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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341237" target="_blank" >RIV/68407700:21230/20:00341237 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/WACV45572.2020.9093363" target="_blank" >https://doi.org/10.1109/WACV45572.2020.9093363</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/WACV45572.2020.9093363" target="_blank" >10.1109/WACV45572.2020.9093363</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

  • Original language description

    This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and 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 OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are also provided including a benchmark comparing to existing vision libraries.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

  • ISBN

    978-1-7281-6553-0

  • ISSN

    2472-6737

  • e-ISSN

    2642-9381

  • Number of pages

    10

  • Pages from-to

    3663-3672

  • Publisher name

    IEEE

  • Place of publication

    New Jersey

  • Event location

    Snowmass village

  • Event date

    Mar 1, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000578444803078