| What is Kornia ? | |
| ================ | |
| Kornia is a differentiable library that allows classical computer vision to be integrated into deep learning models. | |
| 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. | |
| .. image:: https://raw.githubusercontent.com/kornia/kornia/master/docs/source/_static/img/hakuna_matata.gif | |
| :align: center | |
| The 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. | |
| Why Kornia ? | |
| ------------ | |
| With *Kornia* we fill the gap between classical and deep computer vision that implements | |
| standard and advanced vision algorithms for AI: | |
| 1. **Computer Vision:** Kornia fills the gap between Classical and Deep computer Vision. | |
| 2. **Differentiable:** We leverage the Computer Vision 2.0 paradigm. | |
| 3. **Open Source:** Our libraries and initiatives are always according to the community needs. | |
| 4. **PyTorch:** At our core we use PyTorch and its Autograd engine for its efficiency. | |