n\n ```bash\n pip install -e .\n ```\n\n#### From Github url (latest version)\n\n ```bash\n pip install git+https://github.com/kornia/kornia\n ```\n\n

\n\n## Cite\n\nIf you are using kornia in your research-related documents, it is recommended that you cite the paper. See more in [CITATION](https://github.com/kornia/kornia/blob/main/CITATION.md).\n\n ```bibtex\n @inproceedings{eriba2019kornia,\n author = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski},\n title = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},\n booktitle = {Winter Conference on Applications of Computer Vision},\n year = {2020},\n url = {https://arxiv.org/pdf/1910.02190.pdf}\n }\n ```\n\n## Contributing\n\nWe appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the [CONTRIBUTING](https://github.com/kornia/kornia/blob/main/CONTRIBUTING.md) notes. The participation in this open source project is subject to [Code of Conduct](./CODE_OF_CONDUCT.md).\n\n## Community\n\n- **Forums:** discuss implementations, research, etc. [GitHub Forums](https://github.com/kornia/kornia/discussions)\n- **GitHub Issues:** bug reports, feature requests, install issues, RFCs, thoughts, etc. [OPEN](https://github.com/kornia/kornia/issues/new/choose)\n- **Slack:** Join our workspace to keep in touch with our core contributors and be part of our community. [JOIN HERE](https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-2AQRi~X9Uu6PLMuUZdvfjA)\n\n\n

\n\n\nMade with [contrib.rocks](https://contrib.rocks).\n\n## License\n\nKornia is released under the Apache 2.0 license. See the [LICENSE](./LICENSE) file for more information.\n\n","html":"

\n \n

\n\n

English | 简体中文

\n\n

Docs •\nTry it Now •\nTutorials •\nExamples •\nBlog •\nCommunity

\n\n

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.

Sponsorship

Kornia is an open-source project that is developed and maintained by volunteers. Whether you're using it for research or commercial purposes, consider sponsoring or collaborating with us. Your support will help ensure Kornia's growth and ongoing innovation. Reach out to us today and be a part of shaping the future of this exciting initiative!

\n\n

\n\n\n

Installation

From pip

pip install kornia\n

Other installation options

\n\n

From source with editable mode

pip install -e .\n

From Github url (latest version)

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

\n\n

Cite

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

@inproceedings{eriba2019kornia,\n  author    = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski},\n  title     = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},\n  booktitle = {Winter Conference on Applications of Computer Vision},\n  year      = {2020},\n  url       = {https://arxiv.org/pdf/1910.02190.pdf}\n}\n

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the CONTRIBUTING notes. The participation in this open source project is subject to Code of Conduct.

Community

Forums: discuss implementations, research, etc. GitHub Forums
GitHub Issues: bug reports, feature requests, install issues, RFCs, thoughts, etc. OPEN
Slack: Join our workspace to keep in touch with our core contributors and be part of our community. JOIN HERE

\n\n

\n\n\n

Made with contrib.rocks.

License

Kornia is released under the Apache 2.0 license. See the LICENSE file for more information.

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AI & ML interests

computer-vision, image-processing, machine-learning, deep-learning

Recent Activity

gagan3012 authored a paper about 1 month ago

From RAG to Agentic RAG for Faithful Islamic Question Answering

gagan3012 authored a paper about 1 month ago

Prototypicality Bias Reveals Blindspots in Multimodal Evaluation Metrics

ZennyKenny authored a paper 4 months ago

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution

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Kornia is a differentiable computer vision library for PyTorch.

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Installation

From pip

pip install kornia

Other installation options

From source with editable mode

pip install -e .

From Github url (latest version)

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

Cite

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

@inproceedings{eriba2019kornia,
  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}
}

Contributing

Community

Forums: discuss implementations, research, etc. GitHub Forums
GitHub Issues: bug reports, feature requests, install issues, RFCs, thoughts, etc. OPEN
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