2 Repos
Awesome GitHub RepositoriesDifferentiable Computer Vision
Integration of geometric vision operations into deep learning pipelines via differentiable tensors for gradient computation.
Distinct from Computer Vision: Focuses on the differentiable nature of the operations for training, not just the general toolset for inference.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Differentiable Computer Vision. Refine with filters or upvote what's useful.
Awesome Differentiable Computer Vision GitHub Repositories
Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
- kornia/kornia
kornia/kornia
11,238Auf GitHub ansehenKornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w
Integrates geometric vision operations into deep learning pipelines using tensors that support gradient computation for model training.
Pythonartificial-intelligencecomputer-visiondeep-learning - cvxpy/cvxpy
cvxpy/cvxpy
6,257Auf GitHub ansehenCVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using natural mathematical syntax. It is built on a disciplined convex programming framework that automatically enforces convexity rules, ensuring that problems formulated by the user are valid for convex solvers. The project also functions as a multi-solver optimization interface, abstracting away backend details and dispatching problems to specialized solvers like ECOS, SCS, and Gurobi without manual configuration. Beyond standard convex optimization, CVXPY extends its reach to geometri
Provides sensitivity analysis by computing derivatives of optimal solutions with respect to problem parameters.
C++
